Investigating the Accuracy of Point Clouds Generated for Rock Surfaces

NASA Astrophysics Data System (ADS)

Seker, D. Z.; Incekara, A. H.

2016-12-01

Point clouds which are produced by means of different techniques are widely used to model the rocks and obtain the properties of rock surfaces like roughness, volume and area. These point clouds can be generated by applying laser scanning and close range photogrammetry techniques. Laser scanning is the most common method to produce point cloud. In this method, laser scanner device produces 3D point cloud at regular intervals. In close range photogrammetry, point cloud can be produced with the help of photographs taken in appropriate conditions depending on developing hardware and software technology. Many photogrammetric software which is open source or not currently provide the generation of point cloud support. Both methods are close to each other in terms of accuracy. Sufficient accuracy in the mm and cm range can be obtained with the help of a qualified digital camera and laser scanner. In both methods, field work is completed in less time than conventional techniques. In close range photogrammetry, any part of rock surfaces can be completely represented owing to overlapping oblique photographs. In contrast to the proximity of the data, these two methods are quite different in terms of cost. In this study, whether or not point cloud produced by photographs can be used instead of point cloud produced by laser scanner device is investigated. In accordance with this purpose, rock surfaces which have complex and irregular shape located in İstanbul Technical University Ayazaga Campus were selected as study object. Selected object is mixture of different rock types and consists of both partly weathered and fresh parts. Study was performed on a part of 30m x 10m rock surface. 2D and 3D analysis were performed for several regions selected from the point clouds of the surface models. 2D analysis is area-based and 3D analysis is volume-based. Analysis conclusions showed that point clouds in both are similar and can be used as alternative to each other. This proved that point cloud produced using photographs which are both economical and enables to produce data in less time can be used in several studies instead of point cloud produced by laser scanner.

Design of relative motion and attitude profiles for three-dimensional resident space object imaging with a laser rangefinder

NASA Astrophysics Data System (ADS)

Nayak, M.; Beck, J.; Udrea, B.

This paper focuses on the aerospace application of a single beam laser rangefinder (LRF) for 3D imaging, shape detection, and reconstruction in the context of a space-based space situational awareness (SSA) mission scenario. The primary limitation to 3D imaging from LRF point clouds is the one-dimensional nature of the single beam measurements. A method that combines relative orbital motion and scanning attitude motion to generate point clouds has been developed and the design and characterization of multiple relative motion and attitude maneuver profiles are presented. The target resident space object (RSO) has the shape of a generic telecommunications satellite. The shape and attitude of the RSO are unknown to the chaser satellite however, it is assumed that the RSO is un-cooperative and has fixed inertial pointing. All sensors in the metrology chain are assumed ideal. A previous study by the authors used pure Keplerian motion to perform a similar 3D imaging mission at an asteroid. A new baseline for proximity operations maneuvers for LRF scanning, based on a waypoint adaptation of the Hill-Clohessy-Wiltshire (HCW) equations is examined. Propellant expenditure for each waypoint profile is discussed and combinations of relative motion and attitude maneuvers that minimize the propellant used to achieve a minimum required point cloud density are studied. Both LRF strike-point coverage and point cloud density are maximized; the capability for 3D shape registration and reconstruction from point clouds generated with a single beam LRF without catalog comparison is proven. Next, a method of using edge detection algorithms to process a point cloud into a 3D modeled image containing reconstructed shapes is presented. Weighted accuracy of edge reconstruction with respect to the true model is used to calculate a qualitative “ metric” that evaluates effectiveness of coverage. Both edge recognition algorithms and the metric are independent of point cloud densit- , therefore they are utilized to compare the quality of point clouds generated by various attitude and waypoint command profiles. The RSO model incorporates diverse irregular protruding shapes, such as open sensor covers, instrument pods and solar arrays, to test the limits of the algorithms. This analysis is used to mathematically prove that point clouds generated by a single-beam LRF can achieve sufficient edge recognition accuracy for SSA applications, with meaningful shape information extractable even from sparse point clouds. For all command profiles, reconstruction of RSO shapes from the point clouds generated with the proposed method are compared to the truth model and conclusions are drawn regarding their fidelity.

Quality Assessment and Comparison of Smartphone and Leica C10 Laser Scanner Based Point Clouds

NASA Astrophysics Data System (ADS)

Sirmacek, Beril; Lindenbergh, Roderik; Wang, Jinhu

2016-06-01

3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (which can generate point clouds based on photogrammetry) can provide denser sampling of 3D surface geometry. Unfortunately, terrestrial laser scanning sensors are expensive and trained persons are needed to use them for point cloud acquisition. A potential effective 3D modelling can be generated based on a low cost smartphone sensor. Herein, we show examples of using smartphone camera images to generate 3D models of urban structures. We compare a smartphone based 3D model of an example structure with a terrestrial laser scanning point cloud of the structure. This comparison gives us opportunity to discuss the differences in terms of geometrical correctness, as well as the advantages, disadvantages and limitations in data acquisition and processing. We also discuss how smartphone based point clouds can help to solve further problems with 3D urban model generation in a practical way. We show that terrestrial laser scanning point clouds which do not have color information can be colored using smartphones. The experiments, discussions and scientific findings might be insightful for the future studies in fast, easy and low-cost 3D urban model generation field.

Scan-To Output Validation: Towards a Standardized Geometric Quality Assessment of Building Information Models Based on Point Clouds

NASA Astrophysics Data System (ADS)

Bonduel, M.; Bassier, M.; Vergauwen, M.; Pauwels, P.; Klein, R.

2017-11-01

The use of Building Information Modeling (BIM) for existing buildings based on point clouds is increasing. Standardized geometric quality assessment of the BIMs is needed to make them more reliable and thus reusable for future users. First, available literature on the subject is studied. Next, an initial proposal for a standardized geometric quality assessment is presented. Finally, this method is tested and evaluated with a case study. The number of specifications on BIM relating to existing buildings is limited. The Levels of Accuracy (LOA) specification of the USIBD provides definitions and suggestions regarding geometric model accuracy, but lacks a standardized assessment method. A deviation analysis is found to be dependent on (1) the used mathematical model, (2) the density of the point clouds and (3) the order of comparison. Results of the analysis can be graphical and numerical. An analysis on macro (building) and micro (BIM object) scale is necessary. On macro scale, the complete model is compared to the original point cloud and vice versa to get an overview of the general model quality. The graphical results show occluded zones and non-modeled objects respectively. Colored point clouds are derived from this analysis and integrated in the BIM. On micro scale, the relevant surface parts are extracted per BIM object and compared to the complete point cloud. Occluded zones are extracted based on a maximum deviation. What remains is classified according to the LOA specification. The numerical results are integrated in the BIM with the use of object parameters.

Fast calculation method of computer-generated hologram using a depth camera with point cloud gridding

NASA Astrophysics Data System (ADS)

Zhao, Yu; Shi, Chen-Xiao; Kwon, Ki-Chul; Piao, Yan-Ling; Piao, Mei-Lan; Kim, Nam

2018-03-01

We propose a fast calculation method for a computer-generated hologram (CGH) of real objects that uses a point cloud gridding method. The depth information of the scene is acquired using a depth camera and the point cloud model is reconstructed virtually. Because each point of the point cloud is distributed precisely to the exact coordinates of each layer, each point of the point cloud can be classified into grids according to its depth. A diffraction calculation is performed on the grids using a fast Fourier transform (FFT) to obtain a CGH. The computational complexity is reduced dramatically in comparison with conventional methods. The feasibility of the proposed method was confirmed by numerical and optical experiments.

D Modeling of Components of a Garden by Using Point Cloud Data

NASA Astrophysics Data System (ADS)

Kumazakia, R.; Kunii, Y.

2016-06-01

Laser measurement is currently applied to several tasks such as plumbing management, road investigation through mobile mapping systems, and elevation model utilization through airborne LiDAR. Effective laser measurement methods have been well-documented in civil engineering, but few attempts have been made to establish equally effective methods in landscape engineering. By using point cloud data acquired through laser measurement, the aesthetic landscaping of Japanese gardens can be enhanced. This study focuses on simple landscape simulations for pruning and rearranging trees as well as rearranging rocks, lanterns, and other garden features by using point cloud data. However, such simulations lack concreteness. Therefore, this study considers the construction of a library of garden features extracted from point cloud data. The library would serve as a resource for creating new gardens and simulating gardens prior to conducting repairs. Extracted garden features are imported as 3ds Max objects, and realistic 3D models are generated by using a material editor system. As further work toward the publication of a 3D model library, file formats for tree crowns and trunks should be adjusted. Moreover, reducing the size of created models is necessary. Models created using point cloud data are informative because simply shaped garden features such as trees are often seen in the 3D industry.

The Segmentation of Point Clouds with K-Means and ANN (artifical Neural Network)

NASA Astrophysics Data System (ADS)

Kuçak, R. A.; Özdemir, E.; Erol, S.

2017-05-01

Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

Assessment of different models for computing the probability of a clear line of sight

NASA Astrophysics Data System (ADS)

Bojin, Sorin; Paulescu, Marius; Badescu, Viorel

2017-12-01

This paper is focused on modeling the morphological properties of the cloud fields in terms of the probability of a clear line of sight (PCLOS). PCLOS is defined as the probability that a line of sight between observer and a given point of the celestial vault goes freely without intersecting a cloud. A variety of PCLOS models assuming the cloud shape hemisphere, semi-ellipsoid and ellipsoid are tested. The effective parameters (cloud aspect ratio and absolute cloud fraction) are extracted from high-resolution series of sunshine number measurements. The performance of the PCLOS models is evaluated from the perspective of their ability in retrieving the point cloudiness. The advantages and disadvantages of the tested models are discussed, aiming to a simplified parameterization of PCLOS models.

Effect of electromagnetic field on Kordylewski clouds formation

NASA Astrophysics Data System (ADS)

Salnikova, Tatiana; Stepanov, Sergey

2018-05-01

In previous papers the authors suggest a clarification of the phenomenon of appearance-disappearance of Kordylewski clouds - accumulation of cosmic dust mass in the vicinity of the triangle libration points of the Earth-Moon system. Under gravi-tational and light perturbation of the Sun the triangle libration points aren't the points of relative equilibrium. However, there exist the stable periodic motion of the particles, surrounding every of the triangle libration points. Due to this fact we can consider a probabilistic model of the dust clouds formation. These clouds move along the periodical orbits in small vicinity of the point of periodical orbit. To continue this research we suggest a mathematical model to investigate also the electromagnetic influences, arising under consideration of the charged dust particles in the vicinity of the triangle libration points of the Earth-Moon system. In this model we take under consideration the self-unduced force field within the set of charged particles, the probability distribution density evolves according to the Vlasov equation.

An Efficient Method to Create Digital Terrain Models from Point Clouds Collected by Mobile LiDAR Systems

NASA Astrophysics Data System (ADS)

Gézero, L.; Antunes, C.

2017-05-01

The digital terrain models (DTM) assume an essential role in all types of road maintenance, water supply and sanitation projects. The demand of such information is more significant in developing countries, where the lack of infrastructures is higher. In recent years, the use of Mobile LiDAR Systems (MLS) proved to be a very efficient technique in the acquisition of precise and dense point clouds. These point clouds can be a solution to obtain the data for the production of DTM in remote areas, due mainly to the safety, precision, speed of acquisition and the detail of the information gathered. However, the point clouds filtering and algorithms to separate "terrain points" from "no terrain points", quickly and consistently, remain a challenge that has caught the interest of researchers. This work presents a method to create the DTM from point clouds collected by MLS. The method is based in two interactive steps. The first step of the process allows reducing the cloud point to a set of points that represent the terrain's shape, being the distance between points inversely proportional to the terrain variation. The second step is based on the Delaunay triangulation of the points resulting from the first step. The achieved results encourage a wider use of this technology as a solution for large scale DTM production in remote areas.

Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer

NASA Astrophysics Data System (ADS)

Ma, Hongchao; Cai, Zhan; Zhang, Liang

2018-01-01

This paper discusses airborne light detection and ranging (LiDAR) point cloud filtering (a binary classification problem) from the machine learning point of view. We compared three supervised classifiers for point cloud filtering, namely, Adaptive Boosting, support vector machine, and random forest (RF). Nineteen features were generated from raw LiDAR point cloud based on height and other geometric information within a given neighborhood. The test datasets issued by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to evaluate the performance of the three filtering algorithms; RF showed the best results with an average total error of 5.50%. The paper also makes tentative exploration in the application of transfer learning theory to point cloud filtering, which has not been introduced into the LiDAR field to the authors' knowledge. We performed filtering of three datasets from real projects carried out in China with RF models constructed by learning from the 15 ISPRS datasets and then transferred with little to no change of the parameters. Reliable results were achieved, especially in rural area (overall accuracy achieved 95.64%), indicating the feasibility of model transfer in the context of point cloud filtering for both easy automation and acceptable accuracy.

Combining 3d Volume and Mesh Models for Representing Complicated Heritage Buildings

NASA Astrophysics Data System (ADS)

Tsai, F.; Chang, H.; Lin, Y.-W.

2017-08-01

This study developed a simple but effective strategy to combine 3D volume and mesh models for representing complicated heritage buildings and structures. The idea is to seamlessly integrate 3D parametric or polyhedral models and mesh-based digital surfaces to generate a hybrid 3D model that can take advantages of both modeling methods. The proposed hybrid model generation framework is separated into three phases. Firstly, after acquiring or generating 3D point clouds of the target, these 3D points are partitioned into different groups. Secondly, a parametric or polyhedral model of each group is generated based on plane and surface fitting algorithms to represent the basic structure of that region. A "bare-bones" model of the target can subsequently be constructed by connecting all 3D volume element models. In the third phase, the constructed bare-bones model is used as a mask to remove points enclosed by the bare-bones model from the original point clouds. The remaining points are then connected to form 3D surface mesh patches. The boundary points of each surface patch are identified and these boundary points are projected onto the surfaces of the bare-bones model. Finally, new meshes are created to connect the projected points and original mesh boundaries to integrate the mesh surfaces with the 3D volume model. The proposed method was applied to an open-source point cloud data set and point clouds of a local historical structure. Preliminary results indicated that the reconstructed hybrid models using the proposed method can retain both fundamental 3D volume characteristics and accurate geometric appearance with fine details. The reconstructed hybrid models can also be used to represent targets in different levels of detail according to user and system requirements in different applications.

Superposition and alignment of labeled point clouds.

PubMed

Fober, Thomas; Glinca, Serghei; Klebe, Gerhard; Hüllermeier, Eyke

2011-01-01

Geometric objects are often represented approximately in terms of a finite set of points in three-dimensional euclidean space. In this paper, we extend this representation to what we call labeled point clouds. A labeled point cloud is a finite set of points, where each point is not only associated with a position in three-dimensional space, but also with a discrete class label that represents a specific property. This type of model is especially suitable for modeling biomolecules such as proteins and protein binding sites, where a label may represent an atom type or a physico-chemical property. Proceeding from this representation, we address the question of how to compare two labeled points clouds in terms of their similarity. Using fuzzy modeling techniques, we develop a suitable similarity measure as well as an efficient evolutionary algorithm to compute it. Moreover, we consider the problem of establishing an alignment of the structures in the sense of a one-to-one correspondence between their basic constituents. From a biological point of view, alignments of this kind are of great interest, since mutually corresponding molecular constituents offer important information about evolution and heredity, and can also serve as a means to explain a degree of similarity. In this paper, we therefore develop a method for computing pairwise or multiple alignments of labeled point clouds. To this end, we proceed from an optimal superposition of the corresponding point clouds and construct an alignment which is as much as possible in agreement with the neighborhood structure established by this superposition. We apply our methods to the structural analysis of protein binding sites.

Indoor Modelling from Slam-Based Laser Scanner: Door Detection to Envelope Reconstruction

NASA Astrophysics Data System (ADS)

Díaz-Vilariño, L.; Verbree, E.; Zlatanova, S.; Diakité, A.

2017-09-01

Updated and detailed indoor models are being increasingly demanded for various applications such as emergency management or navigational assistance. The consolidation of new portable and mobile acquisition systems has led to a higher availability of 3D point cloud data from indoors. In this work, we explore the combined use of point clouds and trajectories from SLAM-based laser scanner to automate the reconstruction of building indoors. The methodology starts by door detection, since doors represent transitions from one indoor space to other, which constitutes an initial approach about the global configuration of the point cloud into building rooms. For this purpose, the trajectory is used to create a vertical point cloud profile in which doors are detected as local minimum of vertical distances. As point cloud and trajectory are related by time stamp, this feature is used to subdivide the point cloud into subspaces according to the location of the doors. The correspondence between subspaces and building rooms is not unambiguous. One subspace always corresponds to one room, but one room is not necessarily depicted by just one subspace, for example, in case of a room containing several doors and in which the acquisition is performed in a discontinue way. The labelling problem is formulated as combinatorial approach solved as a minimum energy optimization. Once the point cloud is subdivided into building rooms, envelop (conformed by walls, ceilings and floors) is reconstructed for each space. The connectivity between spaces is included by adding the previously detected doors to the reconstructed model. The methodology is tested in a real case study.

Large Scale Ice Water Path and 3-D Ice Water Content

DOE Data Explorer

Liu, Guosheng

2008-01-15

Cloud ice water concentration is one of the most important, yet poorly observed, cloud properties. Developing physical parameterizations used in general circulation models through single-column modeling is one of the key foci of the ARM program. In addition to the vertical profiles of temperature, water vapor and condensed water at the model grids, large-scale horizontal advective tendencies of these variables are also required as forcing terms in the single-column models. Observed horizontal advection of condensed water has not been available because the radar/lidar/radiometer observations at the ARM site are single-point measurement, therefore, do not provide horizontal distribution of condensed water. The intention of this product is to provide large-scale distribution of cloud ice water by merging available surface and satellite measurements. The satellite cloud ice water algorithm uses ARM ground-based measurements as baseline, produces datasets for 3-D cloud ice water distributions in a 10 deg x 10 deg area near ARM site. The approach of the study is to expand a (surface) point measurement to an (satellite) areal measurement. That is, this study takes the advantage of the high quality cloud measurements at the point of ARM site. We use the cloud characteristics derived from the point measurement to guide/constrain satellite retrieval, then use the satellite algorithm to derive the cloud ice water distributions within an area, i.e., 10 deg x 10 deg centered at ARM site.

An Approach of Web-based Point Cloud Visualization without Plug-in

NASA Astrophysics Data System (ADS)

Ye, Mengxuan; Wei, Shuangfeng; Zhang, Dongmei

2016-11-01

With the advances in three-dimensional laser scanning technology, the demand for visualization of massive point cloud is increasingly urgent, but a few years ago point cloud visualization was limited to desktop-based solutions until the introduction of WebGL, several web renderers are available. This paper addressed the current issues in web-based point cloud visualization, and proposed a method of web-based point cloud visualization without plug-in. The method combines ASP.NET and WebGL technologies, using the spatial database PostgreSQL to store data and the open web technologies HTML5 and CSS3 to implement the user interface, a visualization system online for 3D point cloud is developed by Javascript with the web interactions. Finally, the method is applied to the real case. Experiment proves that the new model is of great practical value which avoids the shortcoming of the existing WebGIS solutions.

Study of Huizhou architecture component point cloud in surface reconstruction

NASA Astrophysics Data System (ADS)

Zhang, Runmei; Wang, Guangyin; Ma, Jixiang; Wu, Yulu; Zhang, Guangbin

2017-06-01

Surface reconfiguration softwares have many problems such as complicated operation on point cloud data, too many interaction definitions, and too stringent requirements for inputing data. Thus, it has not been widely popularized so far. This paper selects the unique Huizhou Architecture chuandou wooden beam framework as the research object, and presents a complete set of implementation in data acquisition from point, point cloud preprocessing and finally implemented surface reconstruction. Firstly, preprocessing the acquired point cloud data, including segmentation and filtering. Secondly, the surface’s normals are deduced directly from the point cloud dataset. Finally, the surface reconstruction is studied by using Greedy Projection Triangulation Algorithm. Comparing the reconstructed model with the three-dimensional surface reconstruction softwares, the results show that the proposed scheme is more smooth, time efficient and portable.

Applications of low altitude photogrammetry for morphometry, displacements, and landform modeling

NASA Astrophysics Data System (ADS)

Gomez, F. G.; Polun, S. G.; Hickcox, K.; Miles, C.; Delisle, C.; Beem, J. R.

2016-12-01

Low-altitude aerial surveying is emerging as a tool that greatly improves the ease and efficiency of measuring landforms for quantitative geomorphic analyses. High-resolution, close-range photogrammetry produces dense, 3-dimensional point clouds that facilitate the construction of digital surface models, as well as a potential means of classifying ground targets using spatial structure. This study presents results from recent applications of UAS-based photogrammetry, including high resolution surface morphometry of a lava flow, repeat-pass applications to mass movements, and fault scarp degradation modeling. Depending upon the desired photographic resolution and the platform/payload flown, aerial photos are typically acquired at altitudes of 40 - 100 meters above the ground surface. In all cases, high-precision ground control points are key for accurate (and repeatable) orientation - relying on low-precision GPS coordinates (whether on the ground or geotags in the aerial photos) typically results in substantial rotations (tilt) of the reference frame. Using common ground control points between repeat surveys results in matching point clouds with RMS residuals better than 10 cm. In arid regions, the point cloud is used to assess lava flow surface roughness using multi-scale measurements of point cloud dimensionality. For the landslide study, the point cloud provides a basis for assessing possible displacements. In addition, the high resolution orthophotos facilitate mapping of fractures and their growth. For neotectonic applications, we compare fault scarp modeling results from UAV-derived point clouds versus field-based surveys (kinematic GPS and electronic distance measurements). In summary, there is a wide ranging toolbox of low-altitude aerial platforms becoming available for field geoscientists. In many instances, these tools will present convenience and reduced cost compared with the effort and expense to contract acquisitions of aerial imagery.

Reconstruction of Consistent 3d CAD Models from Point Cloud Data Using a Priori CAD Models

NASA Astrophysics Data System (ADS)

Bey, A.; Chaine, R.; Marc, R.; Thibault, G.; Akkouche, S.

2011-09-01

We address the reconstruction of 3D CAD models from point cloud data acquired in industrial environments, using a pre-existing 3D model as an initial estimate of the scene to be processed. Indeed, this prior knowledge can be used to drive the reconstruction so as to generate an accurate 3D model matching the point cloud. We more particularly focus our work on the cylindrical parts of the 3D models. We propose to state the problem in a probabilistic framework: we have to search for the 3D model which maximizes some probability taking several constraints into account, such as the relevancy with respect to the point cloud and the a priori 3D model, and the consistency of the reconstructed model. The resulting optimization problem can then be handled using a stochastic exploration of the solution space, based on the random insertion of elements in the configuration under construction, coupled with a greedy management of the conflicts which efficiently improves the configuration at each step. We show that this approach provides reliable reconstructed 3D models by presenting some results on industrial data sets.

Multiview point clouds denoising based on interference elimination

NASA Astrophysics Data System (ADS)

Hu, Yang; Wu, Qian; Wang, Le; Jiang, Huanyu

2018-03-01

Newly emerging low-cost depth sensors offer huge potentials for three-dimensional (3-D) modeling, but existing high noise restricts these sensors from obtaining accurate results. Thus, we proposed a method for denoising registered multiview point clouds with high noise to solve that problem. The proposed method is aimed at fully using redundant information to eliminate the interferences among point clouds of different views based on an iterative procedure. In each iteration, noisy points are either deleted or moved to their weighted average targets in accordance with two cases. Simulated data and practical data captured by a Kinect v2 sensor were tested in experiments qualitatively and quantitatively. Results showed that the proposed method can effectively reduce noise and recover local features from highly noisy multiview point clouds with good robustness, compared to truncated signed distance function and moving least squares (MLS). Moreover, the resulting low-noise point clouds can be further smoothed by the MLS to achieve improved results. This study provides the feasibility of obtaining fine 3-D models with high-noise devices, especially for depth sensors, such as Kinect.

Accuracy Assessment of a Canal-Tunnel 3d Model by Comparing Photogrammetry and Laserscanning Recording Techniques

NASA Astrophysics Data System (ADS)

Charbonnier, P.; Chavant, P.; Foucher, P.; Muzet, V.; Prybyla, D.; Perrin, T.; Grussenmeyer, P.; Guillemin, S.

2013-07-01

With recent developments in the field of technology and computer science, conventional methods are being supplanted by laser scanning and digital photogrammetry. These two different surveying techniques generate 3-D models of real world objects or structures. In this paper, we consider the application of terrestrial Laser scanning (TLS) and photogrammetry to the surveying of canal tunnels. The inspection of such structures requires time, safe access, specific processing and professional operators. Therefore, a French partnership proposes to develop a dedicated equipment based on image processing for visual inspection of canal tunnels. A 3D model of the vault and side walls of the tunnel is constructed from images recorded onboard a boat moving inside the tunnel. To assess the accuracy of this photogrammetric model (PM), a reference model is build using static TLS. We here address the problem comparing the resulting point clouds. Difficulties arise because of the highly differentiated acquisition processes, which result in very different point densities. We propose a new tool, designed to compare differences between pairs of point cloud or surfaces (triangulated meshes). Moreover, dealing with huge datasets requires the implementation of appropriate structures and algorithms. Several techniques are presented : point-to-point, cloud-to-cloud and cloud-to-mesh. In addition farthest point resampling, octree structure and Hausdorff distance are adopted and described. Experimental results are shown for a 475 m long canal tunnel located in France.

Point clouds segmentation as base for as-built BIM creation

NASA Astrophysics Data System (ADS)

Macher, H.; Landes, T.; Grussenmeyer, P.

2015-08-01

In this paper, a three steps segmentation approach is proposed in order to create 3D models from point clouds acquired by TLS inside buildings. The three scales of segmentation are floors, rooms and planes composing the rooms. First, floor segmentation is performed based on analysis of point distribution along Z axis. Then, for each floor, room segmentation is achieved considering a slice of point cloud at ceiling level. Finally, planes are segmented for each room, and planes corresponding to ceilings and floors are identified. Results of each step are analysed and potential improvements are proposed. Based on segmented point clouds, the creation of as-built BIM is considered in a future work section. Not only the classification of planes into several categories is proposed, but the potential use of point clouds acquired outside buildings is also considered.

Diffuse cloud chemistry. [in interstellar matter

NASA Technical Reports Server (NTRS)

Van Dishoeck, Ewine F.; Black, John H.

1988-01-01

The current status of models of diffuse interstellar clouds is reviewed. A detailed comparison of recent gas-phase steady-state models shows that both the physical conditions and the molecular abundances in diffuse clouds are still not fully understood. Alternative mechanisms are discussed and observational tests which may discriminate between the various models are suggested. Recent developments regarding the velocity structure of diffuse clouds are mentioned. Similarities and differences between the chemistries in diffuse clouds and those in translucent and high latitude clouds are pointed out.

Comparison of 3D point clouds produced by LIDAR and UAV photoscan in the Rochefort cave (Belgium)

NASA Astrophysics Data System (ADS)

Watlet, Arnaud; Triantafyllou, Antoine; Kaufmann, Olivier; Le Mouelic, Stéphane

2016-04-01

Amongst today's techniques that are able to produce 3D point clouds, LIDAR and UAV (Unmanned Aerial Vehicle) photogrammetry are probably the most commonly used. Both methods have their own advantages and limitations. LIDAR scans create high resolution and high precision 3D point clouds, but such methods are generally costly, especially for sporadic surveys. Compared to LIDAR, UAV (e.g. drones) are cheap and flexible to use in different kind of environments. Moreover, the photogrammetric processing workflow of digital images taken with UAV becomes easier with the rise of many affordable software packages (e.g. Agisoft, PhotoModeler3D, VisualSFM). We present here a challenging study made at the Rochefort Cave Laboratory (South Belgium) comprising surface and underground surveys. The site is located in the Belgian Variscan fold-and-thrust belt, a region that shows many karstic networks within Devonian limestone units. A LIDAR scan has been acquired in the main chamber of the cave (~ 15000 m³) to spatialize 3D point cloud of its inner walls and infer geological beds and structures. Even if the use of LIDAR instrument was not really comfortable in such caving environment, the collected data showed a remarkable precision according to few control points geometry. We also decided to perform another challenging survey of the same cave chamber by modelling a 3D point cloud using photogrammetry of a set of DSLR camera pictures taken from the ground and UAV pictures. The aim was to compare both techniques in terms of (i) implementation of data acquisition and processing, (ii) quality of resulting 3D points clouds (points density, field vs cloud recovery and points precision), (iii) their application for geological purposes. Through Rochefort case study, main conclusions are that LIDAR technique provides higher density point clouds with slightly higher precision than photogrammetry method. However, 3D data modeled by photogrammetry provide visible light spectral information for each modeled voxel and interpolated vertices that can be a useful attributes for clustering during data treatment. We thus illustrate such applications to the Rochefort cave by using both sources of 3D information to quantify the orientation of inaccessible geological structures (e.g. faults, tectonic and gravitational joints, and sediments bedding), cluster these structures using color information gathered from UAV's 3D point cloud and compare these data to structural data surveyed on the field. An additional drone photoscan was also conducted in the surface sinkhole giving access to the surveyed underground cavity to seek geological bodies' connections.

D Building Reconstruction by Multiview Images and the Integrated Application with Augmented Reality

NASA Astrophysics Data System (ADS)

Hwang, Jin-Tsong; Chu, Ting-Chen

2016-10-01

This study presents an approach wherein photographs with a high degree of overlap are clicked using a digital camera and used to generate three-dimensional (3D) point clouds via feature point extraction and matching. To reconstruct a building model, an unmanned aerial vehicle (UAV) is used to click photographs from vertical shooting angles above the building. Multiview images are taken from the ground to eliminate the shielding effect on UAV images caused by trees. Point clouds from the UAV and multiview images are generated via Pix4Dmapper. By merging two sets of point clouds via tie points, the complete building model is reconstructed. The 3D models are reconstructed using AutoCAD 2016 to generate vectors from the point clouds; SketchUp Make 2016 is used to rebuild a complete building model with textures. To apply 3D building models in urban planning and design, a modern approach is to rebuild the digital models; however, replacing the landscape design and building distribution in real time is difficult as the frequency of building replacement increases. One potential solution to these problems is augmented reality (AR). Using Unity3D and Vuforia to design and implement the smartphone application service, a markerless AR of the building model can be built. This study is aimed at providing technical and design skills related to urban planning, urban designing, and building information retrieval using AR.

Brute Force Matching Between Camera Shots and Synthetic Images from Point Clouds

NASA Astrophysics Data System (ADS)

Boerner, R.; Kröhnert, M.

2016-06-01

3D point clouds, acquired by state-of-the-art terrestrial laser scanning techniques (TLS), provide spatial information about accuracies up to several millimetres. Unfortunately, common TLS data has no spectral information about the covered scene. However, the matching of TLS data with images is important for monoplotting purposes and point cloud colouration. Well-established methods solve this issue by matching of close range images and point cloud data by fitting optical camera systems on top of laser scanners or rather using ground control points. The approach addressed in this paper aims for the matching of 2D image and 3D point cloud data from a freely moving camera within an environment covered by a large 3D point cloud, e.g. a 3D city model. The key advantage of the free movement affects augmented reality applications or real time measurements. Therefore, a so-called real image, captured by a smartphone camera, has to be matched with a so-called synthetic image which consists of reverse projected 3D point cloud data to a synthetic projection centre whose exterior orientation parameters match the parameters of the image, assuming an ideal distortion free camera.

Knowledge-Based Object Detection in Laser Scanning Point Clouds

NASA Astrophysics Data System (ADS)

Boochs, F.; Karmacharya, A.; Marbs, A.

2012-07-01

Object identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools available for use. Such modeling algorithms are data-driven and concentrate on specific features of the objects, being accessible to numerical models. We present an approach that brings the human expert knowledge about the scene, the objects inside, and their representation by the data and the behavior of algorithms to the machine. This "understanding" enables the machine to assist human interpretation of the scene inside the point cloud. Furthermore, it allows the machine to understand possibilities and limitations of algorithms and to take this into account within the processing chain. This not only assists the researchers in defining optimal processing steps, but also provides suggestions when certain changes or new details emerge from the point cloud. Our approach benefits from the advancement in knowledge technologies within the Semantic Web framework. This advancement has provided a strong base for applications based on knowledge management. In the article we will present and describe the knowledge technologies used for our approach such as Web Ontology Language (OWL), used for formulating the knowledge base and the Semantic Web Rule Language (SWRL) with 3D processing and topologic built-ins, aiming to combine geometrical analysis of 3D point clouds, and specialists' knowledge of the scene and algorithmic processing.

Extracting valley-ridge lines from point-cloud-based 3D fingerprint models.

PubMed

Pang, Xufang; Song, Zhan; Xie, Wuyuan

2013-01-01

3D fingerprinting is an emerging technology with the distinct advantage of touchless operation. More important, 3D fingerprint models contain more biometric information than traditional 2D fingerprint images. However, current approaches to fingerprint feature detection usually must transform the 3D models to a 2D space through unwrapping or other methods, which might introduce distortions. A new approach directly extracts valley-ridge features from point-cloud-based 3D fingerprint models. It first applies the moving least-squares method to fit a local paraboloid surface and represent the local point cloud area. It then computes the local surface's curvatures and curvature tensors to facilitate detection of the potential valley and ridge points. The approach projects those points to the most likely valley-ridge lines, using statistical means such as covariance analysis and cross correlation. To finally extract the valley-ridge lines, it grows the polylines that approximate the projected feature points and removes the perturbations between the sampled points. Experiments with different 3D fingerprint models demonstrate this approach's feasibility and performance.

Analysis of 3d Building Models Accuracy Based on the Airborne Laser Scanning Point Clouds

NASA Astrophysics Data System (ADS)

Ostrowski, W.; Pilarska, M.; Charyton, J.; Bakuła, K.

2018-05-01

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term "3D building models" can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

Underwater 3d Modeling: Image Enhancement and Point Cloud Filtering

NASA Astrophysics Data System (ADS)

Sarakinou, I.; Papadimitriou, K.; Georgoula, O.; Patias, P.

2016-06-01

This paper examines the results of image enhancement and point cloud filtering on the visual and geometric quality of 3D models for the representation of underwater features. Specifically it evaluates the combination of effects from the manual editing of images' radiometry (captured at shallow depths) and the selection of parameters for point cloud definition and mesh building (processed in 3D modeling software). Such datasets, are usually collected by divers, handled by scientists and used for geovisualization purposes. In the presented study, have been created 3D models from three sets of images (seafloor, part of a wreck and a small boat's wreck) captured at three different depths (3.5m, 10m and 14m respectively). Four models have been created from the first dataset (seafloor) in order to evaluate the results from the application of image enhancement techniques and point cloud filtering. The main process for this preliminary study included a) the definition of parameters for the point cloud filtering and the creation of a reference model, b) the radiometric editing of images, followed by the creation of three improved models and c) the assessment of results by comparing the visual and the geometric quality of improved models versus the reference one. Finally, the selected technique is tested on two other data sets in order to examine its appropriateness for different depths (at 10m and 14m) and different objects (part of a wreck and a small boat's wreck) in the context of an ongoing research in the Laboratory of Photogrammetry and Remote Sensing.

Real object-based 360-degree integral-floating display using multiple depth camera

NASA Astrophysics Data System (ADS)

Erdenebat, Munkh-Uchral; Dashdavaa, Erkhembaatar; Kwon, Ki-Chul; Wu, Hui-Ying; Yoo, Kwan-Hee; Kim, Young-Seok; Kim, Nam

2015-03-01

A novel 360-degree integral-floating display based on the real object is proposed. The general procedure of the display system is similar with conventional 360-degree integral-floating displays. Unlike previously presented 360-degree displays, the proposed system displays the 3D image generated from the real object in 360-degree viewing zone. In order to display real object in 360-degree viewing zone, multiple depth camera have been utilized to acquire the depth information around the object. Then, the 3D point cloud representations of the real object are reconstructed according to the acquired depth information. By using a special point cloud registration method, the multiple virtual 3D point cloud representations captured by each depth camera are combined as single synthetic 3D point cloud model, and the elemental image arrays are generated for the newly synthesized 3D point cloud model from the given anamorphic optic system's angular step. The theory has been verified experimentally, and it shows that the proposed 360-degree integral-floating display can be an excellent way to display real object in the 360-degree viewing zone.

Automatic pole-like object modeling via 3D part-based analysis of point cloud

NASA Astrophysics Data System (ADS)

He, Liu; Yang, Haoxiang; Huang, Yuchun

2016-10-01

Pole-like objects, including trees, lampposts and traffic signs, are indispensable part of urban infrastructure. With the advance of vehicle-based laser scanning (VLS), massive point cloud of roadside urban areas becomes applied in 3D digital city modeling. Based on the property that different pole-like objects have various canopy parts and similar trunk parts, this paper proposed the 3D part-based shape analysis to robustly extract, identify and model the pole-like objects. The proposed method includes: 3D clustering and recognition of trunks, voxel growing and part-based 3D modeling. After preprocessing, the trunk center is identified as the point that has local density peak and the largest minimum inter-cluster distance. Starting from the trunk centers, the remaining points are iteratively clustered to the same centers of their nearest point with higher density. To eliminate the noisy points, cluster border is refined by trimming boundary outliers. Then, candidate trunks are extracted based on the clustering results in three orthogonal planes by shape analysis. Voxel growing obtains the completed pole-like objects regardless of overlaying. Finally, entire trunk, branch and crown part are analyzed to obtain seven feature parameters. These parameters are utilized to model three parts respectively and get signal part-assembled 3D model. The proposed method is tested using the VLS-based point cloud of Wuhan University, China. The point cloud includes many kinds of trees, lampposts and other pole-like posters under different occlusions and overlaying. Experimental results show that the proposed method can extract the exact attributes and model the roadside pole-like objects efficiently.

Temporally consistent segmentation of point clouds

NASA Astrophysics Data System (ADS)

Owens, Jason L.; Osteen, Philip R.; Daniilidis, Kostas

2014-06-01

We consider the problem of generating temporally consistent point cloud segmentations from streaming RGB-D data, where every incoming frame extends existing labels to new points or contributes new labels while maintaining the labels for pre-existing segments. Our approach generates an over-segmentation based on voxel cloud connectivity, where a modified k-means algorithm selects supervoxel seeds and associates similar neighboring voxels to form segments. Given the data stream from a potentially mobile sensor, we solve for the camera transformation between consecutive frames using a joint optimization over point correspondences and image appearance. The aligned point cloud may then be integrated into a consistent model coordinate frame. Previously labeled points are used to mask incoming points from the new frame, while new and previous boundary points extend the existing segmentation. We evaluate the algorithm on newly-generated RGB-D datasets.

A model of cloud application assignments in software-defined storages

NASA Astrophysics Data System (ADS)

Bolodurina, Irina P.; Parfenov, Denis I.; Polezhaev, Petr N.; E Shukhman, Alexander

2017-01-01

The aim of this study is to analyze the structure and mechanisms of interaction of typical cloud applications and to suggest the approaches to optimize their placement in storage systems. In this paper, we describe a generalized model of cloud applications including the three basic layers: a model of application, a model of service, and a model of resource. The distinctive feature of the model suggested implies analyzing cloud resources from the user point of view and from the point of view of a software-defined infrastructure of the virtual data center (DC). The innovation character of this model is in describing at the same time the application data placements, as well as the state of the virtual environment, taking into account the network topology. The model of software-defined storage has been developed as a submodel within the resource model. This model allows implementing the algorithm for control of cloud application assignments in software-defined storages. Experimental researches returned this algorithm decreases in cloud application response time and performance growth in user request processes. The use of software-defined data storages allows the decrease in the number of physical store devices, which demonstrates the efficiency of our algorithm.

Building a LiDAR point cloud simulator: Testing algorithms for high resolution topographic change

NASA Astrophysics Data System (ADS)

Carrea, Dario; Abellán, Antonio; Derron, Marc-Henri; Jaboyedoff, Michel

2014-05-01

Terrestrial laser technique (TLS) is becoming a common tool in Geosciences, with clear applications ranging from the generation of a high resolution 3D models to the monitoring of unstable slopes and the quantification of morphological changes. Nevertheless, like every measurement techniques, TLS still has some limitations that are not clearly understood and affect the accuracy of the dataset (point cloud). A challenge in LiDAR research is to understand the influence of instrumental parameters on measurement errors during LiDAR acquisition. Indeed, different critical parameters interact with the scans quality at different ranges: the existence of shadow areas, the spatial resolution (point density), and the diameter of the laser beam, the incidence angle and the single point accuracy. The objective of this study is to test the main limitations of different algorithms usually applied on point cloud data treatment, from alignment to monitoring. To this end, we built in MATLAB(c) environment a LiDAR point cloud simulator able to recreate the multiple sources of errors related to instrumental settings that we normally observe in real datasets. In a first step we characterized the error from single laser pulse by modelling the influence of range and incidence angle on single point data accuracy. In a second step, we simulated the scanning part of the system in order to analyze the shifting and angular error effects. Other parameters have been added to the point cloud simulator, such as point spacing, acquisition window, etc., in order to create point clouds of simple and/or complex geometries. We tested the influence of point density and vitiating point of view on the Iterative Closest Point (ICP) alignment and also in some deformation tracking algorithm with same point cloud geometry, in order to determine alignment and deformation detection threshold. We also generated a series of high resolution point clouds in order to model small changes on different environments (erosion, landslide monitoring, etc) and we then tested the use of filtering techniques using 3D moving windows along the space and time, which considerably reduces data scattering due to the benefits of data redundancy. In conclusion, the simulator allowed us to improve our different algorithms and to understand how instrumental error affects final results. And also, improve the methodology of scans acquisition to find the best compromise between point density, positioning and acquisition time with the best accuracy possible to characterize the topographic change.

Methodologies for Development of Patient Specific Bone Models from Human Body CT Scans

NASA Astrophysics Data System (ADS)

Chougule, Vikas Narayan; Mulay, Arati Vinayak; Ahuja, Bharatkumar Bhagatraj

2016-06-01

This work deals with development of algorithm for physical replication of patient specific human bone and construction of corresponding implants/inserts RP models by using Reverse Engineering approach from non-invasive medical images for surgical purpose. In medical field, the volumetric data i.e. voxel and triangular facet based models are primarily used for bio-modelling and visualization, which requires huge memory space. On the other side, recent advances in Computer Aided Design (CAD) technology provides additional facilities/functions for design, prototyping and manufacturing of any object having freeform surfaces based on boundary representation techniques. This work presents a process to physical replication of 3D rapid prototyping (RP) physical models of human bone from various CAD modeling techniques developed by using 3D point cloud data which is obtained from non-invasive CT/MRI scans in DICOM 3.0 format. This point cloud data is used for construction of 3D CAD model by fitting B-spline curves through these points and then fitting surface between these curve networks by using swept blend techniques. This process also can be achieved by generating the triangular mesh directly from 3D point cloud data without developing any surface model using any commercial CAD software. The generated STL file from 3D point cloud data is used as a basic input for RP process. The Delaunay tetrahedralization approach is used to process the 3D point cloud data to obtain STL file. CT scan data of Metacarpus (human bone) is used as the case study for the generation of the 3D RP model. A 3D physical model of the human bone is generated on rapid prototyping machine and its virtual reality model is presented for visualization. The generated CAD model by different techniques is compared for the accuracy and reliability. The results of this research work are assessed for clinical reliability in replication of human bone in medical field.

Building Facade Modeling Under Line Feature Constraint Based on Close-Range Images

NASA Astrophysics Data System (ADS)

Liang, Y.; Sheng, Y. H.

2018-04-01

To solve existing problems in modeling facade of building merely with point feature based on close-range images , a new method for modeling building facade under line feature constraint is proposed in this paper. Firstly, Camera parameters and sparse spatial point clouds data were restored using the SFM , and 3D dense point clouds were generated with MVS; Secondly, the line features were detected based on the gradient direction , those detected line features were fit considering directions and lengths , then line features were matched under multiple types of constraints and extracted from multi-image sequence. At last, final facade mesh of a building was triangulated with point cloud and line features. The experiment shows that this method can effectively reconstruct the geometric facade of buildings using the advantages of combining point and line features of the close - range image sequence, especially in restoring the contour information of the facade of buildings.

Constraining the models' response of tropical low clouds to SST forcings using CALIPSO observations

NASA Astrophysics Data System (ADS)

Cesana, G.; Del Genio, A. D.; Ackerman, A. S.; Brient, F.; Fridlind, A. M.; Kelley, M.; Elsaesser, G.

2017-12-01

Low-cloud response to a warmer climate is still pointed out as being the largest source of uncertainty in the last generation of climate models. To date there is no consensus among the models on whether the tropical low cloudiness would increase or decrease in a warmer climate. In addition, it has been shown that - depending on their climate sensitivity - the models either predict deeper or shallower low clouds. Recently, several relationships between inter-model characteristics of the present-day climate and future climate changes have been highlighted. These so-called emergent constraints aim to target relevant model improvements and to constrain models' projections based on current climate observations. Here we propose to use - for the first time - 10 years of CALIPSO cloud statistics to assess the ability of the models to represent the vertical structure of tropical low clouds for abnormally warm SST. We use a simulator approach to compare observations and simulations and focus on the low-layered clouds (i.e. z < 3.2km) as well the more detailed level perspective of clouds (40 levels from 0 to 19km). Results show that in most models an increase of the SST leads to a decrease of the low-layer cloud fraction. Vertically, the clouds deepen namely by decreasing the cloud fraction in the lowest levels and increasing it around the top of the boundary-layer. This feature is coincident with an increase of the high-level cloud fraction (z > 6.5km). Although the models' spread is large, the multi-model mean captures the observed variations but with a smaller amplitude. We then employ the GISS model to investigate how changes in cloud parameterizations affect the response of low clouds to warmer SSTs on the one hand; and how they affect the variations of the model's cloud profiles with respect to environmental parameters on the other hand. Finally, we use CALIPSO observations to constrain the model by determining i) what set of parameters allows reproducing the observed relationships and ii) what are the consequences on the cloud feedbacks. These results point toward process-oriented constraints of low-cloud responses to surface warming and environmental parameters.

Towards 3D Matching of Point Clouds Derived from Oblique and Nadir Airborne Imagery

NASA Astrophysics Data System (ADS)

Zhang, Ming

Because of the low-expense high-efficient image collection process and the rich 3D and texture information presented in the images, a combined use of 2D airborne nadir and oblique images to reconstruct 3D geometric scene has a promising market for future commercial usage like urban planning or first responders. The methodology introduced in this thesis provides a feasible way towards fully automated 3D city modeling from oblique and nadir airborne imagery. In this thesis, the difficulty of matching 2D images with large disparity is avoided by grouping the images first and applying the 3D registration afterward. The procedure starts with the extraction of point clouds using a modified version of the RIT 3D Extraction Workflow. Then the point clouds are refined by noise removal and surface smoothing processes. Since the point clouds extracted from different image groups use independent coordinate systems, there are translation, rotation and scale differences existing. To figure out these differences, 3D keypoints and their features are extracted. For each pair of point clouds, an initial alignment and a more accurate registration are applied in succession. The final transform matrix presents the parameters describing the translation, rotation and scale requirements. The methodology presented in the thesis has been shown to behave well for test data. The robustness of this method is discussed by adding artificial noise to the test data. For Pictometry oblique aerial imagery, the initial alignment provides a rough alignment result, which contains a larger offset compared to that of test data because of the low quality of the point clouds themselves, but it can be further refined through the final optimization. The accuracy of the final registration result is evaluated by comparing it to the result obtained from manual selection of matched points. Using the method introduced, point clouds extracted from different image groups could be combined with each other to build a more complete point cloud, or be used as a complement to existing point clouds extracted from other sources. This research will both improve the state of the art of 3D city modeling and inspire new ideas in related fields.

Tropical Oceanic Precipitation Processes over Warm Pool: 2D and 3D Cloud Resolving Model Simulations

NASA Technical Reports Server (NTRS)

Tao, W.- K.; Johnson, D.

1998-01-01

Rainfall is a key link in the hydrologic cycle as well as the primary heat source for the atmosphere, The vertical distribution of convective latent-heat release modulates the large-scale circulations of the tropics, Furthermore, changes in the moisture distribution at middle and upper levels of the troposphere can affect cloud distributions and cloud liquid water and ice contents. How the incoming solar and outgoing longwave radiation respond to these changes in clouds is a major factor in assessing climate change. Present large-scale weather and climate models simulate cloud processes only crudely, reducing confidence in their predictions on both global and regional scales. One of the most promising methods to test physical parameterizations used in General Circulation Models (GCMS) and climate models is to use field observations together with Cloud Resolving Models (CRMs). The CRMs use more sophisticated and physically realistic parameterizations of cloud microphysical processes, and allow for their complex interactions with solar and infrared radiative transfer processes. The CRMs can reasonably well resolve the evolution, structure, and life cycles of individual clouds and cloud systems, The major objective of this paper is to investigate the latent heating, moisture and momenti,im budgets associated with several convective systems developed during the TOGA COARE IFA - westerly wind burst event (late December, 1992). The tool for this study is the Goddard Cumulus Ensemble (CCE) model which includes a 3-class ice-phase microphysical scheme, The model domain contains 256 x 256 grid points (using 2 km resolution) in the horizontal and 38 grid points (to a depth of 22 km depth) in the vertical, The 2D domain has 1024 grid points. The simulations are performed over a 7 day time period. We will examine (1) the precipitation processes (i.e., condensation/evaporation) and their interaction with warm pool; (2) the heating and moisture budgets in the convective and stratiform regions; (3) the cloud (upward-downward) mass fluxes in convective and stratiform regions; (4) characteristics of clouds (such as cloud size, updraft intensity and cloud lifetime) and the comparison of clouds with Radar observations. Differences and similarities in organization of convection between simulated 2D and 3D cloud systems. Preliminary results indicated that there is major differences between 2D and 3D simulated stratiform rainfall amount and convective updraft and downdraft mass fluxes.

Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation

NASA Astrophysics Data System (ADS)

Owers, Christopher J.; Rogers, Kerrylee; Woodroffe, Colin D.

2018-05-01

Above-ground biomass represents a small yet significant contributor to carbon storage in coastal wetlands. Despite this, above-ground biomass is often poorly quantified, particularly in areas where vegetation structure is complex. Traditional methods for providing accurate estimates involve harvesting vegetation to develop mangrove allometric equations and quantify saltmarsh biomass in quadrats. However broad scale application of these methods may not capture structural variability in vegetation resulting in a loss of detail and estimates with considerable uncertainty. Terrestrial laser scanning (TLS) collects high resolution three-dimensional point clouds capable of providing detailed structural morphology of vegetation. This study demonstrates that TLS is a suitable non-destructive method for estimating biomass of structurally complex coastal wetland vegetation. We compare volumetric models, 3-D surface reconstruction and rasterised volume, and point cloud elevation histogram modelling techniques to estimate biomass. Our results show that current volumetric modelling approaches for estimating TLS-derived biomass are comparable to traditional mangrove allometrics and saltmarsh harvesting. However, volumetric modelling approaches oversimplify vegetation structure by under-utilising the large amount of structural information provided by the point cloud. The point cloud elevation histogram model presented in this study, as an alternative to volumetric modelling, utilises all of the information within the point cloud, as opposed to sub-sampling based on specific criteria. This method is simple but highly effective for both mangrove (r2 = 0.95) and saltmarsh (r2 > 0.92) vegetation. Our results provide evidence that application of TLS in coastal wetlands is an effective non-destructive method to accurately quantify biomass for structurally complex vegetation.

The One to Multiple Automatic High Accuracy Registration of Terrestrial LIDAR and Optical Images

NASA Astrophysics Data System (ADS)

Wang, Y.; Hu, C.; Xia, G.; Xue, H.

2018-04-01

The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two - dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.

A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest

PubMed Central

Wang, Yunsheng; Weinacker, Holger; Koch, Barbara

2008-01-01

A procedure for both vertical canopy structure analysis and 3D single tree modelling based on Lidar point cloud is presented in this paper. The whole area of research is segmented into small study cells by a raster net. For each cell, a normalized point cloud whose point heights represent the absolute heights of the ground objects is generated from the original Lidar raw point cloud. The main tree canopy layers and the height ranges of the layers are detected according to a statistical analysis of the height distribution probability of the normalized raw points. For the 3D modelling of individual trees, individual trees are detected and delineated not only from the top canopy layer but also from the sub canopy layer. The normalized points are resampled into a local voxel space. A series of horizontal 2D projection images at the different height levels are then generated respect to the voxel space. Tree crown regions are detected from the projection images. Individual trees are then extracted by means of a pre-order forest traversal process through all the tree crown regions at the different height levels. Finally, 3D tree crown models of the extracted individual trees are reconstructed. With further analyses on the 3D models of individual tree crowns, important parameters such as crown height range, crown volume and crown contours at the different height levels can be derived. PMID:27879916

Applications of Panoramic Images: from 720° Panorama to Interior 3d Models of Augmented Reality

NASA Astrophysics Data System (ADS)

Lee, I.-C.; Tsai, F.

2015-05-01

A series of panoramic images are usually used to generate a 720° panorama image. Although panoramic images are typically used for establishing tour guiding systems, in this research, we demonstrate the potential of using panoramic images acquired from multiple sites to create not only 720° panorama, but also three-dimensional (3D) point clouds and 3D indoor models. Since 3D modeling is one of the goals of this research, the location of the panoramic sites needed to be carefully planned in order to maintain a robust result for close-range photogrammetry. After the images are acquired, panoramic images are processed into 720° panoramas, and these panoramas which can be used directly as panorama guiding systems or other applications. In addition to these straightforward applications, interior orientation parameters can also be estimated while generating 720° panorama. These parameters are focal length, principle point, and lens radial distortion. The panoramic images can then be processed with closerange photogrammetry procedures to extract the exterior orientation parameters and generate 3D point clouds. In this research, VisaulSFM, a structure from motion software is used to estimate the exterior orientation, and CMVS toolkit is used to generate 3D point clouds. Next, the 3D point clouds are used as references to create building interior models. In this research, Trimble Sketchup was used to build the model, and the 3D point cloud was added to the determining of locations of building objects using plane finding procedure. In the texturing process, the panorama images are used as the data source for creating model textures. This 3D indoor model was used as an Augmented Reality model replacing a guide map or a floor plan commonly used in an on-line touring guide system. The 3D indoor model generating procedure has been utilized in two research projects: a cultural heritage site at Kinmen, and Taipei Main Station pedestrian zone guidance and navigation system. The results presented in this paper demonstrate the potential of using panoramic images to generate 3D point clouds and 3D models. However, it is currently a manual and labor-intensive process. A research is being carried out to Increase the degree of automation of these procedures.

Point Cloud Based Relative Pose Estimation of a Satellite in Close Range

PubMed Central

Liu, Lujiang; Zhao, Gaopeng; Bo, Yuming

2016-01-01

Determination of the relative pose of satellites is essential in space rendezvous operations and on-orbit servicing missions. The key problems are the adoption of suitable sensor on board of a chaser and efficient techniques for pose estimation. This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor. A novel model based pose estimation method is proposed; it includes a fast and reliable pose initial acquisition method based on global optimal searching by processing the dense point cloud data directly, and a pose tracking method based on Iterative Closest Point algorithm. Also, a simulation system is presented in this paper in order to evaluate the performance of the sensor and generate simulated sensor point cloud data. It also provides truth pose of the test target so that the pose estimation error can be quantified. To investigate the effectiveness of the proposed approach and achievable pose accuracy, numerical simulation experiments are performed; results demonstrate algorithm capability of operating with point cloud directly and large pose variations. Also, a field testing experiment is conducted and results show that the proposed method is effective. PMID:27271633

Study on Huizhou architecture of point cloud registration based on optimized ICP algorithm

NASA Astrophysics Data System (ADS)

Zhang, Runmei; Wu, Yulu; Zhang, Guangbin; Zhou, Wei; Tao, Yuqian

2018-03-01

In view of the current point cloud registration software has high hardware requirements, heavy workload and moltiple interactive definition, the source of software with better processing effect is not open, a two--step registration method based on normal vector distribution feature and coarse feature based iterative closest point (ICP) algorithm is proposed in this paper. This method combines fast point feature histogram (FPFH) algorithm, define the adjacency region of point cloud and the calculation model of the distribution of normal vectors, setting up the local coordinate system for each key point, and obtaining the transformation matrix to finish rough registration, the rough registration results of two stations are accurately registered by using the ICP algorithm. Experimental results show that, compared with the traditional ICP algorithm, the method used in this paper has obvious time and precision advantages for large amount of point clouds.

Point Cloud and Digital Surface Model Generation from High Resolution Multiple View Stereo Satellite Imagery

NASA Astrophysics Data System (ADS)

Gong, K.; Fritsch, D.

2018-05-01

Nowadays, multiple-view stereo satellite imagery has become a valuable data source for digital surface model generation and 3D reconstruction. In 2016, a well-organized multiple view stereo publicly benchmark for commercial satellite imagery has been released by the John Hopkins University Applied Physics Laboratory, USA. This benchmark motivates us to explore the method that can generate accurate digital surface models from a large number of high resolution satellite images. In this paper, we propose a pipeline for processing the benchmark data to digital surface models. As a pre-procedure, we filter all the possible image pairs according to the incidence angle and capture date. With the selected image pairs, the relative bias-compensated model is applied for relative orientation. After the epipolar image pairs' generation, dense image matching and triangulation, the 3D point clouds and DSMs are acquired. The DSMs are aligned to a quasi-ground plane by the relative bias-compensated model. We apply the median filter to generate the fused point cloud and DSM. By comparing with the reference LiDAR DSM, the accuracy, the completeness and the robustness are evaluated. The results show, that the point cloud reconstructs the surface with small structures and the fused DSM generated by our pipeline is accurate and robust.

Automatic Modelling of Rubble Mound Breakwaters from LIDAR Data

NASA Astrophysics Data System (ADS)

Bueno, M.; Díaz-Vilariño, L.; González-Jorge, H.; Martínez-Sánchez, J.; Arias, P.

2015-08-01

Rubble mound breakwaters maintenance is critical to the protection of beaches and ports. LiDAR systems provide accurate point clouds from the emerged part of the structure that can be modelled to make it more useful and easy to handle. This work introduces a methodology for the automatic modelling of breakwaters with armour units of cube shape. The algorithm is divided in three main steps: normal vector computation, plane segmentation, and cube reconstruction. Plane segmentation uses the normal orientation of the points and the edge length of the cube. Cube reconstruction uses the intersection of three perpendicular planes and the edge length. Three point clouds cropped from the main point cloud of the structure are used for the tests. The number of cubes detected is around 56 % for two of the point clouds and 32 % for the third one over the total physical cubes. Accuracy assessment is done by comparison with manually drawn cubes calculating the differences between the vertexes. It ranges between 6.4 cm and 15 cm. Computing time ranges between 578.5 s and 8018.2 s. The computing time increases with the number of cubes and the requirements of collision detection.

The Use of Uas for Rapid 3d Mapping in Geomatics Education

NASA Astrophysics Data System (ADS)

Teo, Tee-Ann; Tian-Yuan Shih, Peter; Yu, Sz-Cheng; Tsai, Fuan

2016-06-01

With the development of technology, UAS is an advance technology to support rapid mapping for disaster response. The aim of this study is to develop educational modules for UAS data processing in rapid 3D mapping. The designed modules for this study are focused on UAV data processing from available freeware or trial software for education purpose. The key modules include orientation modelling, 3D point clouds generation, image georeferencing and visualization. The orientation modelling modules adopts VisualSFM to determine the projection matrix for each image station. Besides, the approximate ground control points are measured from OpenStreetMap for absolute orientation. The second module uses SURE and the orientation files from previous module for 3D point clouds generation. Then, the ground point selection and digital terrain model generation can be archived by LAStools. The third module stitches individual rectified images into a mosaic image using Microsoft ICE (Image Composite Editor). The last module visualizes and measures the generated dense point clouds in CloudCompare. These comprehensive UAS processing modules allow the students to gain the skills to process and deliver UAS photogrammetric products in rapid 3D mapping. Moreover, they can also apply the photogrammetric products for analysis in practice.

From Laser Scanning to Finite Element Analysis of Complex Buildings by Using a Semi-Automatic Procedure.

PubMed

Castellazzi, Giovanni; D'Altri, Antonio Maria; Bitelli, Gabriele; Selvaggi, Ilenia; Lambertini, Alessandro

2015-07-28

In this paper, a new semi-automatic procedure to transform three-dimensional point clouds of complex objects to three-dimensional finite element models is presented and validated. The procedure conceives of the point cloud as a stacking of point sections. The complexity of the clouds is arbitrary, since the procedure is designed for terrestrial laser scanner surveys applied to buildings with irregular geometry, such as historical buildings. The procedure aims at solving the problems connected to the generation of finite element models of these complex structures by constructing a fine discretized geometry with a reduced amount of time and ready to be used with structural analysis. If the starting clouds represent the inner and outer surfaces of the structure, the resulting finite element model will accurately capture the whole three-dimensional structure, producing a complex solid made by voxel elements. A comparison analysis with a CAD-based model is carried out on a historical building damaged by a seismic event. The results indicate that the proposed procedure is effective and obtains comparable models in a shorter time, with an increased level of automation.

Microphysical Processes Affecting the Pinatubo Volcanic Plume

NASA Technical Reports Server (NTRS)

Hamill, Patrick; Houben, Howard; Young, Richard; Turco, Richard; Zhao, Jingxia

1996-01-01

In this paper we consider microphysical processes which affect the formation of sulfate particles and their size distribution in a dispersing cloud. A model for the dispersion of the Mt. Pinatubo volcanic cloud is described. We then consider a single point in the dispersing cloud and study the effects of nucleation, condensation and coagulation on the time evolution of the particle size distribution at that point.

D Point Cloud Model Colorization by Dense Registration of Digital Images

NASA Astrophysics Data System (ADS)

Crombez, N.; Caron, G.; Mouaddib, E.

2015-02-01

Architectural heritage is a historic and artistic property which has to be protected, preserved, restored and must be shown to the public. Modern tools like 3D laser scanners are more and more used in heritage documentation. Most of the time, the 3D laser scanner is completed by a digital camera which is used to enrich the accurate geometric informations with the scanned objects colors. However, the photometric quality of the acquired point clouds is generally rather low because of several problems presented below. We propose an accurate method for registering digital images acquired from any viewpoints on point clouds which is a crucial step for a good colorization by colors projection. We express this image-to-geometry registration as a pose estimation problem. The camera pose is computed using the entire images intensities under a photometric visual and virtual servoing (VVS) framework. The camera extrinsic and intrinsic parameters are automatically estimated. Because we estimates the intrinsic parameters we do not need any informations about the camera which took the used digital image. Finally, when the point cloud model and the digital image are correctly registered, we project the 3D model in the digital image frame and assign new colors to the visible points. The performance of the approach is proven in simulation and real experiments on indoor and outdoor datasets of the cathedral of Amiens, which highlight the success of our method, leading to point clouds with better photometric quality and resolution.

a Threshold-Free Filtering Algorithm for Airborne LIDAR Point Clouds Based on Expectation-Maximization

NASA Astrophysics Data System (ADS)

Hui, Z.; Cheng, P.; Ziggah, Y. Y.; Nie, Y.

2018-04-01

Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting or thresholds adjusting, which will be time-consuming and reduce the degree of automation of the algorithm. To overcome this problem, this paper proposed a threshold-free filtering algorithm based on expectation-maximization. The proposed algorithm is developed based on an assumption that point clouds are seen as a mixture of Gaussian models. The separation of ground points and non-ground points from point clouds can be replaced as a separation of a mixed Gaussian model. Expectation-maximization (EM) is applied for realizing the separation. EM is used to calculate maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or object can be computed. After several iterations, point clouds can be labelled as the component with a larger likelihood. Furthermore, intensity information was also utilized to optimize the filtering results acquired using the EM method. The proposed algorithm was tested using two different datasets used in practice. Experimental results showed that the proposed method can filter non-ground points effectively. To quantitatively evaluate the proposed method, this paper adopted the dataset provided by the ISPRS for the test. The proposed algorithm can obtain a 4.48 % total error which is much lower than most of the eight classical filtering algorithms reported by the ISPRS.

Air Modeling - Observational Meteorological Data

EPA Pesticide Factsheets

Observed meteorological data for use in air quality modeling consist of physical parameters that are measured directly by instrumentation, and include temperature, dew point, wind direction, wind speed, cloud cover, cloud layer(s), ceiling height,

Comparision of photogrammetric point clouds with BIM building elements for construction progress monitoring

NASA Astrophysics Data System (ADS)

Tuttas, S.; Braun, A.; Borrmann, A.; Stilla, U.

2014-08-01

For construction progress monitoring a planned state of the construction at a certain time (as-planed) has to be compared to the actual state (as-built). The as-planed state is derived from a building information model (BIM), which contains the geometry of the building and the construction schedule. In this paper we introduce an approach for the generation of an as-built point cloud by photogrammetry. It is regarded that that images on a construction cannot be taken from everywhere it seems to be necessary. Because of this we use a combination of structure from motion process together with control points to create a scaled point cloud in a consistent coordinate system. Subsequently this point cloud is used for an as-built - as-planed comparison. For that voxels of an octree are marked as occupied, free or unknown by raycasting based on the triangulated points and the camera positions. This allows to identify not existing building parts. For the verification of the existence of building parts a second test based on the points in front and behind the as-planed model planes is performed. The proposed procedure is tested based on an inner city construction site under real conditions.

Automatic Building Abstraction from Aerial Photogrammetry

NASA Astrophysics Data System (ADS)

Ley, A.; Hänsch, R.; Hellwich, O.

2017-09-01

Multi-view stereo has been shown to be a viable tool for the creation of realistic 3D city models. Nevertheless, it still states significant challenges since it results in dense, but noisy and incomplete point clouds when applied to aerial images. 3D city modelling usually requires a different representation of the 3D scene than these point clouds. This paper applies a fully-automatic pipeline to generate a simplified mesh from a given dense point cloud. The mesh provides a certain level of abstraction as it only consists of relatively large planar and textured surfaces. Thus, it is possible to remove noise, outlier, as well as clutter, while maintaining a high level of accuracy.

Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

NASA Astrophysics Data System (ADS)

Ge, Xuming

2017-08-01

The coarse registration of point clouds from urban building scenes has become a key topic in applications of terrestrial laser scanning technology. Sampling-based algorithms in the random sample consensus (RANSAC) model have emerged as mainstream solutions to address coarse registration problems. In this paper, we propose a novel combined solution to automatically align two markerless point clouds from building scenes. Firstly, the method segments non-ground points from ground points. Secondly, the proposed method detects feature points from each cross section and then obtains semantic keypoints by connecting feature points with specific rules. Finally, the detected semantic keypoints from two point clouds act as inputs to a modified 4PCS algorithm. Examples are presented and the results compared with those of K-4PCS to demonstrate the main contributions of the proposed method, which are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.

Point clouds in BIM

NASA Astrophysics Data System (ADS)

Antova, Gergana; Kunchev, Ivan; Mickrenska-Cherneva, Christina

2016-10-01

The representation of physical buildings in Building Information Models (BIM) has been a subject of research since four decades in the fields of Construction Informatics and GeoInformatics. The early digital representations of buildings mainly appeared as 3D drawings constructed by CAD software, and the 3D representation of the buildings was only geometric, while semantics and topology were out of modelling focus. On the other hand, less detailed building representations, with often focus on ‘outside’ representations were also found in form of 2D /2,5D GeoInformation models. Point clouds from 3D laser scanning data give a full and exact representation of the building geometry. The article presents different aspects and the benefits of using point clouds in BIM in the different stages of a lifecycle of a building.

Filtering Photogrammetric Point Clouds Using Standard LIDAR Filters Towards DTM Generation

NASA Astrophysics Data System (ADS)

Zhang, Z.; Gerke, M.; Vosselman, G.; Yang, M. Y.

2018-05-01

Digital Terrain Models (DTMs) can be generated from point clouds acquired by laser scanning or photogrammetric dense matching. During the last two decades, much effort has been paid to developing robust filtering algorithms for the airborne laser scanning (ALS) data. With the point cloud quality from dense image matching (DIM) getting better and better, the research question that arises is whether those standard Lidar filters can be used to filter photogrammetric point clouds as well. Experiments are implemented to filter two dense matching point clouds with different noise levels. Results show that the standard Lidar filter is robust to random noise. However, artefacts and blunders in the DIM points often appear due to low contrast or poor texture in the images. Filtering will be erroneous in these locations. Filtering the DIM points pre-processed by a ranking filter will bring higher Type II error (i.e. non-ground points actually labelled as ground points) but much lower Type I error (i.e. bare ground points labelled as non-ground points). Finally, the potential DTM accuracy that can be achieved by DIM points is evaluated. Two DIM point clouds derived by Pix4Dmapper and SURE are compared. On grassland dense matching generates points higher than the true terrain surface, which will result in incorrectly elevated DTMs. The application of the ranking filter leads to a reduced bias in the DTM height, but a slightly increased noise level.

A point cloud modeling method based on geometric constraints mixing the robust least squares method

NASA Astrophysics Data System (ADS)

Yue, JIanping; Pan, Yi; Yue, Shun; Liu, Dapeng; Liu, Bin; Huang, Nan

2016-10-01

The appearance of 3D laser scanning technology has provided a new method for the acquisition of spatial 3D information. It has been widely used in the field of Surveying and Mapping Engineering with the characteristics of automatic and high precision. 3D laser scanning data processing process mainly includes the external laser data acquisition, the internal industry laser data splicing, the late 3D modeling and data integration system. For the point cloud modeling, domestic and foreign researchers have done a lot of research. Surface reconstruction technology mainly include the point shape, the triangle model, the triangle Bezier surface model, the rectangular surface model and so on, and the neural network and the Alfa shape are also used in the curved surface reconstruction. But in these methods, it is often focused on single surface fitting, automatic or manual block fitting, which ignores the model's integrity. It leads to a serious problems in the model after stitching, that is, the surfaces fitting separately is often not satisfied with the well-known geometric constraints, such as parallel, vertical, a fixed angle, or a fixed distance. However, the research on the special modeling theory such as the dimension constraint and the position constraint is not used widely. One of the traditional modeling methods adding geometric constraints is a method combing the penalty function method and the Levenberg-Marquardt algorithm (L-M algorithm), whose stability is pretty good. But in the research process, it is found that the method is greatly influenced by the initial value. In this paper, we propose an improved method of point cloud model taking into account the geometric constraint. We first apply robust least-squares to enhance the initial value's accuracy, and then use penalty function method to transform constrained optimization problems into unconstrained optimization problems, and finally solve the problems using the L-M algorithm. The experimental results show that the internal accuracy is improved, and it is shown that the improved method for point clouds modeling proposed by this paper outperforms the traditional point clouds modeling methods.

A Voxel-Based Approach for Imaging Voids in Three-Dimensional Point Clouds

NASA Astrophysics Data System (ADS)

Salvaggio, Katie N.

Geographically accurate scene models have enormous potential beyond that of just simple visualizations in regard to automated scene generation. In recent years, thanks to ever increasing computational efficiencies, there has been significant growth in both the computer vision and photogrammetry communities pertaining to automatic scene reconstruction from multiple-view imagery. The result of these algorithms is a three-dimensional (3D) point cloud which can be used to derive a final model using surface reconstruction techniques. However, the fidelity of these point clouds has not been well studied, and voids often exist within the point cloud. Voids exist in texturally difficult areas, as well as areas where multiple views were not obtained during collection, constant occlusion existed due to collection angles or overlapping scene geometry, or in regions that failed to triangulate accurately. It may be possible to fill in small voids in the scene using surface reconstruction or hole-filling techniques, but this is not the case with larger more complex voids, and attempting to reconstruct them using only the knowledge of the incomplete point cloud is neither accurate nor aesthetically pleasing. A method is presented for identifying voids in point clouds by using a voxel-based approach to partition the 3D space. By using collection geometry and information derived from the point cloud, it is possible to detect unsampled voxels such that voids can be identified. This analysis takes into account the location of the camera and the 3D points themselves to capitalize on the idea of free space, such that voxels that lie on the ray between the camera and point are devoid of obstruction, as a clear line of sight is a necessary requirement for reconstruction. Using this approach, voxels are classified into three categories: occupied (contains points from the point cloud), free (rays from the camera to the point passed through the voxel), and unsampled (does not contain points and no rays passed through the area). Voids in the voxel space are manifested as unsampled voxels. A similar line-of-sight analysis can then be used to pinpoint locations at aircraft altitude at which the voids in the point clouds could theoretically be imaged. This work is based on the assumption that inclusion of more images of the void areas in the 3D reconstruction process will reduce the number of voids in the point cloud that were a result of lack of coverage. Voids resulting from texturally difficult areas will not benefit from more imagery in the reconstruction process, and thus are identified and removed prior to the determination of future potential imaging locations.

Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds.

PubMed

Tran, Thi Huong Giang; Ressl, Camillo; Pfeifer, Norbert

2018-02-03

This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.

A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising

NASA Astrophysics Data System (ADS)

Cui, Xin; Li, Shipeng; Yan, Xiutian; He, Xinhua

2018-04-01

In order to remove the noise of three-dimensional scattered point cloud and smooth the data without damnify the sharp geometric feature simultaneity, a novel algorithm is proposed in this paper. The feature-preserving weight is added to fuzzy c-means algorithm which invented a curvature weighted fuzzy c-means clustering algorithm. Firstly, the large-scale outliers are removed by the statistics of r radius neighboring points. Then, the algorithm estimates the curvature of the point cloud data by using conicoid parabolic fitting method and calculates the curvature feature value. Finally, the proposed clustering algorithm is adapted to calculate the weighted cluster centers. The cluster centers are regarded as the new points. The experimental results show that this approach is efficient to different scale and intensities of noise in point cloud with a high precision, and perform a feature-preserving nature at the same time. Also it is robust enough to different noise model.

What's the Point of a Raster ? Advantages of 3D Point Cloud Processing over Raster Based Methods for Accurate Geomorphic Analysis of High Resolution Topography.

NASA Astrophysics Data System (ADS)

Lague, D.

2014-12-01

High Resolution Topographic (HRT) datasets are predominantly stored and analyzed as 2D raster grids of elevations (i.e., Digital Elevation Models). Raster grid processing is common in GIS software and benefits from a large library of fast algorithms dedicated to geometrical analysis, drainage network computation and topographic change measurement. Yet, all instruments or methods currently generating HRT datasets (e.g., ALS, TLS, SFM, stereo satellite imagery) output natively 3D unstructured point clouds that are (i) non-regularly sampled, (ii) incomplete (e.g., submerged parts of river channels are rarely measured), and (iii) include 3D elements (e.g., vegetation, vertical features such as river banks or cliffs) that cannot be accurately described in a DEM. Interpolating the raw point cloud onto a 2D grid generally results in a loss of position accuracy, spatial resolution and in more or less controlled interpolation. Here I demonstrate how studying earth surface topography and processes directly on native 3D point cloud datasets offers several advantages over raster based methods: point cloud methods preserve the accuracy of the original data, can better handle the evaluation of uncertainty associated to topographic change measurements and are more suitable to study vegetation characteristics and steep features of the landscape. In this presentation, I will illustrate and compare Point Cloud based and Raster based workflows with various examples involving ALS, TLS and SFM for the analysis of bank erosion processes in bedrock and alluvial rivers, rockfall statistics (including rockfall volume estimate directly from point clouds) and the interaction of vegetation/hydraulics and sedimentation in salt marshes. These workflows use 2 recently published algorithms for point cloud classification (CANUPO) and point cloud comparison (M3C2) now implemented in the open source software CloudCompare.

Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target.

PubMed

Yin, Fang; Chou, Wusheng; Wu, Yun; Yang, Guang; Xu, Song

2018-03-28

This paper proposes an autonomous algorithm to determine the relative pose between the chaser spacecraft and the uncooperative space target, which is essential in advanced space applications, e.g., on-orbit serving missions. The proposed method, named Congruent Tetrahedron Align (CTA) algorithm, uses the very sparse unorganized 3D point cloud acquired by a LIDAR sensor, and does not require any prior pose information. The core of the method is to determine the relative pose by looking for the congruent tetrahedron in scanning point cloud and model point cloud on the basis of its known model. The two-level index hash table is built for speeding up the search speed. In addition, the Iterative Closest Point (ICP) algorithm is used for pose tracking after CTA. In order to evaluate the method in arbitrary initial attitude, a simulated system is presented. Specifically, the performance of the proposed method to provide the initial pose needed for the tracking algorithm is demonstrated, as well as their robustness against noise. Finally, a field experiment is conducted and the results demonstrated the effectiveness of the proposed method.

Sparse Unorganized Point Cloud Based Relative Pose Estimation for Uncooperative Space Target

PubMed Central

Chou, Wusheng; Wu, Yun; Yang, Guang; Xu, Song

2018-01-01

This paper proposes an autonomous algorithm to determine the relative pose between the chaser spacecraft and the uncooperative space target, which is essential in advanced space applications, e.g., on-orbit serving missions. The proposed method, named Congruent Tetrahedron Align (CTA) algorithm, uses the very sparse unorganized 3D point cloud acquired by a LIDAR sensor, and does not require any prior pose information. The core of the method is to determine the relative pose by looking for the congruent tetrahedron in scanning point cloud and model point cloud on the basis of its known model. The two-level index hash table is built for speeding up the search speed. In addition, the Iterative Closest Point (ICP) algorithm is used for pose tracking after CTA. In order to evaluate the method in arbitrary initial attitude, a simulated system is presented. Specifically, the performance of the proposed method to provide the initial pose needed for the tracking algorithm is demonstrated, as well as their robustness against noise. Finally, a field experiment is conducted and the results demonstrated the effectiveness of the proposed method. PMID:29597323

Point cloud registration from local feature correspondences-Evaluation on challenging datasets.

PubMed

Petricek, Tomas; Svoboda, Tomas

2017-01-01

Registration of laser scans, or point clouds in general, is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. A coarse alignment of the point clouds is generally needed before applying local methods such as the Iterative Closest Point (ICP) algorithm. We propose a feature-based approach to point cloud registration and evaluate the proposed method and its individual components on challenging real-world datasets. For a moderate overlap between the laser scans, the method provides a superior registration accuracy compared to state-of-the-art methods including Generalized ICP, 3D Normal-Distribution Transform, Fast Point-Feature Histograms, and 4-Points Congruent Sets. Compared to the surface normals, the points as the underlying features yield higher performance in both keypoint detection and establishing local reference frames. Moreover, sign disambiguation of the basis vectors proves to be an important aspect in creating repeatable local reference frames. A novel method for sign disambiguation is proposed which yields highly repeatable reference frames.

Semantic Segmentation of Building Elements Using Point Cloud Hashing

NASA Astrophysics Data System (ADS)

Chizhova, M.; Gurianov, A.; Hess, M.; Luhmann, T.; Brunn, A.; Stilla, U.

2018-05-01

For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect) into different building types and structural elements (dome, nave, transept etc.), including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling).

Simultaneous colour visualizations of multiple ALS point cloud attributes for land cover and vegetation analysis

NASA Astrophysics Data System (ADS)

Zlinszky, András; Schroiff, Anke; Otepka, Johannes; Mandlburger, Gottfried; Pfeifer, Norbert

2014-05-01

LIDAR point clouds hold valuable information for land cover and vegetation analysis, not only in the spatial distribution of the points but also in their various attributes. However, LIDAR point clouds are rarely used for visual interpretation, since for most users, the point cloud is difficult to interpret compared to passive optical imagery. Meanwhile, point cloud viewing software is available allowing interactive 3D interpretation, but typically only one attribute at a time. This results in a large number of points with the same colour, crowding the scene and often obscuring detail. We developed a scheme for mapping information from multiple LIDAR point attributes to the Red, Green, and Blue channels of a widely used LIDAR data format, which are otherwise mostly used to add information from imagery to create "photorealistic" point clouds. The possible combinations of parameters are therefore represented in a wide range of colours, but relative differences in individual parameter values of points can be well understood. The visualization was implemented in OPALS software, using a simple and robust batch script, and is viewer independent since the information is stored in the point cloud data file itself. In our case, the following colour channel assignment delivered best results: Echo amplitude in the Red, echo width in the Green and normalized height above a Digital Terrain Model in the Blue channel. With correct parameter scaling (but completely without point classification), points belonging to asphalt and bare soil are dark red, low grassland and crop vegetation are bright red to yellow, shrubs and low trees are green and high trees are blue. Depending on roof material and DTM quality, buildings are shown from red through purple to dark blue. Erroneously high or low points, or points with incorrect amplitude or echo width usually have colours contrasting from terrain or vegetation. This allows efficient visual interpretation of the point cloud in planar, profile and 3D views since it reduces crowding of the scene and delivers intuitive contextual information. The resulting visualization has proved useful for vegetation analysis for habitat mapping, and can also be applied as a first step for point cloud level classification. An interactive demonstration of the visualization script is shown during poster attendance, including the opportunity to view your own point cloud sample files.

Photogrammetric Recording and Reconstruction of Town Scale Models - the Case of the Plan-Relief of Strasbourg

NASA Astrophysics Data System (ADS)

Macher, H.; Grussenmeyer, P.; Landes, T.; Halin, G.; Chevrier, C.; Huyghe, O.

2017-08-01

The French collection of Plan-Reliefs, scale models of fortified towns, constitutes a precious testimony of the history of France. The aim of the URBANIA project is the valorisation and the diffusion of this Heritage through the creation of virtual models. The town scale model of Strasbourg at 1/600 currently exhibited in the Historical Museum of Strasbourg was selected as a case study. In this paper, the photogrammetric recording of this scale model is first presented. The acquisition protocol as well as the data post-processing are detailed. Then, the modelling of the city and more specially building blocks is investigated. Based on point clouds of the scale model, the extraction of roof elements is considered. It deals first with the segmentation of the point cloud into building blocks. Then, for each block, points belonging to roofs are identified and the extraction of chimney point clouds as well as roof ridges and roof planes is performed. Finally, the 3D parametric modelling of the building blocks is studied by considering roof polygons and polylines describing chimneys as input. In a future works section, the semantically enrichment and the potential usage scenarios of the scale model are envisaged.

3D granulometry: grain-scale shape and size distribution from point cloud dataset of river environments

NASA Astrophysics Data System (ADS)

Steer, Philippe; Lague, Dimitri; Gourdon, Aurélie; Croissant, Thomas; Crave, Alain

2016-04-01

The grain-scale morphology of river sediments and their size distribution are important factors controlling the efficiency of fluvial erosion and transport. In turn, constraining the spatial evolution of these two metrics offer deep insights on the dynamics of river erosion and sediment transport from hillslopes to the sea. However, the size distribution of river sediments is generally assessed using statistically-biased field measurements and determining the grain-scale shape of river sediments remains a real challenge in geomorphology. Here we determine, with new methodological approaches based on the segmentation and geomorphological fitting of 3D point cloud dataset, the size distribution and grain-scale shape of sediments located in river environments. Point cloud segmentation is performed using either machine-learning algorithms or geometrical criterion, such as local plan fitting or curvature analysis. Once the grains are individualized into several sub-clouds, each grain-scale morphology is determined using a 3D geometrical fitting algorithm applied on the sub-cloud. If different geometrical models can be conceived and tested, only ellipsoidal models were used in this study. A phase of results checking is then performed to remove grains showing a best-fitting model with a low level of confidence. The main benefits of this automatic method are that it provides 1) an un-biased estimate of grain-size distribution on a large range of scales, from centimeter to tens of meters; 2) access to a very large number of data, only limited by the number of grains in the point-cloud dataset; 3) access to the 3D morphology of grains, in turn allowing to develop new metrics characterizing the size and shape of grains. The main limit of this method is that it is only able to detect grains with a characteristic size greater than the resolution of the point cloud. This new 3D granulometric method is then applied to river terraces both in the Poerua catchment in New-Zealand and along the Laonong river in Taiwan, which point clouds were obtained using both terrestrial lidar scanning and structure from motion photogrammetry.

Research of MPPT for photovoltaic generation based on two-dimensional cloud model

NASA Astrophysics Data System (ADS)

Liu, Shuping; Fan, Wei

2013-03-01

The cloud model is a mathematical representation to fuzziness and randomness in linguistic concepts. It represents a qualitative concept with expected value Ex, entropy En and hyper entropy He, and integrates the fuzziness and randomness of a linguistic concept in a unified way. This model is a new method for transformation between qualitative and quantitative in the knowledge. This paper is introduced MPPT (maximum power point tracking, MPPT) controller based two- dimensional cloud model through analysis of auto-optimization MPPT control of photovoltaic power system and combining theory of cloud model. Simulation result shows that the cloud controller is simple and easy, directly perceived through the senses, and has strong robustness, better control performance.

Nonuniform multiview color texture mapping of image sequence and three-dimensional model for faded cultural relics with sift feature points

NASA Astrophysics Data System (ADS)

Li, Na; Gong, Xingyu; Li, Hongan; Jia, Pengtao

2018-01-01

For faded relics, such as Terracotta Army, the 2D-3D registration between an optical camera and point cloud model is an important part for color texture reconstruction and further applications. This paper proposes a nonuniform multiview color texture mapping for the image sequence and the three-dimensional (3D) model of point cloud collected by Handyscan3D. We first introduce nonuniform multiview calibration, including the explanation of its algorithm principle and the analysis of its advantages. We then establish transformation equations based on sift feature points for the multiview image sequence. At the same time, the selection of nonuniform multiview sift feature points is introduced in detail. Finally, the solving process of the collinear equations based on multiview perspective projection is given with three steps and the flowchart. In the experiment, this method is applied to the color reconstruction of the kneeling figurine, Tangsancai lady, and general figurine. These results demonstrate that the proposed method provides an effective support for the color reconstruction of the faded cultural relics and be able to improve the accuracy of 2D-3D registration between the image sequence and the point cloud model.

Hierarchical Regularization of Polygons for Photogrammetric Point Clouds of Oblique Images

NASA Astrophysics Data System (ADS)

Xie, L.; Hu, H.; Zhu, Q.; Wu, B.; Zhang, Y.

2017-05-01

Despite the success of multi-view stereo (MVS) reconstruction from massive oblique images in city scale, only point clouds and triangulated meshes are available from existing MVS pipelines, which are topologically defect laden, free of semantical information and hard to edit and manipulate interactively in further applications. On the other hand, 2D polygons and polygonal models are still the industrial standard. However, extraction of the 2D polygons from MVS point clouds is still a non-trivial task, given the fact that the boundaries of the detected planes are zigzagged and regularities, such as parallel and orthogonal, cannot preserve. Aiming to solve these issues, this paper proposes a hierarchical polygon regularization method for the photogrammetric point clouds from existing MVS pipelines, which comprises of local and global levels. After boundary points extraction, e.g. using alpha shapes, the local level is used to consolidate the original points, by refining the orientation and position of the points using linear priors. The points are then grouped into local segments by forward searching. In the global level, regularities are enforced through a labeling process, which encourage the segments share the same label and the same label represents segments are parallel or orthogonal. This is formulated as Markov Random Field and solved efficiently. Preliminary results are made with point clouds from aerial oblique images and compared with two classical regularization methods, which have revealed that the proposed method are more powerful in abstracting a single building and is promising for further 3D polygonal model reconstruction and GIS applications.

Low cost digital photogrammetry: From the extraction of point clouds by SFM technique to 3D mathematical modeling

NASA Astrophysics Data System (ADS)

Michele, Mangiameli; Giuseppe, Mussumeci; Salvatore, Zito

2017-07-01

The Structure From Motion (SFM) is a technique applied to a series of photographs of an object that returns a 3D reconstruction made up by points in the space (point clouds). This research aims at comparing the results of the SFM approach with the results of a 3D laser scanning in terms of density and accuracy of the model. The experience was conducted by detecting several architectural elements (walls and portals of historical buildings) both with a 3D laser scanner of the latest generation and an amateur photographic camera. The point clouds acquired by laser scanner and those acquired by the photo camera have been systematically compared. In particular we present the experience carried out on the "Don Diego Pappalardo Palace" site in Pedara (Catania, Sicily).

Hole-ness of point clouds

NASA Astrophysics Data System (ADS)

Gronz, Oliver; Seeger, Manuel; Klaes, Björn; Casper, Markus C.; Ries, Johannes B.

2015-04-01

Accurate and dense 3D models of soil surfaces can be used in various ways: They can be used as initial shapes for erosion models. They can be used as benchmark shapes for erosion model outputs. They can be used to derive metrics, such as random roughness... One easy and low-cost method to produce these models is structure from motion (SfM). Using this method, two questions arise: Does the soil moisture, which changes the colour, albedo and reflectivity of the soil, influence the model quality? How can the model quality be evaluated? To answer these questions, a suitable data set has been produced: soil has been placed on a tray and areas with different roughness structures have been formed. For different moisture states - dry, medium, saturated - and two different lighting conditions - direct and indirect - sets of high-resolution images at the same camera positions have been taken. From the six image sets, 3D point clouds have been produced using VisualSfM. The visual inspection of the 3D models showed that all models have different areas, where holes of different sizes occur. But it is obviously a subjective task to determine the model's quality by visual inspection. One typical approach to evaluate model quality objectively is to estimate the point density on a regular, two-dimensional grid: the number of 3D points in each grid cell projected on a plane is calculated. This works well for surfaces that do not show vertical structures. Along vertical structures, many points will be projected on the same grid cell and thus the point density rather depends on the shape of the surface but less on the quality of the model. Another approach has been applied by using the points resulting from Poisson Surface Reconstructions. One of this algorithm's properties is the filling of holes: new points are interpolated inside the holes. Using the original 3D point cloud and the interpolated Poisson point set, two analyses have been performed: For all Poisson points, the distance to the closest original point cloud member has been calculated. For the resulting set of distances, histograms have been produced that show the distribution of point distances. As the Poisson points also make up a connected mesh, the size and distribution of single holes can also be estimated by labeling Poisson points that belong to the same hole: each hole gets a specific number. Afterwards, the area of the mesh formed by each set of Poisson hole points can be calculated. The result is a set of distinctive holes and their sizes. The two approaches showed that the hole-ness of the point cloud depends on the soil moisture respectively the reflectivity: the distance distribution of the model of the saturated soil shows the smallest number of large distances. The histogram of the medium state shows more large distances and the dry model shows the largest distances. Models resulting from indirect lighting are better than the models resulting from direct light for all moisture states.

Effect of target color and scanning geometry on terrestrial LiDAR point-cloud noise and plane fitting

NASA Astrophysics Data System (ADS)

Bolkas, Dimitrios; Martinez, Aaron

2018-01-01

Point-cloud coordinate information derived from terrestrial Light Detection And Ranging (LiDAR) is important for several applications in surveying and civil engineering. Plane fitting and segmentation of target-surfaces is an important step in several applications such as in the monitoring of structures. Reliable parametric modeling and segmentation relies on the underlying quality of the point-cloud. Therefore, understanding how point-cloud errors affect fitting of planes and segmentation is important. Point-cloud intensity, which accompanies the point-cloud data, often goes hand-in-hand with point-cloud noise. This study uses industrial particle boards painted with eight different colors (black, white, grey, red, green, blue, brown, and yellow) and two different sheens (flat and semi-gloss) to explore how noise and plane residuals vary with scanning geometry (i.e., distance and incidence angle) and target-color. Results show that darker colors, such as black and brown, can produce point clouds that are several times noisier than bright targets, such as white. In addition, semi-gloss targets manage to reduce noise in dark targets by about 2-3 times. The study of plane residuals with scanning geometry reveals that, in many of the cases tested, residuals decrease with increasing incidence angles, which can assist in understanding the distribution of plane residuals in a dataset. Finally, a scheme is developed to derive survey guidelines based on the data collected in this experiment. Three examples demonstrate that users should consider instrument specification, required precision of plane residuals, required point-spacing, target-color, and target-sheen, when selecting scanning locations. Outcomes of this study can aid users to select appropriate instrumentation and improve planning of terrestrial LiDAR data-acquisition.

From Laser Scanning to Finite Element Analysis of Complex Buildings by Using a Semi-Automatic Procedure

PubMed Central

Castellazzi, Giovanni; D’Altri, Antonio Maria; Bitelli, Gabriele; Selvaggi, Ilenia; Lambertini, Alessandro

2015-01-01

In this paper, a new semi-automatic procedure to transform three-dimensional point clouds of complex objects to three-dimensional finite element models is presented and validated. The procedure conceives of the point cloud as a stacking of point sections. The complexity of the clouds is arbitrary, since the procedure is designed for terrestrial laser scanner surveys applied to buildings with irregular geometry, such as historical buildings. The procedure aims at solving the problems connected to the generation of finite element models of these complex structures by constructing a fine discretized geometry with a reduced amount of time and ready to be used with structural analysis. If the starting clouds represent the inner and outer surfaces of the structure, the resulting finite element model will accurately capture the whole three-dimensional structure, producing a complex solid made by voxel elements. A comparison analysis with a CAD-based model is carried out on a historical building damaged by a seismic event. The results indicate that the proposed procedure is effective and obtains comparable models in a shorter time, with an increased level of automation. PMID:26225978

Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game

NASA Astrophysics Data System (ADS)

Zai, Dawei; Li, Jonathan; Guo, Yulan; Cheng, Ming; Huang, Pengdi; Cao, Xiaofei; Wang, Cheng

2017-12-01

It is challenging to automatically register TLS point clouds with noise, outliers and varying overlap. In this paper, we propose a new method for pairwise registration of TLS point clouds. We first generate covariance matrix descriptors with an adaptive neighborhood size from point clouds to find candidate correspondences, we then construct a non-cooperative game to isolate mutual compatible correspondences, which are considered as true positives. The method was tested on three models acquired by two different TLS systems. Experimental results demonstrate that our proposed adaptive covariance (ACOV) descriptor is invariant to rigid transformation and robust to noise and varying resolutions. The average registration errors achieved on three models are 0.46 cm, 0.32 cm and 1.73 cm, respectively. The computational times cost on these models are about 288 s, 184 s and 903 s, respectively. Besides, our registration framework using ACOV descriptors and a game theoretic method is superior to the state-of-the-art methods in terms of both registration error and computational time. The experiment on a large outdoor scene further demonstrates the feasibility and effectiveness of our proposed pairwise registration framework.

Photogrammetric DSM denoising

NASA Astrophysics Data System (ADS)

Nex, F.; Gerke, M.

2014-08-01

Image matching techniques can nowadays provide very dense point clouds and they are often considered a valid alternative to LiDAR point cloud. However, photogrammetric point clouds are often characterized by a higher level of random noise compared to LiDAR data and by the presence of large outliers. These problems constitute a limitation in the practical use of photogrammetric data for many applications but an effective way to enhance the generated point cloud has still to be found. In this paper we concentrate on the restoration of Digital Surface Models (DSM), computed from dense image matching point clouds. A photogrammetric DSM, i.e. a 2.5D representation of the surface is still one of the major products derived from point clouds. Four different algorithms devoted to DSM denoising are presented: a standard median filter approach, a bilateral filter, a variational approach (TGV: Total Generalized Variation), as well as a newly developed algorithm, which is embedded into a Markov Random Field (MRF) framework and optimized through graph-cuts. The ability of each algorithm to recover the original DSM has been quantitatively evaluated. To do that, a synthetic DSM has been generated and different typologies of noise have been added to mimic the typical errors of photogrammetric DSMs. The evaluation reveals that standard filters like median and edge preserving smoothing through a bilateral filter approach cannot sufficiently remove typical errors occurring in a photogrammetric DSM. The TGV-based approach much better removes random noise, but large areas with outliers still remain. Our own method which explicitly models the degradation properties of those DSM outperforms the others in all aspects.

A New Approach for Inspection of Selected Geometric Parameters of a Railway Track Using Image-Based Point Clouds

PubMed Central

Sawicki, Piotr

2018-01-01

The paper presents the results of testing a proposed image-based point clouds measuring method for geometric parameters determination of a railway track. The study was performed based on a configuration of digital images and reference control network. A DSLR (digital Single-Lens-Reflex) Nikon D5100 camera was used to acquire six digital images of the tested section of railway tracks. The dense point clouds and the 3D mesh model were generated with the use of two software systems, RealityCapture and PhotoScan, which have implemented different matching and 3D object reconstruction techniques: Multi-View Stereo and Semi-Global Matching, respectively. The study found that both applications could generate appropriate 3D models. Final meshes of 3D models were filtered with the MeshLab software. The CloudCompare application was used to determine the track gauge and cant for defined cross-sections, and the results obtained from point clouds by dense image matching techniques were compared with results of direct geodetic measurements. The obtained RMS difference in the horizontal (gauge) and vertical (cant) plane was RMS∆ < 0.45 mm. The achieved accuracy meets the accuracy condition of measurements and inspection of the rail tracks (error m < 1 mm), specified in the Polish branch railway instruction Id-14 (D-75) and the European technical norm EN 13848-4:2011. PMID:29509679

A New Approach for Inspection of Selected Geometric Parameters of a Railway Track Using Image-Based Point Clouds.

PubMed

Gabara, Grzegorz; Sawicki, Piotr

2018-03-06

The paper presents the results of testing a proposed image-based point clouds measuring method for geometric parameters determination of a railway track. The study was performed based on a configuration of digital images and reference control network. A DSLR (digital Single-Lens-Reflex) Nikon D5100 camera was used to acquire six digital images of the tested section of railway tracks. The dense point clouds and the 3D mesh model were generated with the use of two software systems, RealityCapture and PhotoScan, which have implemented different matching and 3D object reconstruction techniques: Multi-View Stereo and Semi-Global Matching, respectively. The study found that both applications could generate appropriate 3D models. Final meshes of 3D models were filtered with the MeshLab software. The CloudCompare application was used to determine the track gauge and cant for defined cross-sections, and the results obtained from point clouds by dense image matching techniques were compared with results of direct geodetic measurements. The obtained RMS difference in the horizontal (gauge) and vertical (cant) plane was RMS∆ < 0.45 mm. The achieved accuracy meets the accuracy condition of measurements and inspection of the rail tracks (error m < 1 mm), specified in the Polish branch railway instruction Id-14 (D-75) and the European technical norm EN 13848-4:2011.

Accuracy Analysis of a Dam Model from Drone Surveys

PubMed Central

Buffi, Giulia; Venturi, Sara

2017-01-01

This paper investigates the accuracy of models obtained by drone surveys. To this end, this work analyzes how the placement of ground control points (GCPs) used to georeference the dense point cloud of a dam affects the resulting three-dimensional (3D) model. Images of a double arch masonry dam upstream face are acquired from drone survey and used to build the 3D model of the dam for vulnerability analysis purposes. However, there still remained the issue of understanding the real impact of a correct GCPs location choice to properly georeference the images and thus, the model. To this end, a high number of GCPs configurations were investigated, building a series of dense point clouds. The accuracy of these resulting dense clouds was estimated comparing the coordinates of check points extracted from the model and their true coordinates measured via traditional topography. The paper aims at providing information about the optimal choice of GCPs placement not only for dams but also for all surveys of high-rise structures. The knowledge a priori of the effect of the GCPs number and location on the model accuracy can increase survey reliability and accuracy and speed up the survey set-up operations. PMID:28771185

Accuracy Analysis of a Dam Model from Drone Surveys.

PubMed

Ridolfi, Elena; Buffi, Giulia; Venturi, Sara; Manciola, Piergiorgio

2017-08-03

This paper investigates the accuracy of models obtained by drone surveys. To this end, this work analyzes how the placement of ground control points (GCPs) used to georeference the dense point cloud of a dam affects the resulting three-dimensional (3D) model. Images of a double arch masonry dam upstream face are acquired from drone survey and used to build the 3D model of the dam for vulnerability analysis purposes. However, there still remained the issue of understanding the real impact of a correct GCPs location choice to properly georeference the images and thus, the model. To this end, a high number of GCPs configurations were investigated, building a series of dense point clouds. The accuracy of these resulting dense clouds was estimated comparing the coordinates of check points extracted from the model and their true coordinates measured via traditional topography. The paper aims at providing information about the optimal choice of GCPs placement not only for dams but also for all surveys of high-rise structures. The knowledge a priori of the effect of the GCPs number and location on the model accuracy can increase survey reliability and accuracy and speed up the survey set-up operations.

Vertical Optical Scanning with Panoramic Vision for Tree Trunk Reconstruction

PubMed Central

Berveglieri, Adilson; Liang, Xinlian; Honkavaara, Eija

2017-01-01

This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras. PMID:29207468

Vertical Optical Scanning with Panoramic Vision for Tree Trunk Reconstruction.

PubMed

Berveglieri, Adilson; Tommaselli, Antonio M G; Liang, Xinlian; Honkavaara, Eija

2017-12-02

This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras.

Structure Line Detection from LIDAR Point Clouds Using Topological Elevation Analysis

NASA Astrophysics Data System (ADS)

Lo, C. Y.; Chen, L. C.

2012-07-01

Airborne LIDAR point clouds, which have considerable points on object surfaces, are essential to building modeling. In the last two decades, studies have developed different approaches to identify structure lines using two main approaches, data-driven and modeldriven. These studies have shown that automatic modeling processes depend on certain considerations, such as used thresholds, initial value, designed formulas, and predefined cues. Following the development of laser scanning systems, scanning rates have increased and can provide point clouds with higher point density. Therefore, this study proposes using topological elevation analysis (TEA) to detect structure lines instead of threshold-dependent concepts and predefined constraints. This analysis contains two parts: data pre-processing and structure line detection. To preserve the original elevation information, a pseudo-grid for generating digital surface models is produced during the first part. The highest point in each grid is set as the elevation value, and its original threedimensional position is preserved. In the second part, using TEA, the structure lines are identified based on the topology of local elevation changes in two directions. Because structure lines can contain certain geometric properties, their locations have small relieves in the radial direction and steep elevation changes in the circular direction. Following the proposed approach, TEA can be used to determine 3D line information without selecting thresholds. For validation, the TEA results are compared with those of the region growing approach. The results indicate that the proposed method can produce structure lines using dense point clouds.

Alternative Methods for Estimating Plane Parameters Based on a Point Cloud

NASA Astrophysics Data System (ADS)

Stryczek, Roman

2017-12-01

Non-contact measurement techniques carried out using triangulation optical sensors are increasingly popular in measurements with the use of industrial robots directly on production lines. The result of such measurements is often a cloud of measurement points that is characterized by considerable measuring noise, presence of a number of points that differ from the reference model, and excessive errors that must be eliminated from the analysis. To obtain vector information points contained in the cloud that describe reference models, the data obtained during a measurement should be subjected to appropriate processing operations. The present paperwork presents an analysis of suitability of methods known as RANdom Sample Consensus (RANSAC), Monte Carlo Method (MCM), and Particle Swarm Optimization (PSO) for the extraction of the reference model. The effectiveness of the tested methods is illustrated by examples of measurement of the height of an object and the angle of a plane, which were made on the basis of experiments carried out at workshop conditions.

Cloud point phenomena for POE-type nonionic surfactants in a model room temperature ionic liquid.

PubMed

Inoue, Tohru; Misono, Takeshi

2008-10-15

The cloud point phenomenon has been investigated for the solutions of polyoxyethylene (POE)-type nonionic surfactants (C(12)E(5), C(12)E(6), C(12)E(7), C(10)E(6), and C(14)E(6)) in 1-butyl-3-methylimidazolium tetrafluoroborate (bmimBF(4)), a typical room temperature ionic liquid (RTIL). The cloud point, T(c), increases with the elongation of the POE chain, while decreases with the increase in the hydrocarbon chain length. This demonstrates that the solvophilicity/solvophobicity of the surfactants in RTIL comes from POE chain/hydrocarbon chain. When compared with an aqueous system, the chain length dependence of T(c) is larger for the RTIL system regarding both POE and hydrocarbon chains; in particular, hydrocarbon chain length affects T(c) much more strongly in the RTIL system than in equivalent aqueous systems. In a similar fashion to the much-studied aqueous systems, the micellar growth is also observed in this RTIL solvent as the temperature approaches T(c). The cloud point curves have been analyzed using a Flory-Huggins-type model based on phase separation in polymer solutions.

Classification of Aerial Photogrammetric 3d Point Clouds

NASA Astrophysics Data System (ADS)

Becker, C.; Häni, N.; Rosinskaya, E.; d'Angelo, E.; Strecha, C.

2017-05-01

We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

Large-scale urban point cloud labeling and reconstruction

NASA Astrophysics Data System (ADS)

Zhang, Liqiang; Li, Zhuqiang; Li, Anjian; Liu, Fangyu

2018-04-01

The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. In this paper, a novel framework is proposed for classification and reconstruction of airborne laser scanning point cloud data. To label point clouds, we present a rectified linear units neural network named ReLu-NN where the rectified linear units (ReLu) instead of the traditional sigmoid are taken as the activation function in order to speed up the convergence. Since the features of the point cloud are sparse, we reduce the number of neurons by the dropout to avoid over-fitting of the training process. The set of feature descriptors for each 3D point is encoded through self-taught learning, and forms a discriminative feature representation which is taken as the input of the ReLu-NN. The segmented building points are consolidated through an edge-aware point set resampling algorithm, and then they are reconstructed into 3D lightweight models using the 2.5D contouring method (Zhou and Neumann, 2010). Compared with deep learning approaches, the ReLu-NN introduced can easily classify unorganized point clouds without rasterizing the data, and it does not need a large number of training samples. Most of the parameters in the network are learned, and thus the intensive parameter tuning cost is significantly reduced. Experimental results on various datasets demonstrate that the proposed framework achieves better performance than other related algorithms in terms of classification accuracy and reconstruction quality.

Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

NASA Astrophysics Data System (ADS)

Sirmacek, B.; Lindenbergh, R. C.; Menenti, M.

2013-10-01

Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

Supervised Outlier Detection in Large-Scale Mvs Point Clouds for 3d City Modeling Applications

NASA Astrophysics Data System (ADS)

Stucker, C.; Richard, A.; Wegner, J. D.; Schindler, K.

2018-05-01

We propose to use a discriminative classifier for outlier detection in large-scale point clouds of cities generated via multi-view stereo (MVS) from densely acquired images. What makes outlier removal hard are varying distributions of inliers and outliers across a scene. Heuristic outlier removal using a specific feature that encodes point distribution often delivers unsatisfying results. Although most outliers can be identified correctly (high recall), many inliers are erroneously removed (low precision), too. This aggravates object 3D reconstruction due to missing data. We thus propose to discriminatively learn class-specific distributions directly from the data to achieve high precision. We apply a standard Random Forest classifier that infers a binary label (inlier or outlier) for each 3D point in the raw, unfiltered point cloud and test two approaches for training. In the first, non-semantic approach, features are extracted without considering the semantic interpretation of the 3D points. The trained model approximates the average distribution of inliers and outliers across all semantic classes. Second, semantic interpretation is incorporated into the learning process, i.e. we train separate inlieroutlier classifiers per semantic class (building facades, roof, ground, vegetation, fields, and water). Performance of learned filtering is evaluated on several large SfM point clouds of cities. We find that results confirm our underlying assumption that discriminatively learning inlier-outlier distributions does improve precision over global heuristics by up to ≍ 12 percent points. Moreover, semantically informed filtering that models class-specific distributions further improves precision by up to ≍ 10 percent points, being able to remove very isolated building, roof, and water points while preserving inliers on building facades and vegetation.

Analysis of Uncertainty in a Middle-Cost Device for 3D Measurements in BIM Perspective

PubMed Central

Sánchez, Alonso; Naranjo, José-Manuel; Jiménez, Antonio; González, Alfonso

2016-01-01

Medium-cost devices equipped with sensors are being developed to get 3D measurements. Some allow for generating geometric models and point clouds. Nevertheless, the accuracy of these measurements should be evaluated, taking into account the requirements of the Building Information Model (BIM). This paper analyzes the uncertainty in outdoor/indoor three-dimensional coordinate measures and point clouds (using Spherical Accuracy Standard (SAS) methods) for Eyes Map, a medium-cost tablet manufactured by e-Capture Research & Development Company, Mérida, Spain. To achieve it, in outdoor tests, by means of this device, the coordinates of targets were measured from 1 to 6 m and cloud points were obtained. Subsequently, these were compared to the coordinates of the same targets measured by a Total Station. The Euclidean average distance error was 0.005–0.027 m for measurements by Photogrammetry and 0.013–0.021 m for the point clouds. All of them satisfy the tolerance for point cloud acquisition (0.051 m) according to the BIM Guide for 3D Imaging (General Services Administration); similar results are obtained in the indoor tests, with values of 0.022 m. In this paper, we establish the optimal distances for the observations in both, Photogrammetry and 3D Photomodeling modes (outdoor) and point out some working conditions to avoid in indoor environments. Finally, the authors discuss some recommendations for improving the performance and working methods of the device. PMID:27669245

Impact of survey workflow on precision and accuracy of terrestrial LiDAR datasets

NASA Astrophysics Data System (ADS)

Gold, P. O.; Cowgill, E.; Kreylos, O.

2009-12-01

Ground-based LiDAR (Light Detection and Ranging) survey techniques are enabling remote visualization and quantitative analysis of geologic features at unprecedented levels of detail. For example, digital terrain models computed from LiDAR data have been used to measure displaced landforms along active faults and to quantify fault-surface roughness. But how accurately do terrestrial LiDAR data represent the true ground surface, and in particular, how internally consistent and precise are the mosaiced LiDAR datasets from which surface models are constructed? Addressing this question is essential for designing survey workflows that capture the necessary level of accuracy for a given project while minimizing survey time and equipment, which is essential for effective surveying of remote sites. To address this problem, we seek to define a metric that quantifies how scan registration error changes as a function of survey workflow. Specifically, we are using a Trimble GX3D laser scanner to conduct a series of experimental surveys to quantify how common variables in field workflows impact the precision of scan registration. Primary variables we are testing include 1) use of an independently measured network of control points to locate scanner and target positions, 2) the number of known-point locations used to place the scanner and point clouds in 3-D space, 3) the type of target used to measure distances between the scanner and the known points, and 4) setting up the scanner over a known point as opposed to resectioning of known points. Precision of the registered point cloud is quantified using Trimble Realworks software by automatic calculation of registration errors (errors between locations of the same known points in different scans). Accuracy of the registered cloud (i.e., its ground-truth) will be measured in subsequent experiments. To obtain an independent measure of scan-registration errors and to better visualize the effects of these errors on a registered point cloud, we scan from multiple locations an object of known geometry (a cylinder mounted above a square box). Preliminary results show that even in a controlled experimental scan of an object of known dimensions, there is significant variability in the precision of the registered point cloud. For example, when 3 scans of the central object are registered using 4 known points (maximum time, maximum equipment), the point clouds align to within ~1 cm (normal to the object surface). However, when the same point clouds are registered with only 1 known point (minimum time, minimum equipment), misalignment of the point clouds can range from 2.5 to 5 cm, depending on target type. The greater misalignment of the 3 point clouds when registered with fewer known points stems from the field method employed in acquiring the dataset and demonstrates the impact of field workflow on LiDAR dataset precision. By quantifying the degree of scan mismatch in results such as this, we can provide users with the information needed to maximize efficiency in remote field surveys.

Determination of Large-Scale Cloud Ice Water Concentration by Combining Surface Radar and Satellite Data in Support of ARM SCM Activities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Guosheng

2013-03-15

Single-column modeling (SCM) is one of the key elements of Atmospheric Radiation Measurement (ARM) research initiatives for the development and testing of various physical parameterizations to be used in general circulation models (GCMs). The data required for use with an SCM include observed vertical profiles of temperature, water vapor, and condensed water, as well as the large-scale vertical motion and tendencies of temperature, water vapor, and condensed water due to horizontal advection. Surface-based measurements operated at ARM sites and upper-air sounding networks supply most of the required variables for model inputs, but do not provide the horizontal advection term ofmore » condensed water. Since surface cloud radar and microwave radiometer observations at ARM sites are single-point measurements, they can provide the amount of condensed water at the location of observation sites, but not a horizontal distribution of condensed water contents. Consequently, observational data for the large-scale advection tendencies of condensed water have not been available to the ARM cloud modeling community based on surface observations alone. This lack of advection data of water condensate could cause large uncertainties in SCM simulations. Additionally, to evaluate GCMs cloud physical parameterization, we need to compare GCM results with observed cloud water amounts over a scale that is large enough to be comparable to what a GCM grid represents. To this end, the point-measurements at ARM surface sites are again not adequate. Therefore, cloud water observations over a large area are needed. The main goal of this project is to retrieve ice water contents over an area of 10 x 10 deg. surrounding the ARM sites by combining surface and satellite observations. Built on the progress made during previous ARM research, we have conducted the retrievals of 3-dimensional ice water content by combining surface radar/radiometer and satellite measurements, and have produced 3-D cloud ice water contents in support of cloud modeling activities. The approach of the study is to expand a (surface) point measurement to an (satellite) area measurement. That is, the study takes the advantage of the high quality cloud measurements (particularly cloud radar and microwave radiometer measurements) at the point of the ARM sites. We use the cloud ice water characteristics derived from the point measurement to guide/constrain a satellite retrieval algorithm, then use the satellite algorithm to derive the 3-D cloud ice water distributions within an 10° (latitude) x 10° (longitude) area. During the research period, we have developed, validated and improved our cloud ice water retrievals, and have produced and archived at ARM website as a PI-product of the 3-D cloud ice water contents using combined satellite high-frequency microwave and surface radar observations for SGP March 2000 IOP and TWP-ICE 2006 IOP over 10 deg. x 10 deg. area centered at ARM SGP central facility and Darwin sites. We have also worked on validation of the 3-D ice water product by CloudSat data, synergy with visible/infrared cloud ice water retrievals for better results at low ice water conditions, and created a long-term (several years) of ice water climatology in 10 x 10 deg. area of ARM SGP and TWP sites and then compared it with GCMs.« less

An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Hu, Pingbo; Scherer, Sebastian

2018-03-01

Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., http://SEMANTIC3D.NET)

Real-time terrain storage generation from multiple sensors towards mobile robot operation interface.

PubMed

Song, Wei; Cho, Seoungjae; Xi, Yulong; Cho, Kyungeun; Um, Kyhyun

2014-01-01

A mobile robot mounted with multiple sensors is used to rapidly collect 3D point clouds and video images so as to allow accurate terrain modeling. In this study, we develop a real-time terrain storage generation and representation system including a nonground point database (PDB), ground mesh database (MDB), and texture database (TDB). A voxel-based flag map is proposed for incrementally registering large-scale point clouds in a terrain model in real time. We quantize the 3D point clouds into 3D grids of the flag map as a comparative table in order to remove the redundant points. We integrate the large-scale 3D point clouds into a nonground PDB and a node-based terrain mesh using the CPU. Subsequently, we program a graphics processing unit (GPU) to generate the TDB by mapping the triangles in the terrain mesh onto the captured video images. Finally, we produce a nonground voxel map and a ground textured mesh as a terrain reconstruction result. Our proposed methods were tested in an outdoor environment. Our results show that the proposed system was able to rapidly generate terrain storage and provide high resolution terrain representation for mobile mapping services and a graphical user interface between remote operators and mobile robots.

Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds.

PubMed

Hamraz, Hamid; Contreras, Marco A; Zhang, Jun

2017-07-28

Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis.

Real-Time Terrain Storage Generation from Multiple Sensors towards Mobile Robot Operation Interface

PubMed Central

Cho, Seoungjae; Xi, Yulong; Cho, Kyungeun

2014-01-01

A mobile robot mounted with multiple sensors is used to rapidly collect 3D point clouds and video images so as to allow accurate terrain modeling. In this study, we develop a real-time terrain storage generation and representation system including a nonground point database (PDB), ground mesh database (MDB), and texture database (TDB). A voxel-based flag map is proposed for incrementally registering large-scale point clouds in a terrain model in real time. We quantize the 3D point clouds into 3D grids of the flag map as a comparative table in order to remove the redundant points. We integrate the large-scale 3D point clouds into a nonground PDB and a node-based terrain mesh using the CPU. Subsequently, we program a graphics processing unit (GPU) to generate the TDB by mapping the triangles in the terrain mesh onto the captured video images. Finally, we produce a nonground voxel map and a ground textured mesh as a terrain reconstruction result. Our proposed methods were tested in an outdoor environment. Our results show that the proposed system was able to rapidly generate terrain storage and provide high resolution terrain representation for mobile mapping services and a graphical user interface between remote operators and mobile robots. PMID:25101321

Self-Similar Spin Images for Point Cloud Matching

NASA Astrophysics Data System (ADS)

Pulido, Daniel

The rapid growth of Light Detection And Ranging (Lidar) technologies that collect, process, and disseminate 3D point clouds have allowed for increasingly accurate spatial modeling and analysis of the real world. Lidar sensors can generate massive 3D point clouds of a collection area that provide highly detailed spatial and radiometric information. However, a Lidar collection can be expensive and time consuming. Simultaneously, the growth of crowdsourced Web 2.0 data (e.g., Flickr, OpenStreetMap) have provided researchers with a wealth of freely available data sources that cover a variety of geographic areas. Crowdsourced data can be of varying quality and density. In addition, since it is typically not collected as part of a dedicated experiment but rather volunteered, when and where the data is collected is arbitrary. The integration of these two sources of geoinformation can provide researchers the ability to generate products and derive intelligence that mitigate their respective disadvantages and combine their advantages. Therefore, this research will address the problem of fusing two point clouds from potentially different sources. Specifically, we will consider two problems: scale matching and feature matching. Scale matching consists of computing feature metrics of each point cloud and analyzing their distributions to determine scale differences. Feature matching consists of defining local descriptors that are invariant to common dataset distortions (e.g., rotation and translation). Additionally, after matching the point clouds they can be registered and processed further (e.g., change detection). The objective of this research is to develop novel methods to fuse and enhance two point clouds from potentially disparate sources (e.g., Lidar and crowdsourced Web 2.0 datasets). The scope of this research is to investigate both scale and feature matching between two point clouds. The specific focus of this research will be in developing a novel local descriptor based on the concept of self-similarity to aid in the scale and feature matching steps. An open problem in fusion is how best to extract features from two point clouds and then perform feature-based matching. The proposed approach for this matching step is the use of local self-similarity as an invariant measure to match features. In particular, the proposed approach is to combine the concept of local self-similarity with a well-known feature descriptor, Spin Images, and thereby define "Self-Similar Spin Images". This approach is then extended to the case of matching two points clouds in very different coordinate systems (e.g., a geo-referenced Lidar point cloud and stereo-image derived point cloud without geo-referencing). The use of Self-Similar Spin Images is again applied to address this problem by introducing a "Self-Similar Keyscale" that matches the spatial scales of two point clouds. Another open problem is how best to detect changes in content between two point clouds. A method is proposed to find changes between two point clouds by analyzing the order statistics of the nearest neighbors between the two clouds, and thereby define the "Nearest Neighbor Order Statistic" method. Note that the well-known Hausdorff distance is a special case as being just the maximum order statistic. Therefore, by studying the entire histogram of these nearest neighbors it is expected to yield a more robust method to detect points that are present in one cloud but not the other. This approach is applied at multiple resolutions. Therefore, changes detected at the coarsest level will yield large missing targets and at finer levels will yield smaller targets.

Roughness Estimation from Point Clouds - A Comparison of Terrestrial Laser Scanning and Image Matching by Unmanned Aerial Vehicle Acquisitions

NASA Astrophysics Data System (ADS)

Rutzinger, Martin; Bremer, Magnus; Ragg, Hansjörg

2013-04-01

Recently, terrestrial laser scanning (TLS) and matching of images acquired by unmanned arial vehicles (UAV) are operationally used for 3D geodata acquisition in Geoscience applications. However, the two systems cover different application domains in terms of acquisition conditions and data properties i.e. accuracy and line of sight. In this study we investigate the major differences between the two platforms for terrain roughness estimation. Terrain roughness is an important input for various applications such as morphometry studies, geomorphologic mapping, and natural process modeling (e.g. rockfall, avalanche, and hydraulic modeling). Data has been collected simultaneously by TLS using an Optech ILRIS3D and a rotary UAV using an octocopter from twins.nrn for a 900 m² test site located in a riverbed in Tyrol, Austria (Judenbach, Mieming). The TLS point cloud has been acquired from three scan positions. These have been registered using iterative closest point algorithm and a target-based referencing approach. For registration geometric targets (spheres) with a diameter of 20 cm were used. These targets were measured with dGPS for absolute georeferencing. The TLS point cloud has an average point density of 19,000 pts/m², which represents a point spacing of about 5 mm. 15 images where acquired by UAV in a height of 20 m using a calibrated camera with focal length of 18.3 mm. A 3D point cloud containing RGB attributes was derived using APERO/MICMAC software, by a direct georeferencing approach based on the aircraft IMU data. The point cloud is finally co-registered with the TLS data to guarantee an optimal preparation in order to perform the analysis. The UAV point cloud has an average point density of 17,500 pts/m², which represents a point spacing of 7.5 mm. After registration and georeferencing the level of detail of roughness representation in both point clouds have been compared considering elevation differences, roughness and representation of different grain sizes. UAV closes the gap between aerial and terrestrial surveys in terms of resolution and acquisition flexibility. This is also true for the data accuracy. Considering these data collection and data quality properties of both systems they have their merit on its own in terms of scale, data quality, data collection speed and application.

Quantifying Biomass from Point Clouds by Connecting Representations of Ecosystem Structure

NASA Astrophysics Data System (ADS)

Hendryx, S. M.; Barron-Gafford, G.

2017-12-01

Quantifying terrestrial ecosystem biomass is an essential part of monitoring carbon stocks and fluxes within the global carbon cycle and optimizing natural resource management. Point cloud data such as from lidar and structure from motion can be effective for quantifying biomass over large areas, but significant challenges remain in developing effective models that allow for such predictions. Inference models that estimate biomass from point clouds are established in many environments, yet, are often scale-dependent, needing to be fitted and applied at the same spatial scale and grid size at which they were developed. Furthermore, training such models typically requires large in situ datasets that are often prohibitively costly or time-consuming to obtain. We present here a scale- and sensor-invariant framework for efficiently estimating biomass from point clouds. Central to this framework, we present a new algorithm, assignPointsToExistingClusters, that has been developed for finding matches between in situ data and clusters in remotely-sensed point clouds. The algorithm can be used for assessing canopy segmentation accuracy and for training and validating machine learning models for predicting biophysical variables. We demonstrate the algorithm's efficacy by using it to train a random forest model of above ground biomass in a shrubland environment in Southern Arizona. We show that by learning a nonlinear function to estimate biomass from segmented canopy features we can reduce error, especially in the presence of inaccurate clusterings, when compared to a traditional, deterministic technique to estimate biomass from remotely measured canopies. Our random forest on cluster features model extends established methods of training random forest regressions to predict biomass of subplots but requires significantly less training data and is scale invariant. The random forest on cluster features model reduced mean absolute error, when evaluated on all test data in leave one out cross validation, by 40.6% from deterministic mesquite allometry and 35.9% from the inferred ecosystem-state allometric function. Our framework should allow for the inference of biomass more efficiently than common subplot methods and more accurately than individual tree segmentation methods in densely vegetated environments.

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

DOE PAGES

Thayer-Calder, K.; Gettelman, A.; Craig, C.; ...

2015-06-30

Most global climate models parameterize separate cloud types using separate parameterizations. This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations. Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into amore » microphysics scheme.This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Results describing the mean climate and tropical variability from global simulations are presented. The new model shows a degradation in precipitation skill but improvements in short-wave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. Also presented are estimations of computational expense and investigation of sensitivity to number of subcolumns.« less

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

DOE PAGES

Thayer-Calder, Katherine; Gettelman, A.; Craig, Cheryl; ...

2015-12-01

Most global climate models parameterize separate cloud types using separate parameterizations.This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations. Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into a microphysicsmore » scheme. This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Results describing the mean climate and tropical variability from global simulations are presented. In conclusion, the new model shows a degradation in precipitation skill but improvements in short-wave cloud forcing, liquid water path, long-wave cloud forcing, perceptible water, and tropical wave simulation. Also presented are estimations of computational expense and investigation of sensitivity to number of subcolumns.« less

Metric Scale Calculation for Visual Mapping Algorithms

NASA Astrophysics Data System (ADS)

Hanel, A.; Mitschke, A.; Boerner, R.; Van Opdenbosch, D.; Hoegner, L.; Brodie, D.; Stilla, U.

2018-05-01

Visual SLAM algorithms allow localizing the camera by mapping its environment by a point cloud based on visual cues. To obtain the camera locations in a metric coordinate system, the metric scale of the point cloud has to be known. This contribution describes a method to calculate the metric scale for a point cloud of an indoor environment, like a parking garage, by fusing multiple individual scale values. The individual scale values are calculated from structures and objects with a-priori known metric extension, which can be identified in the unscaled point cloud. Extensions of building structures, like the driving lane or the room height, are derived from density peaks in the point distribution. The extension of objects, like traffic signs with a known metric size, are derived using projections of their detections in images onto the point cloud. The method is tested with synthetic image sequences of a drive with a front-looking mono camera through a virtual 3D model of a parking garage. It has been shown, that each individual scale value improves either the robustness of the fused scale value or reduces its error. The error of the fused scale is comparable to other recent works.

Indoor Navigation from Point Clouds: 3d Modelling and Obstacle Detection

NASA Astrophysics Data System (ADS)

Díaz-Vilariño, L.; Boguslawski, P.; Khoshelham, K.; Lorenzo, H.; Mahdjoubi, L.

2016-06-01

In the recent years, indoor modelling and navigation has become a research of interest because many stakeholders require navigation assistance in various application scenarios. The navigational assistance for blind or wheelchair people, building crisis management such as fire protection, augmented reality for gaming, tourism or training emergency assistance units are just some of the direct applications of indoor modelling and navigation. Navigational information is traditionally extracted from 2D drawings or layouts. Real state of indoors, including opening position and geometry for both windows and doors, and the presence of obstacles is commonly ignored. In this work, a real indoor-path planning methodology based on 3D point clouds is developed. The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.

A building extraction approach for Airborne Laser Scanner data utilizing the Object Based Image Analysis paradigm

NASA Astrophysics Data System (ADS)

Tomljenovic, Ivan; Tiede, Dirk; Blaschke, Thomas

2016-10-01

In the past two decades Object-Based Image Analysis (OBIA) established itself as an efficient approach for the classification and extraction of information from remote sensing imagery and, increasingly, from non-image based sources such as Airborne Laser Scanner (ALS) point clouds. ALS data is represented in the form of a point cloud with recorded multiple returns and intensities. In our work, we combined OBIA with ALS point cloud data in order to identify and extract buildings as 2D polygons representing roof outlines in a top down mapping approach. We performed rasterization of the ALS data into a height raster for the purpose of the generation of a Digital Surface Model (DSM) and a derived Digital Elevation Model (DEM). Further objects were generated in conjunction with point statistics from the linked point cloud. With the use of class modelling methods, we generated the final target class of objects representing buildings. The approach was developed for a test area in Biberach an der Riß (Germany). In order to point out the possibilities of the adaptation-free transferability to another data set, the algorithm has been applied ;as is; to the ISPRS Benchmarking data set of Toronto (Canada). The obtained results show high accuracies for the initial study area (thematic accuracies of around 98%, geometric accuracy of above 80%). The very high performance within the ISPRS Benchmark without any modification of the algorithm and without any adaptation of parameters is particularly noteworthy.

Assimilation of Satellite Data in Regional Air Quality Models

NASA Technical Reports Server (NTRS)

Mcnider, Richard T.; Norris, William B.; Casey, Daniel; Pleim, Jonathan E.; Roselle, Shawn J.; Lapenta, William M.

1997-01-01

In terms of important uncertainty in regional-scale air-pollution models, probably no other aspect ranks any higher than the current ability to specify clouds and soil moisture on the regional scale. Because clouds in models are highly parameterized, the ability of models to predict the correct spatial and radiative characteristics is highly suspect and subject to large error. The poor representation of cloud fields from point measurements at National Weather Services stations and the almost total absence of surface moisture availability observations has made assimilation of these variables difficult to impossible. Yet, the correct inclusion of clouds and surface moisture are of first-order importance in regional-scale photochemistry.

Performance testing of 3D point cloud software

NASA Astrophysics Data System (ADS)

Varela-González, M.; González-Jorge, H.; Riveiro, B.; Arias, P.

2013-10-01

LiDAR systems are being used widely in recent years for many applications in the engineering field: civil engineering, cultural heritage, mining, industry and environmental engineering. One of the most important limitations of this technology is the large computational requirements involved in data processing, especially for large mobile LiDAR datasets. Several software solutions for data managing are available in the market, including open source suites, however, users often unknown methodologies to verify their performance properly. In this work a methodology for LiDAR software performance testing is presented and four different suites are studied: QT Modeler, VR Mesh, AutoCAD 3D Civil and the Point Cloud Library running in software developed at the University of Vigo (SITEGI). The software based on the Point Cloud Library shows better results in the loading time of the point clouds and CPU usage. However, it is not as strong as commercial suites in working set and commit size tests.

Automated Detection and Closing of Holes in Aerial Point Clouds Using AN Uas

NASA Astrophysics Data System (ADS)

Fiolka, T.; Rouatbi, F.; Bender, D.

2017-08-01

3D terrain models are an important instrument in areas like geology, agriculture and reconnaissance. Using an automated UAS with a line-based LiDAR can create terrain models fast and easily even from large areas. But the resulting point cloud may contain holes and therefore be incomplete. This might happen due to occlusions, a missed flight route due to wind or simply as a result of changes in the ground height which would alter the swath of the LiDAR system. This paper proposes a method to detect holes in 3D point clouds generated during the flight and adjust the course in order to close them. First, a grid-based search for holes in the horizontal ground plane is performed. Then a check for vertical holes mainly created by buildings walls is done. Due to occlusions and steep LiDAR angles, closing the vertical gaps may be difficult or even impossible. Therefore, the current approach deals with holes in the ground plane and only marks the vertical holes in such a way that the operator can decide on further actions regarding them. The aim is to efficiently create point clouds which can be used for the generation of complete 3D terrain models.

Big Geo Data Services: From More Bytes to More Barrels

NASA Astrophysics Data System (ADS)

Misev, Dimitar; Baumann, Peter

2016-04-01

The data deluge is affecting the oil and gas industry just as much as many other industries. However, aside from the sheer volume there is the challenge of data variety, such as regular and irregular grids, multi-dimensional space/time grids, point clouds, and TINs and other meshes. A uniform conceptualization for modelling and serving them could save substantial effort, such as the proverbial "department of reformatting". The notion of a coverage actually can accomplish this. Its abstract model in ISO 19123 together with the concrete, interoperable OGC Coverage Implementation Schema (CIS), which is currently under adoption as ISO 19123-2, provieds a common platform for representing any n-D grid type, point clouds, and general meshes. This is paired by the OGC Web Coverage Service (WCS) together with its datacube analytics language, the OGC Web Coverage Processing Service (WCPS). The OGC WCS Core Reference Implementation, rasdaman, relies on Array Database technology, i.e. a NewSQL/NoSQL approach. It supports the grid part of coverages, with installations of 100+ TB known and single queries parallelized across 1,000+ cloud nodes. Recent research attempts to address the point cloud and mesh part through a unified query model. The Holy Grail envisioned is that these approaches can be merged into a single service interface at some time. We present both grid amd point cloud / mesh approaches and discuss status, implementation, standardization, and research perspectives, including a live demo.

3D change detection in staggered voxels model for robotic sensing and navigation

NASA Astrophysics Data System (ADS)

Liu, Ruixu; Hampshire, Brandon; Asari, Vijayan K.

2016-05-01

3D scene change detection is a challenging problem in robotic sensing and navigation. There are several unpredictable aspects in performing scene change detection. A change detection method which can support various applications in varying environmental conditions is proposed. Point cloud models are acquired from a RGB-D sensor, which provides the required color and depth information. Change detection is performed on robot view point cloud model. A bilateral filter smooths the surface and fills the holes as well as keeps the edge details on depth image. Registration of the point cloud model is implemented by using Random Sample Consensus (RANSAC) algorithm. It uses surface normal as the previous stage for the ground and wall estimate. After preprocessing the data, we create a point voxel model which defines voxel as surface or free space. Then we create a color model which defines each voxel that has a color by the mean of all points' color value in this voxel. The preliminary change detection is detected by XOR subtract on the point voxel model. Next, the eight neighbors for this center voxel are defined. If they are neither all `changed' voxels nor all `no changed' voxels, a histogram of location and hue channel color is estimated. The experimental evaluations performed to evaluate the capability of our algorithm show promising results for novel change detection that indicate all the changing objects with very limited false alarm rate.

Automatic Reconstruction of 3D Building Models from Terrestrial Laser Scanner Data

NASA Astrophysics Data System (ADS)

El Meouche, R.; Rezoug, M.; Hijazi, I.; Maes, D.

2013-11-01

With modern 3D laser scanners we can acquire a large amount of 3D data in only a few minutes. This technology results in a growing number of applications ranging from the digitalization of historical artifacts to facial authentication. The modeling process demands a lot of time and work (Tim Volodine, 2007). In comparison with the other two stages, the acquisition and the registration, the degree of automation of the modeling stage is almost zero. In this paper, we propose a new surface reconstruction technique for buildings to process the data obtained by a 3D laser scanner. These data are called a point cloud which is a collection of points sampled from the surface of a 3D object. Such a point cloud can consist of millions of points. In order to work more efficiently, we worked with simplified models which contain less points and so less details than a point cloud obtained in situ. The goal of this study was to facilitate the modeling process of a building starting from 3D laser scanner data. In order to do this, we wrote two scripts for Rhinoceros 5.0 based on intelligent algorithms. The first script finds the exterior outline of a building. With a minimum of human interaction, there is a thin box drawn around the surface of a wall. This box is able to rotate 360° around an axis in a corner of the wall in search for the points of other walls. In this way we can eliminate noise points. These are unwanted or irrelevant points. If there is an angled roof, the box can also turn around the edge of the wall and the roof. With the different positions of the box we can calculate the exterior outline. The second script draws the interior outline in a surface of a building. By interior outline we mean the outline of the openings like windows or doors. This script is based on the distances between the points and vector characteristics. Two consecutive points with a relative big distance will form the outline of an opening. Once those points are found, the interior outline can be drawn. The designed scripts are able to ensure for simple point clouds: the elimination of almost all noise points and the reconstruction of a CAD model.

Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5

NASA Astrophysics Data System (ADS)

Ogura, Tomoo; Shiogama, Hideo; Watanabe, Masahiro; Yoshimori, Masakazu; Yokohata, Tokuta; Annan, James D.; Hargreaves, Julia C.; Ushigami, Naoto; Hirota, Kazuya; Someya, Yu; Kamae, Youichi; Tatebe, Hiroaki; Kimoto, Masahide

2017-12-01

This study discusses how much of the biases in top-of-atmosphere (TOA) radiation and clouds can be removed by parameter tuning in the present-day simulation of a climate model in the Coupled Model Inter-comparison Project phase 5 (CMIP5) generation. We used output of a perturbed parameter ensemble (PPE) experiment conducted with an atmosphere-ocean general circulation model (AOGCM) without flux adjustment. The Model for Interdisciplinary Research on Climate version 5 (MIROC5) was used for the PPE experiment. Output of the PPE was compared with satellite observation data to evaluate the model biases and the parametric uncertainty of the biases with respect to TOA radiation and clouds. The results indicate that removing or changing the sign of the biases by parameter tuning alone is difficult. In particular, the cooling bias of the shortwave cloud radiative effect at low latitudes could not be removed, neither in the zonal mean nor at each latitude-longitude grid point. The bias was related to the overestimation of both cloud amount and cloud optical thickness, which could not be removed by the parameter tuning either. However, they could be alleviated by tuning parameters such as the maximum cumulus updraft velocity at the cloud base. On the other hand, the bias of the shortwave cloud radiative effect in the Arctic was sensitive to parameter tuning. It could be removed by tuning such parameters as albedo of ice and snow both in the zonal mean and at each grid point. The obtained results illustrate the benefit of PPE experiments which provide useful information regarding effectiveness and limitations of parameter tuning. Implementing a shallow convection parameterization is suggested as a potential measure to alleviate the biases in radiation and clouds.

Assessing the performance of aerial image point cloud and spectral metrics in predicting boreal forest canopy cover

NASA Astrophysics Data System (ADS)

Melin, M.; Korhonen, L.; Kukkonen, M.; Packalen, P.

2017-07-01

Canopy cover (CC) is a variable used to describe the status of forests and forested habitats, but also the variable used primarily to define what counts as a forest. The estimation of CC has relied heavily on remote sensing with past studies focusing on satellite imagery as well as Airborne Laser Scanning (ALS) using light detection and ranging (lidar). Of these, ALS has been proven highly accurate, because the fraction of pulses penetrating the canopy represents a direct measurement of canopy gap percentage. However, the methods of photogrammetry can be applied to produce point clouds fairly similar to airborne lidar data from aerial images. Currently there is little information about how well such point clouds measure canopy density and gaps. The aim of this study was to assess the suitability of aerial image point clouds for CC estimation and compare the results with those obtained using spectral data from aerial images and Landsat 5. First, we modeled CC for n = 1149 lidar plots using field-measured CCs and lidar data. Next, this data was split into five subsets in north-south direction (y-coordinate). Finally, four CC models (AerialSpectral, AerialPointcloud, AerialCombi (spectral + pointcloud) and Landsat) were created and they were used to predict new CC values to the lidar plots, subset by subset, using five-fold cross validation. The Landsat and AerialSpectral models performed with RMSEs of 13.8% and 12.4%, respectively. AerialPointcloud model reached an RMSE of 10.3%, which was further improved by the inclusion of spectral data; RMSE of the AerialCombi model was 9.3%. We noticed that the aerial image point clouds managed to describe only the outermost layer of the canopy and missed the details in lower canopy, which was resulted in weak characterization of the total CC variation, especially in the tails of the data.

Comparative Analysis of Data Structures for Storing Massive Tins in a Dbms

NASA Astrophysics Data System (ADS)

Kumar, K.; Ledoux, H.; Stoter, J.

2016-06-01

Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m2. PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.

Robust point cloud classification based on multi-level semantic relationships for urban scenes

NASA Astrophysics Data System (ADS)

Zhu, Qing; Li, Yuan; Hu, Han; Wu, Bo

2017-07-01

The semantic classification of point clouds is a fundamental part of three-dimensional urban reconstruction. For datasets with high spatial resolution but significantly more noises, a general trend is to exploit more contexture information to surmount the decrease of discrimination of features for classification. However, previous works on adoption of contexture information are either too restrictive or only in a small region and in this paper, we propose a point cloud classification method based on multi-level semantic relationships, including point-homogeneity, supervoxel-adjacency and class-knowledge constraints, which is more versatile and incrementally propagate the classification cues from individual points to the object level and formulate them as a graphical model. The point-homogeneity constraint clusters points with similar geometric and radiometric properties into regular-shaped supervoxels that correspond to the vertices in the graphical model. The supervoxel-adjacency constraint contributes to the pairwise interactions by providing explicit adjacent relationships between supervoxels. The class-knowledge constraint operates at the object level based on semantic rules, guaranteeing the classification correctness of supervoxel clusters at that level. International Society of Photogrammetry and Remote Sensing (ISPRS) benchmark tests have shown that the proposed method achieves state-of-the-art performance with an average per-area completeness and correctness of 93.88% and 95.78%, respectively. The evaluation of classification of photogrammetric point clouds and DSM generated from aerial imagery confirms the method's reliability in several challenging urban scenes.

Rapid, semi-automatic fracture and contact mapping for point clouds, images and geophysical data

NASA Astrophysics Data System (ADS)

Thiele, Samuel T.; Grose, Lachlan; Samsu, Anindita; Micklethwaite, Steven; Vollgger, Stefan A.; Cruden, Alexander R.

2017-12-01

The advent of large digital datasets from unmanned aerial vehicle (UAV) and satellite platforms now challenges our ability to extract information across multiple scales in a timely manner, often meaning that the full value of the data is not realised. Here we adapt a least-cost-path solver and specially tailored cost functions to rapidly interpolate structural features between manually defined control points in point cloud and raster datasets. We implement the method in the geographic information system QGIS and the point cloud and mesh processing software CloudCompare. Using these implementations, the method can be applied to a variety of three-dimensional (3-D) and two-dimensional (2-D) datasets, including high-resolution aerial imagery, digital outcrop models, digital elevation models (DEMs) and geophysical grids. We demonstrate the algorithm with four diverse applications in which we extract (1) joint and contact patterns in high-resolution orthophotographs, (2) fracture patterns in a dense 3-D point cloud, (3) earthquake surface ruptures of the Greendale Fault associated with the Mw7.1 Darfield earthquake (New Zealand) from high-resolution light detection and ranging (lidar) data, and (4) oceanic fracture zones from bathymetric data of the North Atlantic. The approach improves the consistency of the interpretation process while retaining expert guidance and achieves significant improvements (35-65 %) in digitisation time compared to traditional methods. Furthermore, it opens up new possibilities for data synthesis and can quantify the agreement between datasets and an interpretation.

Close Range Photogrammetry Applied to the Documentation of AN Archaeological Site in Gaza Strip, Palestine

NASA Astrophysics Data System (ADS)

Alby, E.; Elter, R.; Ripoche, C.; Quere, N.; de Strasbourg, INSA

2013-07-01

In a geopolitical very complex context as the Gaza Strip it has to be dealt with an enhancement of an archaeological site. This site is the monastery of St. Hilarion. To enable a cultural appropriation of a place with several identified phases of occupation must undertake extensive archaeological excavation. Excavate in this geographical area is to implement emergency excavations, so the aim of such a project can be questioned for each mission. Real estate pressure is also a motivating setting the documentation because the large population density does not allow systematic studies of underground before construction projects. This is also during the construction of a road that the site was discovered. Site dimensions are 150 m by 80 m. It is located on a sand dune, 300 m from the sea. To implement the survey, four different levels of detail have been defined for terrestrial photogrammetry. The first level elements are similar to objects, capitals, fragment of columns, tiles for example. Modeling of small objects requires the acquisition of very dense point clouds (density: 1 point / 1 mm on average). The object must then be a maximum area of the sensor of the camera, while retaining in the field of view a reference pattern for the scaling of the point cloud generated. The pictures are taken at a short distance from the object, using the images at full resolution. The main obstacle to the modeling of objects is the presence of noise partly due to the studied materials (sand, smooth rock), which do not favor the detection of points of interest quality. Pretreatments of the cloud will be achieved meticulously since the ouster of points on a surface of a small object results in the formation of a hole with a lack of information, useful to resulting mesh. Level 2 focuses on the stratigraphic units such as mosaics. The monastery of St. Hilarion identifies thirteen floors of which has been documented years ago by silver photographs, scanned later. Modeling of pavements is to obtain a three-dimensional model of the mosaic in particular to analyze the subsidence, which it may be subjected. The dense point cloud can go beyond by including the geometric shapes of the pavement. The calculation mesh using high-density point cloud colorization allows cloud sufficient to final rendering. Levels 3 and 4 will allow the survey and representation of loci and sectors. Their modeling can be done by colored mesh or textured by a generic pattern but also by geometric primitives. This method requires the segmentation simple geometrical elements and creates a surface geometry by analysis of the sample points. Statistical tools allow the extraction plans meet the requirements of the operator can monitor quantitatively the quality of the final rendering. Each level has constraints on the accuracy of survey and types of representation especially from the point clouds, which are detailed in the complete article.

The Improvement of the Closed Bounded Volume (CBV) Evaluation Methods to Compute a Feasible Rough Machining Area Based on Faceted Models

NASA Astrophysics Data System (ADS)

Hadi Sutrisno, Himawan; Kiswanto, Gandjar; Istiyanto, Jos

2017-06-01

The rough machining is aimed at shaping a workpiece towards to its final form. This process takes up a big proportion of the machining time due to the removal of the bulk material which may affect the total machining time. In certain models, the rough machining has limitations especially on certain surfaces such as turbine blade and impeller. CBV evaluation is one of the concepts which is used to detect of areas admissible in the process of machining. While in the previous research, CBV area detection used a pair of normal vectors, in this research, the writer simplified the process to detect CBV area with a slicing line for each point cloud formed. The simulation resulted in three steps used for this method and they are: 1. Triangulation from CAD design models, 2. Development of CC point from the point cloud, 3. The slicing line method which is used to evaluate each point cloud position (under CBV and outer CBV). The result of this evaluation method can be used as a tool for orientation set-up on each CC point position of feasible areas in rough machining.

A new algorithm combining geostatistics with the surrogate data approach to increase the accuracy of comparisons of point radiation measurements with cloud measurements

NASA Astrophysics Data System (ADS)

Venema, V. K. C.; Lindau, R.; Varnai, T.; Simmer, C.

2009-04-01

Two main groups of statistical methods used in the Earth sciences are geostatistics and stochastic modelling. Geostatistical methods, such as various kriging algorithms, aim at estimating the mean value for every point as well as possible. In case of sparse measurements, such fields have less variability at small scales and a narrower distribution as the true field. This can lead to biases if a nonlinear process is simulated on such a kriged field. Stochastic modelling aims at reproducing the structure of the data. One of the stochastic modelling methods, the so-called surrogate data approach, replicates the value distribution and power spectrum of a certain data set. However, while stochastic methods reproduce the statistical properties of the data, the location of the measurement is not considered. Because radiative transfer through clouds is a highly nonlinear process it is essential to model the distribution (e.g. of optical depth, extinction, liquid water content or liquid water path) accurately as well as the correlations in the cloud field because of horizontal photon transport. This explains the success of surrogate cloud fields for use in 3D radiative transfer studies. However, up to now we could only achieve good results for the radiative properties averaged over the field, but not for a radiation measurement located at a certain position. Therefore we have developed a new algorithm that combines the accuracy of stochastic (surrogate) modelling with the positioning capabilities of kriging. In this way, we can automatically profit from the large geostatistical literature and software. The algorithm is tested on cloud fields from large eddy simulations (LES). On these clouds a measurement is simulated. From the pseudo-measurement we estimated the distribution and power spectrum. Furthermore, the pseudo-measurement is kriged to a field the size of the final surrogate cloud. The distribution, spectrum and the kriged field are the inputs to the algorithm. This algorithm is similar to the standard iterative amplitude adjusted Fourier transform (IAAFT) algorithm, but has an additional iterative step in which the surrogate field is nudged towards the kriged field. The nudging strength is gradually reduced to zero. We work with four types of pseudo-measurements: one zenith pointing measurement (which together with the wind produces a line measurement), five zenith pointing measurements, a slow and a fast azimuth scan (which together with the wind produce spirals). Because we work with LES clouds and the truth is known, we can validate the algorithm by performing 3D radiative transfer calculations on the original LES clouds and on the new surrogate clouds. For comparison also the radiative properties of the kriged fields and standard surrogate fields are computed. Preliminary results already show that these new surrogate clouds reproduce the structure of the original clouds very well and the minima and maxima are located where the pseudo-measurements sees them. The main limitation seems to be the amount of data, which is especially very limited in case of just one zenith pointing measurement.

Utilizing the Iterative Closest Point (ICP) algorithm for enhanced registration of high resolution surface models - more than a simple black-box application

NASA Astrophysics Data System (ADS)

Stöcker, Claudia; Eltner, Anette

2016-04-01

Advances in computer vision and digital photogrammetry (i.e. structure from motion) allow for fast and flexible high resolution data supply. Within geoscience applications and especially in the field of small surface topography, high resolution digital terrain models and dense 3D point clouds are valuable data sources to capture actual states as well as for multi-temporal studies. However, there are still some limitations regarding robust registration and accuracy demands (e.g. systematic positional errors) which impede the comparison and/or combination of multi-sensor data products. Therefore, post-processing of 3D point clouds can heavily enhance data quality. In this matter the Iterative Closest Point (ICP) algorithm represents an alignment tool which iteratively minimizes distances of corresponding points within two datasets. Even though tool is widely used; it is often applied as a black-box application within 3D data post-processing for surface reconstruction. Aiming for precise and accurate combination of multi-sensor data sets, this study looks closely at different variants of the ICP algorithm including sub-steps of point selection, point matching, weighting, rejection, error metric and minimization. Therefore, an agricultural utilized field was investigated simultaneously by terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) sensors two times (once covered with sparse vegetation and once bare soil). Due to different perspectives both data sets show diverse consistency in terms of shadowed areas and thus gaps so that data merging would provide consistent surface reconstruction. Although photogrammetric processing already included sub-cm accurate ground control surveys, UAV point cloud exhibits an offset towards TLS point cloud. In order to achieve the transformation matrix for fine registration of UAV point clouds, different ICP variants were tested. Statistical analyses of the results show that final success of registration and therefore data quality depends particularly on parameterization and choice of error metric, especially for erroneous data sets as in the case of sparse vegetation cover. At this, the point-to-point metric is more sensitive to data "noise" than the point-to-plane metric which results in considerably higher cloud-to-cloud distances. Concluding, in order to comply with accuracy demands of high resolution surface reconstruction and the aspect that ground control surveys can reach their limits both in time exposure and terrain accessibility ICP algorithm represents a great tool to refine rough initial alignment. Here different variants of registration modules allow for individual application according to the quality of the input data.

Virtual reality based adaptive dose assessment method for arbitrary geometries in nuclear facility decommissioning.

PubMed

Liu, Yong-Kuo; Chao, Nan; Xia, Hong; Peng, Min-Jun; Ayodeji, Abiodun

2018-05-17

This paper presents an improved and efficient virtual reality-based adaptive dose assessment method (VRBAM) applicable to the cutting and dismantling tasks in nuclear facility decommissioning. The method combines the modeling strength of virtual reality with the flexibility of adaptive technology. The initial geometry is designed with the three-dimensional computer-aided design tools, and a hybrid model composed of cuboids and a point-cloud is generated automatically according to the virtual model of the object. In order to improve the efficiency of dose calculation while retaining accuracy, the hybrid model is converted to a weighted point-cloud model, and the point kernels are generated by adaptively simplifying the weighted point-cloud model according to the detector position, an approach that is suitable for arbitrary geometries. The dose rates are calculated with the Point-Kernel method. To account for radiation scattering effects, buildup factors are calculated with the Geometric-Progression formula in the fitting function. The geometric modeling capability of VRBAM was verified by simulating basic geometries, which included a convex surface, a concave surface, a flat surface and their combination. The simulation results show that the VRBAM is more flexible and superior to other approaches in modeling complex geometries. In this paper, the computation time and dose rate results obtained from the proposed method were also compared with those obtained using the MCNP code and an earlier virtual reality-based method (VRBM) developed by the same authors. © 2018 IOP Publishing Ltd.

Point-Cloud Compression for Vehicle-Based Mobile Mapping Systems Using Portable Network Graphics

NASA Astrophysics Data System (ADS)

Kohira, K.; Masuda, H.

2017-09-01

A mobile mapping system is effective for capturing dense point-clouds of roads and roadside objects Point-clouds of urban areas, residential areas, and arterial roads are useful for maintenance of infrastructure, map creation, and automatic driving. However, the data size of point-clouds measured in large areas is enormously large. A large storage capacity is required to store such point-clouds, and heavy loads will be taken on network if point-clouds are transferred through the network. Therefore, it is desirable to reduce data sizes of point-clouds without deterioration of quality. In this research, we propose a novel point-cloud compression method for vehicle-based mobile mapping systems. In our compression method, point-clouds are mapped onto 2D pixels using GPS time and the parameters of the laser scanner. Then, the images are encoded in the Portable Networking Graphics (PNG) format and compressed using the PNG algorithm. In our experiments, our method could efficiently compress point-clouds without deteriorating the quality.

The registration of non-cooperative moving targets laser point cloud in different view point

NASA Astrophysics Data System (ADS)

Wang, Shuai; Sun, Huayan; Guo, Huichao

2018-01-01

Non-cooperative moving target multi-view cloud registration is the key technology of 3D reconstruction of laser threedimension imaging. The main problem is that the density changes greatly and noise exists under different acquisition conditions of point cloud. In this paper, firstly, the feature descriptor is used to find the most similar point cloud, and then based on the registration algorithm of region segmentation, the geometric structure of the point is extracted by the geometric similarity between point and point, The point cloud is divided into regions based on spectral clustering, feature descriptors are created for each region, searching to find the most similar regions in the most similar point of view cloud, and then aligning the pair of point clouds by aligning their minimum bounding boxes. Repeat the above steps again until registration of all point clouds is completed. Experiments show that this method is insensitive to the density of point clouds and performs well on the noise of laser three-dimension imaging.

A Direct Georeferencing Method for Terrestrial Laser Scanning Using GNSS Data and the Vertical Deflection from Global Earth Gravity Models

PubMed Central

Borkowski, Andrzej; Owczarek-Wesołowska, Magdalena; Gromczak, Anna

2017-01-01

Terrestrial laser scanning is an efficient technique in providing highly accurate point clouds for various geoscience applications. The point clouds have to be transformed to a well-defined reference frame, such as the global Geodetic Reference System 1980. The transformation to the geocentric coordinate frame is based on estimating seven Helmert parameters using several GNSS (Global Navigation Satellite System) referencing points. This paper proposes a method for direct point cloud georeferencing that provides coordinates in the geocentric frame. The proposed method employs the vertical deflection from an external global Earth gravity model and thus demands a minimum number of GNSS measurements. The proposed method can be helpful when the number of georeferencing GNSS points is limited, for instance in city corridors. It needs only two georeferencing points. The validation of the method in a field test reveals that the differences between the classical georefencing and the proposed method amount at maximum to 7 mm with the standard deviation of 8 mm for all of three coordinate components. The proposed method may serve as an alternative for the laser scanning data georeferencing, especially when the number of GNSS points is insufficient for classical methods. PMID:28672795

A Direct Georeferencing Method for Terrestrial Laser Scanning Using GNSS Data and the Vertical Deflection from Global Earth Gravity Models.

PubMed

Osada, Edward; Sośnica, Krzysztof; Borkowski, Andrzej; Owczarek-Wesołowska, Magdalena; Gromczak, Anna

2017-06-24

Terrestrial laser scanning is an efficient technique in providing highly accurate point clouds for various geoscience applications. The point clouds have to be transformed to a well-defined reference frame, such as the global Geodetic Reference System 1980. The transformation to the geocentric coordinate frame is based on estimating seven Helmert parameters using several GNSS (Global Navigation Satellite System) referencing points. This paper proposes a method for direct point cloud georeferencing that provides coordinates in the geocentric frame. The proposed method employs the vertical deflection from an external global Earth gravity model and thus demands a minimum number of GNSS measurements. The proposed method can be helpful when the number of georeferencing GNSS points is limited, for instance in city corridors. It needs only two georeferencing points. The validation of the method in a field test reveals that the differences between the classical georefencing and the proposed method amount at maximum to 7 mm with the standard deviation of 8 mm for all of three coordinate components. The proposed method may serve as an alternative for the laser scanning data georeferencing, especially when the number of GNSS points is insufficient for classical methods.

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Li, Xiaowen; Khain, Alexander; Matsui, Toshihisa; Lang, Stephen; Simpson, Joanne

2012-01-01

Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. Atmospheric aerosols are also described using number density size-distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and summertime convection over a mid-latitude continent with different concentrations of CCN: a low clean concentration and a high dirty concentration. The impact of atmospheric aerosol concentration on cloud and precipitation will be investigated.

Estimation of cylinder orientation in three-dimensional point cloud using angular distance-based optimization

NASA Astrophysics Data System (ADS)

Su, Yun-Ting; Hu, Shuowen; Bethel, James S.

2017-05-01

Light detection and ranging (LIDAR) has become a widely used tool in remote sensing for mapping, surveying, modeling, and a host of other applications. The motivation behind this work is the modeling of piping systems in industrial sites, where cylinders are the most common primitive or shape. We focus on cylinder parameter estimation in three-dimensional point clouds, proposing a mathematical formulation based on angular distance to determine the cylinder orientation. We demonstrate the accuracy and robustness of the technique on synthetically generated cylinder point clouds (where the true axis orientation is known) as well as on real LIDAR data of piping systems. The proposed algorithm is compared with a discrete space Hough transform-based approach as well as a continuous space inlier approach, which iteratively discards outlier points to refine the cylinder parameter estimates. Results show that the proposed method is more computationally efficient than the Hough transform approach and is more accurate than both the Hough transform approach and the inlier method.

An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features.

PubMed

He, Ying; Liang, Bin; Yang, Jun; Li, Shunzhi; He, Jin

2017-08-11

The Iterative Closest Points (ICP) algorithm is the mainstream algorithm used in the process of accurate registration of 3D point cloud data. The algorithm requires a proper initial value and the approximate registration of two point clouds to prevent the algorithm from falling into local extremes, but in the actual point cloud matching process, it is difficult to ensure compliance with this requirement. In this paper, we proposed the ICP algorithm based on point cloud features (GF-ICP). This method uses the geometrical features of the point cloud to be registered, such as curvature, surface normal and point cloud density, to search for the correspondence relationships between two point clouds and introduces the geometric features into the error function to realize the accurate registration of two point clouds. The experimental results showed that the algorithm can improve the convergence speed and the interval of convergence without setting a proper initial value.

An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features

PubMed Central

Liang, Bin; Yang, Jun; Li, Shunzhi; He, Jin

2017-01-01

The Iterative Closest Points (ICP) algorithm is the mainstream algorithm used in the process of accurate registration of 3D point cloud data. The algorithm requires a proper initial value and the approximate registration of two point clouds to prevent the algorithm from falling into local extremes, but in the actual point cloud matching process, it is difficult to ensure compliance with this requirement. In this paper, we proposed the ICP algorithm based on point cloud features (GF-ICP). This method uses the geometrical features of the point cloud to be registered, such as curvature, surface normal and point cloud density, to search for the correspondence relationships between two point clouds and introduces the geometric features into the error function to realize the accurate registration of two point clouds. The experimental results showed that the algorithm can improve the convergence speed and the interval of convergence without setting a proper initial value. PMID:28800096

Study of texture stitching in 3D modeling of lidar point cloud based on per-pixel linear interpolation along loop line buffer

NASA Astrophysics Data System (ADS)

Xu, Jianxin; Liang, Hong

2013-07-01

Terrestrial laser scanning creates a point cloud composed of thousands or millions of 3D points. Through pre-processing, generating TINs, mapping texture, a 3D model of a real object is obtained. When the object is too large, the object is separated into some parts. This paper mainly focuses on problem of gray uneven of two adjacent textures' intersection. The new algorithm is presented in the paper, which is per-pixel linear interpolation along loop line buffer .The experiment data derives from point cloud of stone lion which is situated in front of west gate of Henan Polytechnic University. The model flow is composed of three parts. First, the large object is separated into two parts, and then each part is modeled, finally the whole 3D model of the stone lion is composed of two part models. When the two part models are combined, there is an obvious fissure line in the overlapping section of two adjacent textures for the two models. Some researchers decrease brightness value of all pixels for two adjacent textures by some algorithms. However, some algorithms are effect and the fissure line still exists. Gray uneven of two adjacent textures is dealt by the algorithm in the paper. The fissure line in overlapping section textures is eliminated. The gray transition in overlapping section become more smoothly.

Development of Three-Dimensional Dental Scanning Apparatus Using Structured Illumination

PubMed Central

Park, Anjin; Lee, Byeong Ha; Eom, Joo Beom

2017-01-01

We demonstrated a three-dimensional (3D) dental scanning apparatus based on structured illumination. A liquid lens was used for tuning focus and a piezomotor stage was used for the shift of structured light. A simple algorithm, which detects intensity modulation, was used to perform optical sectioning with structured illumination. We reconstructed a 3D point cloud, which represents the 3D coordinates of the digitized surface of a dental gypsum cast by piling up sectioned images. We performed 3D registration of an individual 3D point cloud, which includes alignment and merging the 3D point clouds to exhibit a 3D model of the dental cast. PMID:28714897

Minimalist model of ice microphysics in mixed-phase stratiform clouds

NASA Astrophysics Data System (ADS)

Yang, F.; Ovchinnikov, M.; Shaw, R. A.

2013-12-01

The question of whether persistent ice crystal precipitation from supercooled layer clouds can be explained by time-dependent, stochastic ice nucleation is explored using an approximate, analytical model and a large-eddy simulation (LES) cloud model. The updraft velocity in the cloud defines an accumulation zone, where small ice particles cannot fall out until they are large enough, which will increase the residence time of ice particles in the cloud. Ice particles reach a quasi-steady state between growth by vapor deposition and fall speed at cloud base. The analytical model predicts that ice water content (wi) has a 2.5 power-law relationship with ice number concentration (ni). wi and ni from a LES cloud model with stochastic ice nucleation confirm the 2.5 power-law relationship, and initial indications of the scaling law are observed in data from the Indirect and Semi-Direct Aerosol Campaign. The prefactor of the power law is proportional to the ice nucleation rate and therefore provides a quantitative link to observations of ice microphysical properties. Ice water content (wi) and ice number concentration (ni) relationship from LES. a and c: Accumulation zone region; b and d: Selective accumulation zone region. Black lines in c and d are best fitted 2.5 slope lines. Colors in Figures a and b represent updraft velocity, while colors in c and d represent altitude. The cloud base and top are at about 600 m and 800 m, respectively. Ice water content (wi) and ice number concentration (ni) relationship for two ice nucleation rates. Blue points are from LES with low ice nucleation rate and red points with high ice nucleation rate. Solid and dashed lines are best fitted 2.5 slope lines.

Accuracy Assessment of Underwater Photogrammetric Three Dimensional Modelling for Coral Reefs

NASA Astrophysics Data System (ADS)

Guo, T.; Capra, A.; Troyer, M.; Gruen, A.; Brooks, A. J.; Hench, J. L.; Schmitt, R. J.; Holbrook, S. J.; Dubbini, M.

2016-06-01

Recent advances in automation of photogrammetric 3D modelling software packages have stimulated interest in reconstructing highly accurate 3D object geometry in unconventional environments such as underwater utilizing simple and low-cost camera systems. The accuracy of underwater 3D modelling is affected by more parameters than in single media cases. This study is part of a larger project on 3D measurements of temporal change of coral cover in tropical waters. It compares the accuracies of 3D point clouds generated by using images acquired from a system camera mounted in an underwater housing and the popular GoPro cameras respectively. A precisely measured calibration frame was placed in the target scene in order to provide accurate control information and also quantify the errors of the modelling procedure. In addition, several objects (cinder blocks) with various shapes were arranged in the air and underwater and 3D point clouds were generated by automated image matching. These were further used to examine the relative accuracy of the point cloud generation by comparing the point clouds of the individual objects with the objects measured by the system camera in air (the best possible values). Given a working distance of about 1.5 m, the GoPro camera can achieve a relative accuracy of 1.3 mm in air and 2.0 mm in water. The system camera achieved an accuracy of 1.8 mm in water, which meets our requirements for coral measurement in this system.

Clustering, randomness and regularity in cloud fields. I - Theoretical considerations. II - Cumulus cloud fields

NASA Technical Reports Server (NTRS)

Weger, R. C.; Lee, J.; Zhu, Tianri; Welch, R. M.

1992-01-01

The current controversy existing in reference to the regularity vs. clustering in cloud fields is examined by means of analysis and simulation studies based upon nearest-neighbor cumulative distribution statistics. It is shown that the Poisson representation of random point processes is superior to pseudorandom-number-generated models and that pseudorandom-number-generated models bias the observed nearest-neighbor statistics towards regularity. Interpretation of this nearest-neighbor statistics is discussed for many cases of superpositions of clustering, randomness, and regularity. A detailed analysis is carried out of cumulus cloud field spatial distributions based upon Landsat, AVHRR, and Skylab data, showing that, when both large and small clouds are included in the cloud field distributions, the cloud field always has a strong clustering signal.

Identity-Based Authentication for Cloud Computing

NASA Astrophysics Data System (ADS)

Li, Hongwei; Dai, Yuanshun; Tian, Ling; Yang, Haomiao

Cloud computing is a recently developed new technology for complex systems with massive-scale services sharing among numerous users. Therefore, authentication of both users and services is a significant issue for the trust and security of the cloud computing. SSL Authentication Protocol (SAP), once applied in cloud computing, will become so complicated that users will undergo a heavily loaded point both in computation and communication. This paper, based on the identity-based hierarchical model for cloud computing (IBHMCC) and its corresponding encryption and signature schemes, presented a new identity-based authentication protocol for cloud computing and services. Through simulation testing, it is shown that the authentication protocol is more lightweight and efficient than SAP, specially the more lightweight user side. Such merit of our model with great scalability is very suited to the massive-scale cloud.

A Quantitative Investigation of Entrainment and Detrainment in Numerically Simulated Convective Clouds. Pt. 1; Model Development

NASA Technical Reports Server (NTRS)

Cohen, Charles

1998-01-01

A method is developed which uses numerical tracers to make accurate diagnoses of entraimnent and detrainment rates and of the properties of the entrained and detrained air in numerically simulated clouds. The numerical advection scheme is modified to make it nondispersive, as required by the use of the tracers. Tests of the new method are made, and an appropriate definition of clouds is selected. Distributions of mixing fractions in the model consistently show maximums at the end points, for nearly undilute environmental air or nearly undilute cloud air, with a uniform distribution between. The cumulonimbus clouds simulated here entrain air that had been substantially changed by the clouds, and detrained air that is not necessarily representative of the cloud air at the same level.

Point Cloud Based Change Detection - an Automated Approach for Cloud-based Services

NASA Astrophysics Data System (ADS)

Collins, Patrick; Bahr, Thomas

2016-04-01

The fusion of stereo photogrammetric point clouds with LiDAR data or terrain information derived from SAR interferometry has a significant potential for 3D topographic change detection. In the present case study latest point cloud generation and analysis capabilities are used to examine a landslide that occurred in the village of Malin in Maharashtra, India, on 30 July 2014, and affected an area of ca. 44.000 m2. It focuses on Pléiades high resolution satellite imagery and the Airbus DS WorldDEMTM as a product of the TanDEM-X mission. This case study was performed using the COTS software package ENVI 5.3. Integration of custom processes and automation is supported by IDL (Interactive Data Language). Thus, ENVI analytics is running via the object-oriented and IDL-based ENVITask API. The pre-event topography is represented by the WorldDEMTM product, delivered with a raster of 12 m x 12 m and based on the EGM2008 geoid (called pre-DEM). For the post-event situation a Pléiades 1B stereo image pair of the AOI affected was obtained. The ENVITask "GeneratePointCloudsByDenseImageMatching" was implemented to extract passive point clouds in LAS format from the panchromatic stereo datasets: • A dense image-matching algorithm is used to identify corresponding points in the two images. • A block adjustment is applied to refine the 3D coordinates that describe the scene geometry. • Additionally, the WorldDEMTM was input to constrain the range of heights in the matching area, and subsequently the length of the epipolar line. The "PointCloudFeatureExtraction" task was executed to generate the post-event digital surface model from the photogrammetric point clouds (called post-DEM). Post-processing consisted of the following steps: • Adding the geoid component (EGM 2008) to the post-DEM. • Pre-DEM reprojection to the UTM Zone 43N (WGS-84) coordinate system and resizing. • Subtraction of the pre-DEM from the post-DEM. • Filtering and threshold based classification of the DEM difference to analyze the surface changes in 3D. The automated point cloud generation and analysis introduced here can be embedded in virtually any existing geospatial workflow for operational applications. Three integration options were implemented in this case study: • Integration within any ArcGIS environment whether deployed on the desktop, in the cloud, or online. Execution uses a customized ArcGIS script tool. A Python script file retrieves the parameters from the user interface and runs the precompiled IDL code. That IDL code is used to interface between the Python script and the relevant ENVITasks. • Publishing the point cloud processing tasks as services via the ENVI Services Engine (ESE). ESE is a cloud-based image analysis solution to publish and deploy advanced ENVI image and data analytics to existing enterprise infrastructures. For this purpose the entire IDL code can be capsuled in a single ENVITask. • Integration in an existing geospatial workflow using the Python-to-IDL Bridge. This mechanism allows calling IDL code within Python on a user-defined platform. The results of this case study allow a 3D estimation of the topographic changes within the tectonically active and anthropogenically invaded Malin area after the landslide event. Accordingly, the point cloud analysis was correlated successfully with modelled displacement contours of the slope. Based on optical satellite imagery, such point clouds of high precision and density distribution can be obtained in a few minutes to support the operational monitoring of landslide processes.

Achievable Rate Estimation of IEEE 802.11ad Visual Big-Data Uplink Access in Cloud-Enabled Surveillance Applications.

PubMed

Kim, Joongheon; Kim, Jong-Kook

2016-01-01

This paper addresses the computation procedures for estimating the impact of interference in 60 GHz IEEE 802.11ad uplink access in order to construct visual big-data database from randomly deployed surveillance camera sensing devices. The acquired large-scale massive visual information from surveillance camera devices will be used for organizing big-data database, i.e., this estimation is essential for constructing centralized cloud-enabled surveillance database. This performance estimation study captures interference impacts on the target cloud access points from multiple interference components generated by the 60 GHz wireless transmissions from nearby surveillance camera devices to their associated cloud access points. With this uplink interference scenario, the interference impacts on the main wireless transmission from a target surveillance camera device to its associated target cloud access point with a number of settings are measured and estimated under the consideration of 60 GHz radiation characteristics and antenna radiation pattern models.

Scaling predictive modeling in drug development with cloud computing.

PubMed

Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

2015-01-26

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

Bayesian Multiscale Modeling of Closed Curves in Point Clouds

PubMed Central

Gu, Kelvin; Pati, Debdeep; Dunson, David B.

2014-01-01

Modeling object boundaries based on image or point cloud data is frequently necessary in medical and scientific applications ranging from detecting tumor contours for targeted radiation therapy, to the classification of organisms based on their structural information. In low-contrast images or sparse and noisy point clouds, there is often insufficient data to recover local segments of the boundary in isolation. Thus, it becomes critical to model the entire boundary in the form of a closed curve. To achieve this, we develop a Bayesian hierarchical model that expresses highly diverse 2D objects in the form of closed curves. The model is based on a novel multiscale deformation process. By relating multiple objects through a hierarchical formulation, we can successfully recover missing boundaries by borrowing structural information from similar objects at the appropriate scale. Furthermore, the model’s latent parameters help interpret the population, indicating dimensions of significant structural variability and also specifying a ‘central curve’ that summarizes the collection. Theoretical properties of our prior are studied in specific cases and efficient Markov chain Monte Carlo methods are developed, evaluated through simulation examples and applied to panorex teeth images for modeling teeth contours and also to a brain tumor contour detection problem. PMID:25544786

Linking Advanced Visualization and MATLAB for the Analysis of 3D Gene Expression Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruebel, Oliver; Keranen, Soile V.E.; Biggin, Mark

Three-dimensional gene expression PointCloud data generated by the Berkeley Drosophila Transcription Network Project (BDTNP) provides quantitative information about the spatial and temporal expression of genes in early Drosophila embryos at cellular resolution. The BDTNP team visualizes and analyzes Point-Cloud data using the software application PointCloudXplore (PCX). To maximize the impact of novel, complex data sets, such as PointClouds, the data needs to be accessible to biologists and comprehensible to developers of analysis functions. We address this challenge by linking PCX and Matlab via a dedicated interface, thereby providing biologists seamless access to advanced data analysis functions and giving bioinformatics researchersmore » the opportunity to integrate their analysis directly into the visualization application. To demonstrate the usefulness of this approach, we computationally model parts of the expression pattern of the gene even skipped using a genetic algorithm implemented in Matlab and integrated into PCX via our Matlab interface.« less

Interactive Classification of Construction Materials: Feedback Driven Framework for Annotation and Analysis of 3d Point Clouds

NASA Astrophysics Data System (ADS)

Hess, M. R.; Petrovic, V.; Kuester, F.

2017-08-01

Digital documentation of cultural heritage structures is increasingly more common through the application of different imaging techniques. Many works have focused on the application of laser scanning and photogrammetry techniques for the acquisition of threedimensional (3D) geometry detailing cultural heritage sites and structures. With an abundance of these 3D data assets, there must be a digital environment where these data can be visualized and analyzed. Presented here is a feedback driven visualization framework that seamlessly enables interactive exploration and manipulation of massive point cloud data. The focus of this work is on the classification of different building materials with the goal of building more accurate as-built information models of historical structures. User defined functions have been tested within the interactive point cloud visualization framework to evaluate automated and semi-automated classification of 3D point data. These functions include decisions based on observed color, laser intensity, normal vector or local surface geometry. Multiple case studies are presented here to demonstrate the flexibility and utility of the presented point cloud visualization framework to achieve classification objectives.

Geospatial Field Methods: An Undergraduate Course Built Around Point Cloud Construction and Analysis to Promote Spatial Learning and Use of Emerging Technology in Geoscience

NASA Astrophysics Data System (ADS)

Bunds, M. P.

2017-12-01

Point clouds are a powerful data source in the geosciences, and the emergence of structure-from-motion (SfM) photogrammetric techniques has allowed them to be generated quickly and inexpensively. Consequently, applications of them as well as methods to generate, manipulate, and analyze them warrant inclusion in undergraduate curriculum. In a new course called Geospatial Field Methods at Utah Valley University, students in small groups use SfM to generate a point cloud from imagery collected with a small unmanned aerial system (sUAS) and use it as a primary data source for a research project. Before creating their point clouds, students develop needed technical skills in laboratory and class activities. The students then apply the skills to construct the point clouds, and the research projects and point cloud construction serve as a central theme for the class. Intended student outcomes for the class include: technical skills related to acquiring, processing, and analyzing geospatial data; improved ability to carry out a research project; and increased knowledge related to their specific project. To construct the point clouds, students first plan their field work by outlining the field site, identifying locations for ground control points (GCPs), and loading them onto a handheld GPS for use in the field. They also estimate sUAS flight elevation, speed, and the flight path grid spacing required to produce a point cloud with the resolution required for their project goals. In the field, the students place the GCPs using handheld GPS, and survey the GCP locations using post-processed-kinematic (PPK) or real-time-kinematic (RTK) methods. The students pilot the sUAS and operate its camera according to the parameters that they estimated in planning their field work. Data processing includes obtaining accurate locations for the PPK/RTK base station and GCPs, and SfM processing with Agisoft Photoscan. The resulting point clouds are rasterized into digital surface models, assessed for accuracy, and analyzed in Geographic Information System software. Student projects have included mapping and analyzing landslide morphology, fault scarps, and earthquake ground surface rupture. Students have praised the geospatial skills they learn, whereas helping them stay on schedule to finish their projects is a challenge.

Reconstruction of forest geometries from terrestrial laser scanning point clouds for canopy radiative transfer modelling

NASA Astrophysics Data System (ADS)

Bremer, Magnus; Schmidtner, Korbinian; Rutzinger, Martin

2015-04-01

The architecture of forest canopies is a key parameter for forest ecological issues helping to model the variability of wood biomass and foliage in space and time. In order to understand the nature of subpixel effects of optical space-borne sensors with coarse spatial resolution, hypothetical 3D canopy models are widely used for the simulation of radiative transfer in forests. Thereby, radiation is traced through the atmosphere and canopy geometries until it reaches the optical sensor. For a realistic simulation scene we decompose terrestrial laser scanning point cloud data of leaf-off larch forest plots in the Austrian Alps and reconstruct detailed model ready input data for radiative transfer simulations. The point clouds are pre-classified into primitive classes using Principle Component Analysis (PCA) using scale adapted radius neighbourhoods. Elongated point structures are extracted as tree trunks. The tree trunks are used as seeds for a Dijkstra-growing procedure, in order to obtain single tree segmentation in the interlinked canopies. For the optimized reconstruction of branching architectures as vector models, point cloud skeletonisation is used in combination with an iterative Dijkstra-growing and by applying distance constraints. This allows conducting a hierarchical reconstruction preferring the tree trunk and higher order branches and avoiding over-skeletonization effects. Based on the reconstructed branching architectures, larch needles are modelled based on the hierarchical level of branches and the geometrical openness of the canopy. For radiative transfer simulations, branch architectures are used as mesh geometries representing branches as cylindrical pipes. Needles are either used as meshes or as voxel-turbids. The presented workflow allows an automatic classification and single tree segmentation in interlinked canopies. The iterative Dijkstra-growing using distance constraints generated realistic reconstruction results. As the mesh representation of branches proved to be sufficient for the simulation approach, the modelling of huge amounts of needles is much more efficient in voxel-turbid representation.

SPARSE—A subgrid particle averaged Reynolds stress equivalent model: testing with a priori closure

PubMed Central

Davis, Sean L.; Sen, Oishik; Udaykumar, H. S.

2017-01-01

A Lagrangian particle cloud model is proposed that accounts for the effects of Reynolds-averaged particle and turbulent stresses and the averaged carrier-phase velocity of the subparticle cloud scale on the averaged motion and velocity of the cloud. The SPARSE (subgrid particle averaged Reynolds stress equivalent) model is based on a combination of a truncated Taylor expansion of a drag correction function and Reynolds averaging. It reduces the required number of computational parcels to trace a cloud of particles in Eulerian–Lagrangian methods for the simulation of particle-laden flow. Closure is performed in an a priori manner using a reference simulation where all particles in the cloud are traced individually with a point-particle model. Comparison of a first-order model and SPARSE with the reference simulation in one dimension shows that both the stress and the averaging of the carrier-phase velocity on the cloud subscale affect the averaged motion of the particle. A three-dimensional isotropic turbulence computation shows that only one computational parcel is sufficient to accurately trace a cloud of tens of thousands of particles. PMID:28413341

SPARSE-A subgrid particle averaged Reynolds stress equivalent model: testing with a priori closure.

PubMed

Davis, Sean L; Jacobs, Gustaaf B; Sen, Oishik; Udaykumar, H S

2017-03-01

A Lagrangian particle cloud model is proposed that accounts for the effects of Reynolds-averaged particle and turbulent stresses and the averaged carrier-phase velocity of the subparticle cloud scale on the averaged motion and velocity of the cloud. The SPARSE (subgrid particle averaged Reynolds stress equivalent) model is based on a combination of a truncated Taylor expansion of a drag correction function and Reynolds averaging. It reduces the required number of computational parcels to trace a cloud of particles in Eulerian-Lagrangian methods for the simulation of particle-laden flow. Closure is performed in an a priori manner using a reference simulation where all particles in the cloud are traced individually with a point-particle model. Comparison of a first-order model and SPARSE with the reference simulation in one dimension shows that both the stress and the averaging of the carrier-phase velocity on the cloud subscale affect the averaged motion of the particle. A three-dimensional isotropic turbulence computation shows that only one computational parcel is sufficient to accurately trace a cloud of tens of thousands of particles.

A novel point cloud registration using 2D image features

NASA Astrophysics Data System (ADS)

Lin, Chien-Chou; Tai, Yen-Chou; Lee, Jhong-Jin; Chen, Yong-Sheng

2017-01-01

Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

An Emprical Point Error Model for Tls Derived Point Clouds

NASA Astrophysics Data System (ADS)

Ozendi, Mustafa; Akca, Devrim; Topan, Hüseyin

2016-06-01

The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (𝜎𝜃) and vertical (𝜎𝛼) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as 𝜎𝜃=±36.6𝑐𝑐 and 𝜎𝛼=±17.8𝑐𝑐, respectively. On the other hand, a priori precision of the range observation (𝜎𝜌) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as 𝜎𝜌=±2-12 𝑚𝑚 for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yuying; Xie, Shaocheng; Klein, Stephen A.

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the conceptmore » of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to surface contamination (Mace et al. 2007; Marchand et al. 2008). Therefore, the ARM ground-based cloud observations can provide important observations of clouds that complement measurements from space.« less

Quantitative evaluation for small surface damage based on iterative difference and triangulation of 3D point cloud

NASA Astrophysics Data System (ADS)

Zhang, Yuyan; Guo, Quanli; Wang, Zhenchun; Yang, Degong

2018-03-01

This paper proposes a non-contact, non-destructive evaluation method for the surface damage of high-speed sliding electrical contact rails. The proposed method establishes a model of damage identification and calculation. A laser scanning system is built to obtain the 3D point cloud data of the rail surface. In order to extract the damage region of the rail surface, the 3D point cloud data are processed using iterative difference, nearest neighbours search and a data registration algorithm. The curvature of the point cloud data in the damage region is mapped to RGB color information, which can directly reflect the change trend of the curvature of the point cloud data in the damage region. The extracted damage region is divided into three prism elements by a method of triangulation. The volume and mass of a single element are calculated by the method of geometric segmentation. Finally, the total volume and mass of the damage region are obtained by the principle of superposition. The proposed method is applied to several typical injuries and the results are discussed. The experimental results show that the algorithm can identify damage shapes and calculate damage mass with milligram precision, which are useful for evaluating the damage in a further research stage.

Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Babacan, K.; Chen, L.; Sohn, G.

2017-11-01

As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever increasing variety of semantic information is needed to express an indoor model adequately. On the other hand, for the existing buildings, automatically generating semantically enriched BIM from point cloud data is in its infancy. The previous research to enhance the semantic content rely on frameworks in which some specific rules and/or features that are hand coded by specialists. These methods immanently lack generalization and easily break in different circumstances. On this account, a generalized framework is urgently needed to automatically and accurately generate semantic information. Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. More specifically, we build a volumetric data representation in order to efficiently generate the high number of training samples needed to initiate a convolutional neural network architecture. The feedforward propagation is used in such a way to perform the classification in voxel level for achieving semantic segmentation. The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. We also demonstrate a case study, in which our method can be effectively used to leverage the extraction of planar surfaces in challenging cluttered indoor environments.

Algorithms used in the Airborne Lidar Processing System (ALPS)

USGS Publications Warehouse

Nagle, David B.; Wright, C. Wayne

2016-05-23

The Airborne Lidar Processing System (ALPS) analyzes Experimental Advanced Airborne Research Lidar (EAARL) data—digitized laser-return waveforms, position, and attitude data—to derive point clouds of target surfaces. A full-waveform airborne lidar system, the EAARL seamlessly and simultaneously collects mixed environment data, including submerged, sub-aerial bare earth, and vegetation-covered topographies.ALPS uses three waveform target-detection algorithms to determine target positions within a given waveform: centroid analysis, leading edge detection, and bottom detection using water-column backscatter modeling. The centroid analysis algorithm detects opaque hard surfaces. The leading edge algorithm detects topography beneath vegetation and shallow, submerged topography. The bottom detection algorithm uses water-column backscatter modeling for deeper submerged topography in turbid water.The report describes slant range calculations and explains how ALPS uses laser range and orientation measurements to project measurement points into the Universal Transverse Mercator coordinate system. Parameters used for coordinate transformations in ALPS are described, as are Interactive Data Language-based methods for gridding EAARL point cloud data to derive digital elevation models. Noise reduction in point clouds through use of a random consensus filter is explained, and detailed pseudocode, mathematical equations, and Yorick source code accompany the report.

Segmentation of Large Unstructured Point Clouds Using Octree-Based Region Growing and Conditional Random Fields

NASA Astrophysics Data System (ADS)

Bassier, M.; Bonduel, M.; Van Genechten, B.; Vergauwen, M.

2017-11-01

Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it is a key aspect in the automated procedure from point cloud data to BIM. Current approaches typically only segment a single type of primitive such as planes or cylinders. Also, current algorithms suffer from oversegmenting the data and are often sensor or scene dependent. In this work, a method is presented to automatically segment large unstructured point clouds of buildings. More specifically, the segmentation is formulated as a graph optimisation problem. First, the data is oversegmented with a greedy octree-based region growing method. The growing is conditioned on the segmentation of planes as well as smooth surfaces. Next, the candidate clusters are represented by a Conditional Random Field after which the most likely configuration of candidate clusters is computed given a set of local and contextual features. The experiments prove that the used method is a fast and reliable framework for unstructured point cloud segmentation. Processing speeds up to 40,000 points per second are recorded for the region growing. Additionally, the recall and precision of the graph clustering is approximately 80%. Overall, nearly 22% of oversegmentation is reduced by clustering the data. These clusters will be classified and used as a basis for the reconstruction of BIM models.

Orientation of airborne laser scanning point clouds with multi-view, multi-scale image blocks.

PubMed

Rönnholm, Petri; Hyyppä, Hannu; Hyyppä, Juha; Haggrén, Henrik

2009-01-01

Comprehensive 3D modeling of our environment requires integration of terrestrial and airborne data, which is collected, preferably, using laser scanning and photogrammetric methods. However, integration of these multi-source data requires accurate relative orientations. In this article, two methods for solving relative orientation problems are presented. The first method includes registration by minimizing the distances between of an airborne laser point cloud and a 3D model. The 3D model was derived from photogrammetric measurements and terrestrial laser scanning points. The first method was used as a reference and for validation. Having completed registration in the object space, the relative orientation between images and laser point cloud is known. The second method utilizes an interactive orientation method between a multi-scale image block and a laser point cloud. The multi-scale image block includes both aerial and terrestrial images. Experiments with the multi-scale image block revealed that the accuracy of a relative orientation increased when more images were included in the block. The orientations of the first and second methods were compared. The comparison showed that correct rotations were the most difficult to detect accurately by using the interactive method. Because the interactive method forces laser scanning data to fit with the images, inaccurate rotations cause corresponding shifts to image positions. However, in a test case, in which the orientation differences included only shifts, the interactive method could solve the relative orientation of an aerial image and airborne laser scanning data repeatedly within a couple of centimeters.

Orientation of Airborne Laser Scanning Point Clouds with Multi-View, Multi-Scale Image Blocks

PubMed Central

Rönnholm, Petri; Hyyppä, Hannu; Hyyppä, Juha; Haggrén, Henrik

2009-01-01

Comprehensive 3D modeling of our environment requires integration of terrestrial and airborne data, which is collected, preferably, using laser scanning and photogrammetric methods. However, integration of these multi-source data requires accurate relative orientations. In this article, two methods for solving relative orientation problems are presented. The first method includes registration by minimizing the distances between of an airborne laser point cloud and a 3D model. The 3D model was derived from photogrammetric measurements and terrestrial laser scanning points. The first method was used as a reference and for validation. Having completed registration in the object space, the relative orientation between images and laser point cloud is known. The second method utilizes an interactive orientation method between a multi-scale image block and a laser point cloud. The multi-scale image block includes both aerial and terrestrial images. Experiments with the multi-scale image block revealed that the accuracy of a relative orientation increased when more images were included in the block. The orientations of the first and second methods were compared. The comparison showed that correct rotations were the most difficult to detect accurately by using the interactive method. Because the interactive method forces laser scanning data to fit with the images, inaccurate rotations cause corresponding shifts to image positions. However, in a test case, in which the orientation differences included only shifts, the interactive method could solve the relative orientation of an aerial image and airborne laser scanning data repeatedly within a couple of centimeters. PMID:22454569

Performance Evaluation of sUAS Equipped with Velodyne HDL-32E LiDAR Sensor

NASA Astrophysics Data System (ADS)

Jozkow, G.; Wieczorek, P.; Karpina, M.; Walicka, A.; Borkowski, A.

2017-08-01

The Velodyne HDL-32E laser scanner is used more frequently as main mapping sensor in small commercial UASs. However, there is still little information about the actual accuracy of point clouds collected with such UASs. This work evaluates empirically the accuracy of the point cloud collected with such UAS. Accuracy assessment was conducted in four aspects: impact of sensors on theoretical point cloud accuracy, trajectory reconstruction quality, and internal and absolute point cloud accuracies. Theoretical point cloud accuracy was evaluated by calculating 3D position error knowing errors of used sensors. The quality of trajectory reconstruction was assessed by comparing position and attitude differences from forward and reverse EKF solution. Internal and absolute accuracies were evaluated by fitting planes to 8 point cloud samples extracted for planar surfaces. In addition, the absolute accuracy was also determined by calculating point 3D distances between LiDAR UAS and reference TLS point clouds. Test data consisted of point clouds collected in two separate flights performed over the same area. Executed experiments showed that in tested UAS, the trajectory reconstruction, especially attitude, has significant impact on point cloud accuracy. Estimated absolute accuracy of point clouds collected during both test flights was better than 10 cm, thus investigated UAS fits mapping-grade category.

A shape-based segmentation method for mobile laser scanning point clouds

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen

2013-07-01

Segmentation of mobile laser point clouds of urban scenes into objects is an important step for post-processing (e.g., interpretation) of point clouds. Point clouds of urban scenes contain numerous objects with significant size variability, complex and incomplete structures, and holes or variable point densities, raising great challenges for the segmentation of mobile laser point clouds. This paper addresses these challenges by proposing a shape-based segmentation method. The proposed method first calculates the optimal neighborhood size of each point to derive the geometric features associated with it, and then classifies the point clouds according to geometric features using support vector machines (SVMs). Second, a set of rules are defined to segment the classified point clouds, and a similarity criterion for segments is proposed to overcome over-segmentation. Finally, the segmentation output is merged based on topological connectivity into a meaningful geometrical abstraction. The proposed method has been tested on point clouds of two urban scenes obtained by different mobile laser scanners. The results show that the proposed method segments large-scale mobile laser point clouds with good accuracy and computationally effective time cost, and that it segments pole-like objects particularly well.

Multi-scale Modeling of Arctic Clouds

NASA Astrophysics Data System (ADS)

Hillman, B. R.; Roesler, E. L.; Dexheimer, D.

2017-12-01

The presence and properties of clouds are critically important to the radiative budget in the Arctic, but clouds are notoriously difficult to represent in global climate models (GCMs). The challenge stems partly from a disconnect in the scales at which these models are formulated and the scale of the physical processes important to the formation of clouds (e.g., convection and turbulence). Because of this, these processes are parameterized in large-scale models. Over the past decades, new approaches have been explored in which a cloud system resolving model (CSRM), or in the extreme a large eddy simulation (LES), is embedded into each gridcell of a traditional GCM to replace the cloud and convective parameterizations to explicitly simulate more of these important processes. This approach is attractive in that it allows for more explicit simulation of small-scale processes while also allowing for interaction between the small and large-scale processes. The goal of this study is to quantify the performance of this framework in simulating Arctic clouds relative to a traditional global model, and to explore the limitations of such a framework using coordinated high-resolution (eddy-resolving) simulations. Simulations from the global model are compared with satellite retrievals of cloud fraction partioned by cloud phase from CALIPSO, and limited-area LES simulations are compared with ground-based and tethered-balloon measurements from the ARM Barrow and Oliktok Point measurement facilities.

Satellite remote sensing and cloud modeling of St. Anthony, Minnesota storm clouds and dew point depression

NASA Technical Reports Server (NTRS)

Hung, R. J.; Tsao, Y. D.

1988-01-01

Rawinsonde data and geosynchronous satellite imagery were used to investigate the life cycles of St. Anthony, Minnesota's severe convective storms. It is found that the fully developed storm clouds, with overshooting cloud tops penetrating above the tropopause, collapsed about three minutes before the touchdown of the tornadoes. Results indicate that the probability of producing an outbreak of tornadoes causing greater damage increases when there are higher values of potential energy storage per unit area for overshooting cloud tops penetrating the tropopause. It is also found that there is less chance for clouds with a lower moisture content to be outgrown as a storm cloud than clouds with a higher moisture content.

Interior Reconstruction Using the 3d Hough Transform

NASA Astrophysics Data System (ADS)

Dumitru, R.-C.; Borrmann, D.; Nüchter, A.

2013-02-01

Laser scanners are often used to create accurate 3D models of buildings for civil engineering purposes, but the process of manually vectorizing a 3D point cloud is time consuming and error-prone (Adan and Huber, 2011). Therefore, the need to characterize and quantify complex environments in an automatic fashion arises, posing challenges for data analysis. This paper presents a system for 3D modeling by detecting planes in 3D point clouds, based on which the scene is reconstructed at a high architectural level through removing automatically clutter and foreground data. The implemented software detects openings, such as windows and doors and completes the 3D model by inpainting.

Challenges for Cloud Modeling in the Context of Aerosol–Cloud–Precipitation Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lebo, Zachary J.; Shipway, Ben J.; Fan, Jiwen

The International Cloud Modeling Workshop (CMW) has been a longstanding tradition in the cloud microphysics modeling community and is typically held the week prior to the International Conference on Clouds and Precipitation (ICCP). For the Ninth CMW, more than 40 participants from 10 countries convened at the Met Office in Exeter, United Kingdom. The workshop included 4 detailed case studies (described in more detail below) rooted in recent field campaigns. The overarching objective of these cases was to utilize new observations to better understand inter-model differences and model deficiencies, explore new modeling techniques, and gain physical insight into the behaviormore » of clouds. As was the case at the Eighth CMW, there was a general theme of understanding the role of aerosol impacts in the context of cloud-precipitation interactions. However, an additional objective was the focal point of several cases at the most recent workshop: microphysical-dynamical interactions. Many of the cases focused less on idealized small-domain simulations (as was the general focus of previous workshops) and more on large-scale nested configurations examining effects at various scales.« less

Automatic Generation of Building Models with Levels of Detail 1-3

NASA Astrophysics Data System (ADS)

Nguatem, W.; Drauschke, M.; Mayer, H.

2016-06-01

We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing (Mayer et al., 2012), we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrate our workflow up to the export into CityGML.

Surface modeling method for aircraft engine blades by using speckle patterns based on the virtual stereo vision system

NASA Astrophysics Data System (ADS)

Yu, Zhijing; Ma, Kai; Wang, Zhijun; Wu, Jun; Wang, Tao; Zhuge, Jingchang

2018-03-01

A blade is one of the most important components of an aircraft engine. Due to its high manufacturing costs, it is indispensable to come up with methods for repairing damaged blades. In order to obtain a surface model of the blades, this paper proposes a modeling method by using speckle patterns based on the virtual stereo vision system. Firstly, blades are sprayed evenly creating random speckle patterns and point clouds from blade surfaces can be calculated by using speckle patterns based on the virtual stereo vision system. Secondly, boundary points are obtained in the way of varied step lengths according to curvature and are fitted to get a blade surface envelope with a cubic B-spline curve. Finally, the surface model of blades is established with the envelope curves and the point clouds. Experimental results show that the surface model of aircraft engine blades is fair and accurate.

2D modeling of direct laser metal deposition process using a finite particle method

NASA Astrophysics Data System (ADS)

Anedaf, T.; Abbès, B.; Abbès, F.; Li, Y. M.

2018-05-01

Direct laser metal deposition is one of the material additive manufacturing processes used to produce complex metallic parts. A thorough understanding of the underlying physical phenomena is required to obtain a high-quality parts. In this work, a mathematical model is presented to simulate the coaxial laser direct deposition process tacking into account of mass addition, heat transfer, and fluid flow with free surface and melting. The fluid flow in the melt pool together with mass and energy balances are solved using the Computational Fluid Dynamics (CFD) software NOGRID-points, based on the meshless Finite Pointset Method (FPM). The basis of the computations is a point cloud, which represents the continuum fluid domain. Each finite point carries all fluid information (density, velocity, pressure and temperature). The dynamic shape of the molten zone is explicitly described by the point cloud. The proposed model is used to simulate a single layer cladding.

Numerical Coupling and Simulation of Point-Mass System with the Turbulent Fluid Flow

NASA Astrophysics Data System (ADS)

Gao, Zheng

A computational framework that combines the Eulerian description of the turbulence field with a Lagrangian point-mass ensemble is proposed in this dissertation. Depending on the Reynolds number, the turbulence field is simulated using Direct Numerical Simulation (DNS) or eddy viscosity model. In the meanwhile, the particle system, such as spring-mass system and cloud droplets, are modeled using the ordinary differential system, which is stiff and hence poses a challenge to the stability of the entire system. This computational framework is applied to the numerical study of parachute deceleration and cloud microphysics. These two distinct problems can be uniformly modeled with Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs), and numerically solved in the same framework. For the parachute simulation, a novel porosity model is proposed to simulate the porous effects of the parachute canopy. This model is easy to implement with the projection method and is able to reproduce Darcy's law observed in the experiment. Moreover, the impacts of using different versions of k-epsilon turbulence model in the parachute simulation have been investigated and conclude that the standard and Re-Normalisation Group (RNG) model may overestimate the turbulence effects when Reynolds number is small while the Realizable model has a consistent performance with both large and small Reynolds number. For another application, cloud microphysics, the cloud entrainment-mixing problem is studied in the same numerical framework. Three sets of DNS are carried out with both decaying and forced turbulence. The numerical result suggests a new way parameterize the cloud mixing degree using the dynamical measures. The numerical experiments also verify the negative relationship between the droplets number concentration and the vorticity field. The results imply that the gravity has fewer impacts on the forced turbulence than the decaying turbulence. In summary, the proposed framework can be used to solve a physics problem that involves turbulence field and point-mass system, and therefore has a broad application.

Street curb recognition in 3d point cloud data using morphological operations

NASA Astrophysics Data System (ADS)

Rodríguez-Cuenca, Borja; Concepción Alonso-Rodríguez, María; García-Cortés, Silverio; Ordóñez, Celestino

2015-04-01

Accurate and automatic detection of cartographic-entities saves a great deal of time and money when creating and updating cartographic databases. The current trend in remote sensing feature extraction is to develop methods that are as automatic as possible. The aim is to develop algorithms that can obtain accurate results with the least possible human intervention in the process. Non-manual curb detection is an important issue in road maintenance, 3D urban modeling, and autonomous navigation fields. This paper is focused on the semi-automatic recognition of curbs and street boundaries using a 3D point cloud registered by a mobile laser scanner (MLS) system. This work is divided into four steps. First, a coordinate system transformation is carried out, moving from a global coordinate system to a local one. After that and in order to simplify the calculations involved in the procedure, a rasterization based on the projection of the measured point cloud on the XY plane was carried out, passing from the 3D original data to a 2D image. To determine the location of curbs in the image, different image processing techniques such as thresholding and morphological operations were applied. Finally, the upper and lower edges of curbs are detected by an unsupervised classification algorithm on the curvature and roughness of the points that represent curbs. The proposed method is valid in both straight and curved road sections and applicable both to laser scanner and stereo vision 3D data due to the independence of its scanning geometry. This method has been successfully tested with two datasets measured by different sensors. The first dataset corresponds to a point cloud measured by a TOPCON sensor in the Spanish town of Cudillero. That point cloud comprises more than 6,000,000 points and covers a 400-meter street. The second dataset corresponds to a point cloud measured by a RIEGL sensor in the Austrian town of Horn. That point cloud comprises 8,000,000 points and represents a 160-meter street. The proposed method provides success rates in curb recognition of over 85% in both datasets.

Automated extraction and analysis of rock discontinuity characteristics from 3D point clouds

NASA Astrophysics Data System (ADS)

Bianchetti, Matteo; Villa, Alberto; Agliardi, Federico; Crosta, Giovanni B.

2016-04-01

A reliable characterization of fractured rock masses requires an exhaustive geometrical description of discontinuities, including orientation, spacing, and size. These are required to describe discontinuum rock mass structure, perform Discrete Fracture Network and DEM modelling, or provide input for rock mass classification or equivalent continuum estimate of rock mass properties. Although several advanced methodologies have been developed in the last decades, a complete characterization of discontinuity geometry in practice is still challenging, due to scale-dependent variability of fracture patterns and difficult accessibility to large outcrops. Recent advances in remote survey techniques, such as terrestrial laser scanning and digital photogrammetry, allow a fast and accurate acquisition of dense 3D point clouds, which promoted the development of several semi-automatic approaches to extract discontinuity features. Nevertheless, these often need user supervision on algorithm parameters which can be difficult to assess. To overcome this problem, we developed an original Matlab tool, allowing fast, fully automatic extraction and analysis of discontinuity features with no requirements on point cloud accuracy, density and homogeneity. The tool consists of a set of algorithms which: (i) process raw 3D point clouds, (ii) automatically characterize discontinuity sets, (iii) identify individual discontinuity surfaces, and (iv) analyse their spacing and persistence. The tool operates in either a supervised or unsupervised mode, starting from an automatic preliminary exploration data analysis. The identification and geometrical characterization of discontinuity features is divided in steps. First, coplanar surfaces are identified in the whole point cloud using K-Nearest Neighbor and Principal Component Analysis algorithms optimized on point cloud accuracy and specified typical facet size. Then, discontinuity set orientation is calculated using Kernel Density Estimation and principal vector similarity criteria. Poles to points are assigned to individual discontinuity objects using easy custom vector clustering and Jaccard distance approaches, and each object is segmented into planar clusters using an improved version of the DBSCAN algorithm. Modal set orientations are then recomputed by cluster-based orientation statistics to avoid the effects of biases related to cluster size and density heterogeneity of the point cloud. Finally, spacing values are measured between individual discontinuity clusters along scanlines parallel to modal pole vectors, whereas individual feature size (persistence) is measured using 3D convex hull bounding boxes. Spacing and size are provided both as raw population data and as summary statistics. The tool is optimized for parallel computing on 64bit systems, and a Graphic User Interface (GUI) has been developed to manage data processing, provide several outputs, including reclassified point clouds, tables, plots, derived fracture intensity parameters, and export to modelling software tools. We present test applications performed both on synthetic 3D data (simple 3D solids) and real case studies, validating the results with existing geomechanical datasets.

Feature-constrained surface reconstruction approach for point cloud data acquired with 3D laser scanner

NASA Astrophysics Data System (ADS)

Wang, Yongbo; Sheng, Yehua; Lu, Guonian; Tian, Peng; Zhang, Kai

2008-04-01

Surface reconstruction is an important task in the field of 3d-GIS, computer aided design and computer graphics (CAD & CG), virtual simulation and so on. Based on available incremental surface reconstruction methods, a feature-constrained surface reconstruction approach for point cloud is presented. Firstly features are extracted from point cloud under the rules of curvature extremes and minimum spanning tree. By projecting local sample points to the fitted tangent planes and using extracted features to guide and constrain the process of local triangulation and surface propagation, topological relationship among sample points can be achieved. For the constructed models, a process named consistent normal adjustment and regularization is adopted to adjust normal of each face so that the correct surface model is achieved. Experiments show that the presented approach inherits the convenient implementation and high efficiency of traditional incremental surface reconstruction method, meanwhile, it avoids improper propagation of normal across sharp edges, which means the applicability of incremental surface reconstruction is greatly improved. Above all, appropriate k-neighborhood can help to recognize un-sufficient sampled areas and boundary parts, the presented approach can be used to reconstruct both open and close surfaces without additional interference.

LSAH: a fast and efficient local surface feature for point cloud registration

NASA Astrophysics Data System (ADS)

Lu, Rongrong; Zhu, Feng; Wu, Qingxiao; Kong, Yanzi

2018-04-01

Point cloud registration is a fundamental task in high level three dimensional applications. Noise, uneven point density and varying point cloud resolutions are the three main challenges for point cloud registration. In this paper, we design a robust and compact local surface descriptor called Local Surface Angles Histogram (LSAH) and propose an effectively coarse to fine algorithm for point cloud registration. The LSAH descriptor is formed by concatenating five normalized sub-histograms into one histogram. The five sub-histograms are created by accumulating a different type of angle from a local surface patch respectively. The experimental results show that our LSAH is more robust to uneven point density and point cloud resolutions than four state-of-the-art local descriptors in terms of feature matching. Moreover, we tested our LSAH based coarse to fine algorithm for point cloud registration. The experimental results demonstrate that our algorithm is robust and efficient as well.

Gemini Planet Imager Spectroscopy of the HR 8799 Planets c and d

DOE PAGES

Ingraham, Patrick; Marley, Mark S.; Saumon, Didier; ...

2014-09-30

During the first-light run of the Gemini Planet Imager we obtained K-band spectra of exoplanets HR 8799 c and d. Analysis of the spectra indicates that planet d may be warmer than planet c. Comparisons to recent patchy cloud models and previously obtained observations over multiple wavelengths confirm that thick clouds combined with horizontal variation in the cloud cover generally reproduce the planets’ spectral energy distributions.When combined with the 3 to 4μm photometric data points, the observations provide strong constraints on the atmospheric methane content for both planets. Lastly, the data also provide further evidence that future modeling efforts mustmore » include cloud opacity, possibly including cloud holes, disequilibrium chemistry, and super-solar metallicity.« less

Deformation analysis of a sinkhole in Thuringia using multi-temporal multi-view stereo 3D reconstruction data

NASA Astrophysics Data System (ADS)

Petschko, Helene; Goetz, Jason; Schmidt, Sven

2017-04-01

Sinkholes are a serious threat on life, personal property and infrastructure in large parts of Thuringia. Over 9000 sinkholes have been documented by the Geological Survey of Thuringia, which are caused by collapsing hollows which formed due to solution processes within the local bedrock material. However, little is known about surface processes and their dynamics at the flanks of the sinkhole once the sinkhole has shaped. These processes are of high interest as they might lead to dangerous situations at or within the vicinity of the sinkhole. Our objective was the analysis of these deformations over time in 3D by applying terrestrial photogrammetry with a simple DSLR camera. Within this study, we performed an analysis of deformations within a sinkhole close to Bad Frankenhausen (Thuringia) using terrestrial photogrammetry and multi-view stereo 3D reconstruction to obtain a 3D point cloud describing the morphology of the sinkhole. This was performed for multiple data collection campaigns over a 6-month period. The photos of the sinkhole were taken with a Nikon D3000 SLR Camera. For the comparison of the point clouds the Multiscale Model to Model Comparison (M3C2) plugin of the software CloudCompare was used. It allows to apply advanced methods of point cloud difference calculation which considers the co-registration error between two point clouds for assessing the significance of the calculated difference (given in meters). Three Styrofoam cuboids of known dimensions (16 cm wide/29 cm high/11.5 cm deep) were placed within the sinkhole to test the accuracy of the point cloud difference calculation. The multi-view stereo 3D reconstruction was performed with Agisoft Photoscan. Preliminary analysis indicates that about 26% of the sinkhole showed changes exceeding the co-registration error of the point clouds. The areas of change can mainly be detected on the flanks of the sinkhole and on an earth pillar that formed in the center of the sinkhole. These changes describe toppling (positive change of a few centimeters at the earth pillar) and a few erosion processes along the flanks (negative change of a few centimeters) compared to the first date of data acquisition. Additionally, the Styrofoam cuboids have successfully been detected with an observed depth change of 10 cm. However, the limitations of this approach related to the co-registration of the point clouds and data acquisition (windy conditions) have to be analyzed in more detail.

Cirrus cloud model parameterizations: Incorporating realistic ice particle generation

NASA Technical Reports Server (NTRS)

Sassen, Kenneth; Dodd, G. C.; Starr, David OC.

1990-01-01

Recent cirrus cloud modeling studies have involved the application of a time-dependent, two dimensional Eulerian model, with generalized cloud microphysical parameterizations drawn from experimental findings. For computing the ice versus vapor phase changes, the ice mass content is linked to the maintenance of a relative humidity with respect to ice (RHI) of 105 percent; ice growth occurs both with regard to the introduction of new particles and the growth of existing particles. In a simplified cloud model designed to investigate the basic role of various physical processes in the growth and maintenance of cirrus clouds, these parametric relations are justifiable. In comparison, the one dimensional cloud microphysical model recently applied to evaluating the nucleation and growth of ice crystals in cirrus clouds explicitly treated populations of haze and cloud droplets, and ice crystals. Although these two modeling approaches are clearly incompatible, the goal of the present numerical study is to develop a parametric treatment of new ice particle generation, on the basis of detailed microphysical model findings, for incorporation into improved cirrus growth models. For example, the relation between temperature and the relative humidity required to generate ice crystals from ammonium sulfate haze droplets, whose probability of freezing through the homogeneous nucleation mode are a combined function of time and droplet molality, volume, and temperature. As an example of this approach, the results of cloud microphysical simulations are presented showing the rather narrow domain in the temperature/humidity field where new ice crystals can be generated. The microphysical simulations point out the need for detailed CCN studies at cirrus altitudes and haze droplet measurements within cirrus clouds, but also suggest that a relatively simple treatment of ice particle generation, which includes cloud chemistry, can be incorporated into cirrus cloud growth.

Combining structure-from-motion derived point clouds from satellites and unmanned aircraft systems images with ground-truth data to create high-resolution digital elevation models

NASA Astrophysics Data System (ADS)

Palaseanu, M.; Thatcher, C.; Danielson, J.; Gesch, D. B.; Poppenga, S.; Kottermair, M.; Jalandoni, A.; Carlson, E.

2016-12-01

Coastal topographic and bathymetric (topobathymetric) data with high spatial resolution (1-meter or better) and high vertical accuracy are needed to assess the vulnerability of Pacific Islands to climate change impacts, including sea level rise. According to the Intergovernmental Panel on Climate Change reports, low-lying atolls in the Pacific Ocean are extremely vulnerable to king tide events, storm surge, tsunamis, and sea-level rise. The lack of coastal topobathymetric data has been identified as a critical data gap for climate vulnerability and adaptation efforts in the Republic of the Marshall Islands (RMI). For Majuro Atoll, home to the largest city of RMI, the only elevation dataset currently available is the Shuttle Radar Topography Mission data which has a 30-meter spatial resolution and 16-meter vertical accuracy (expressed as linear error at 90%). To generate high-resolution digital elevation models (DEMs) in the RMI, elevation information and photographic imagery have been collected from field surveys using GNSS/total station and unmanned aerial vehicles for Structure-from-Motion (SfM) point cloud generation. Digital Globe WorldView II imagery was processed to create SfM point clouds to fill in gaps in the point cloud derived from the higher resolution UAS photos. The combined point cloud data is filtered and classified to bare-earth and georeferenced using the GNSS data acquired on roads and along survey transects perpendicular to the coast. A total station was used to collect elevation data under tree canopies where heavy vegetation cover blocked the view of GNSS satellites. A subset of the GPS / total station data was set aside for error assessment of the resulting DEM.

Outcrop-scale fracture trace identification using surface roughness derived from a high-density point cloud

NASA Astrophysics Data System (ADS)

Okyay, U.; Glennie, C. L.; Khan, S.

2017-12-01

Owing to the advent of terrestrial laser scanners (TLS), high-density point cloud data has become increasingly available to the geoscience research community. Research groups have started producing their own point clouds for various applications, gradually shifting their emphasis from obtaining the data towards extracting more and meaningful information from the point clouds. Extracting fracture properties from three-dimensional data in a (semi-)automated manner has been an active area of research in geosciences. Several studies have developed various processing algorithms for extracting only planar surfaces. In comparison, (semi-)automated identification of fracture traces at the outcrop scale, which could be used for mapping fracture distribution have not been investigated frequently. Understanding the spatial distribution and configuration of natural fractures is of particular importance, as they directly influence fluid-flow through the host rock. Surface roughness, typically defined as the deviation of a natural surface from a reference datum, has become an important metric in geoscience research, especially with the increasing density and accuracy of point clouds. In the study presented herein, a surface roughness model was employed to identify fracture traces and their distribution on an ophiolite outcrop in Oman. Surface roughness calculations were performed using orthogonal distance regression over various grid intervals. The results demonstrated that surface roughness could identify outcrop-scale fracture traces from which fracture distribution and density maps can be generated. However, considering outcrop conditions and properties and the purpose of the application, the definition of an adequate grid interval for surface roughness model and selection of threshold values for distribution maps are not straightforward and require user intervention and interpretation.

A hierarchical methodology for urban facade parsing from TLS point clouds

NASA Astrophysics Data System (ADS)

Li, Zhuqiang; Zhang, Liqiang; Mathiopoulos, P. Takis; Liu, Fangyu; Zhang, Liang; Li, Shuaipeng; Liu, Hao

2017-01-01

The effective and automated parsing of building facades from terrestrial laser scanning (TLS) point clouds of urban environments is an important research topic in the GIS and remote sensing fields. It is also challenging because of the complexity and great variety of the available 3D building facade layouts as well as the noise and data missing of the input TLS point clouds. In this paper, we introduce a novel methodology for the accurate and computationally efficient parsing of urban building facades from TLS point clouds. The main novelty of the proposed methodology is that it is a systematic and hierarchical approach that considers, in an adaptive way, the semantic and underlying structures of the urban facades for segmentation and subsequent accurate modeling. Firstly, the available input point cloud is decomposed into depth planes based on a data-driven method; such layer decomposition enables similarity detection in each depth plane layer. Secondly, the labeling of the facade elements is performed using the SVM classifier in combination with our proposed BieS-ScSPM algorithm. The labeling outcome is then augmented with weak architectural knowledge. Thirdly, least-squares fitted normalized gray accumulative curves are applied to detect regular structures, and a binarization dilation extraction algorithm is used to partition facade elements. A dynamic line-by-line division is further applied to extract the boundaries of the elements. The 3D geometrical façade models are then reconstructed by optimizing facade elements across depth plane layers. We have evaluated the performance of the proposed method using several TLS facade datasets. Qualitative and quantitative performance comparisons with several other state-of-the-art methods dealing with the same facade parsing problem have demonstrated its superiority in performance and its effectiveness in improving segmentation accuracy.

Automatic 3d Building Model Generations with Airborne LiDAR Data

NASA Astrophysics Data System (ADS)

Yastikli, N.; Cetin, Z.

2017-11-01

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data.

First Prismatic Building Model Reconstruction from Tomosar Point Clouds

NASA Astrophysics Data System (ADS)

Sun, Y.; Shahzad, M.; Zhu, X.

2016-06-01

This paper demonstrates for the first time the potential of explicitly modelling the individual roof surfaces to reconstruct 3-D prismatic building models using spaceborne tomographic synthetic aperture radar (TomoSAR) point clouds. The proposed approach is modular and works as follows: it first extracts the buildings via DSM generation and cutting-off the ground terrain. The DSM is smoothed using BM3D denoising method proposed in (Dabov et al., 2007) and a gradient map of the smoothed DSM is generated based on height jumps. Watershed segmentation is then adopted to oversegment the DSM into different regions. Subsequently, height and polygon complexity constrained merging is employed to refine (i.e., to reduce) the retrieved number of roof segments. Coarse outline of each roof segment is then reconstructed and later refined using quadtree based regularization plus zig-zag line simplification scheme. Finally, height is associated to each refined roof segment to obtain the 3-D prismatic model of the building. The proposed approach is illustrated and validated over a large building (convention center) in the city of Las Vegas using TomoSAR point clouds generated from a stack of 25 images using Tomo-GENESIS software developed at DLR.

D Model of AL Zubarah Fortress in Qatar - Terrestrial Laser Scanning VS. Dense Image Matching

NASA Astrophysics Data System (ADS)

Kersten, T.; Mechelke, K.; Maziull, L.

2015-02-01

In September 2011 the fortress Al Zubarah, built in 1938 as a typical Arabic fortress and restored in 1987 as a museum, was recorded by the HafenCity University Hamburg using terrestrial laser scanning with the IMAGER 5006h and digital photogrammetry for the Qatar Museum Authority within the framework of the Qatar Islamic Archaeology and Heritage Project. One goal of the object recording was to provide detailed 2D/3D documentation of the fortress. This was used to complete specific detailed restoration work in the recent years. From the registered laser scanning point clouds several cuttings and 2D plans were generated as well as a 3D surface model by triangle meshing. Additionally, point clouds and surface models were automatically generated from digital imagery from a Nikon D70 using the open-source software Bundler/PMVS2, free software VisualSFM, Autodesk Web Service 123D Catch beta, and low-cost software Agisoft PhotoScan. These outputs were compared with the results from terrestrial laser scanning. The point clouds and surface models derived from imagery could not achieve the same quality of geometrical accuracy as laser scanning (i.e. 1-2 cm).

Colour computer-generated holography for point clouds utilizing the Phong illumination model.

PubMed

Symeonidou, Athanasia; Blinder, David; Schelkens, Peter

2018-04-16

A technique integrating the bidirectional reflectance distribution function (BRDF) is proposed to generate realistic high-quality colour computer-generated holograms (CGHs). We build on prior work, namely a fast computer-generated holography method for point clouds that handles occlusions. We extend the method by integrating the Phong illumination model so that the properties of the objects' surfaces are taken into account to achieve natural light phenomena such as reflections and shadows. Our experiments show that rendering holograms with the proposed algorithm provides realistic looking objects without any noteworthy increase to the computational cost.

A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds

PubMed Central

Dorninger, Peter; Pfeifer, Norbert

2008-01-01

Three dimensional city models are necessary for supporting numerous management applications. For the determination of city models for visualization purposes, several standardized workflows do exist. They are either based on photogrammetry or on LiDAR or on a combination of both data acquisition techniques. However, the automated determination of reliable and highly accurate city models is still a challenging task, requiring a workflow comprising several processing steps. The most relevant are building detection, building outline generation, building modeling, and finally, building quality analysis. Commercial software tools for building modeling require, generally, a high degree of human interaction and most automated approaches described in literature stress the steps of such a workflow individually. In this article, we propose a comprehensive approach for automated determination of 3D city models from airborne acquired point cloud data. It is based on the assumption that individual buildings can be modeled properly by a composition of a set of planar faces. Hence, it is based on a reliable 3D segmentation algorithm, detecting planar faces in a point cloud. This segmentation is of crucial importance for the outline detection and for the modeling approach. We describe the theoretical background, the segmentation algorithm, the outline detection, and the modeling approach, and we present and discuss several actual projects. PMID:27873931

3D Reconstruction of Space Objects from Multi-Views by a Visible Sensor

PubMed Central

Zhang, Haopeng; Wei, Quanmao; Jiang, Zhiguo

2017-01-01

In this paper, a novel 3D reconstruction framework is proposed to recover the 3D structural model of a space object from its multi-view images captured by a visible sensor. Given an image sequence, this framework first estimates the relative camera poses and recovers the depths of the surface points by the structure from motion (SFM) method, then the patch-based multi-view stereo (PMVS) algorithm is utilized to generate a dense 3D point cloud. To resolve the wrong matches arising from the symmetric structure and repeated textures of space objects, a new strategy is introduced, in which images are added to SFM in imaging order. Meanwhile, a refining process exploiting the structural prior knowledge that most sub-components of artificial space objects are composed of basic geometric shapes is proposed and applied to the recovered point cloud. The proposed reconstruction framework is tested on both simulated image datasets and real image datasets. Experimental results illustrate that the recovered point cloud models of space objects are accurate and have a complete coverage of the surface. Moreover, outliers and points with severe noise are effectively filtered out by the refinement, resulting in an distinct improvement of the structure and visualization of the recovered points. PMID:28737675

Fast grasping of unknown objects using cylinder searching on a single point cloud

NASA Astrophysics Data System (ADS)

Lei, Qujiang; Wisse, Martijn

2017-03-01

Grasping of unknown objects with neither appearance data nor object models given in advance is very important for robots that work in an unfamiliar environment. The goal of this paper is to quickly synthesize an executable grasp for one unknown object by using cylinder searching on a single point cloud. Specifically, a 3D camera is first used to obtain a partial point cloud of the target unknown object. An original method is then employed to do post treatment on the partial point cloud to minimize the uncertainty which may lead to grasp failure. In order to accelerate the grasp searching, surface normal of the target object is then used to constrain the synthetization of the cylinder grasp candidates. Operability analysis is then used to select out all executable grasp candidates followed by force balance optimization to choose the most reliable grasp as the final grasp execution. In order to verify the effectiveness of our algorithm, Simulations on a Universal Robot arm UR5 and an under-actuated Lacquey Fetch gripper are used to examine the performance of this algorithm, and successful results are obtained.

- and Scene-Guided Integration of Tls and Photogrammetric Point Clouds for Landslide Monitoring

NASA Astrophysics Data System (ADS)

Zieher, T.; Toschi, I.; Remondino, F.; Rutzinger, M.; Kofler, Ch.; Mejia-Aguilar, A.; Schlögel, R.

2018-05-01

Terrestrial and airborne 3D imaging sensors are well-suited data acquisition systems for the area-wide monitoring of landslide activity. State-of-the-art surveying techniques, such as terrestrial laser scanning (TLS) and photogrammetry based on unmanned aerial vehicle (UAV) imagery or terrestrial acquisitions have advantages and limitations associated with their individual measurement principles. In this study we present an integration approach for 3D point clouds derived from these techniques, aiming at improving the topographic representation of landslide features while enabling a more accurate assessment of landslide-induced changes. Four expert-based rules involving local morphometric features computed from eigenvectors, elevation and the agreement of the individual point clouds, are used to choose within voxels of selectable size which sensor's data to keep. Based on the integrated point clouds, digital surface models and shaded reliefs are computed. Using an image correlation technique, displacement vectors are finally derived from the multi-temporal shaded reliefs. All results show comparable patterns of landslide movement rates and directions. However, depending on the applied integration rule, differences in spatial coverage and correlation strength emerge.

Application of Template Matching for Improving Classification of Urban Railroad Point Clouds

PubMed Central

Arastounia, Mostafa; Oude Elberink, Sander

2016-01-01

This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are four rail tracks, two contact cables, and two catenary cables. The dataset includes only geometrical information (three dimensional (3D) coordinates of the points) with no intensity data and no RGB data. The obtained results indicate that all objects of interest are successfully classified at the object level with no false positives and no false negatives. The results also show that an average 97.3% precision and an average 97.7% accuracy at the point cloud level are achieved. The high precision and high accuracy of the rail track classification (both greater than 96%) at the point cloud level stems from the great impact of the employed template matching method on excluding the false positives. The cables also achieve quite high average precision (96.8%) and accuracy (98.4%) due to their high sampling and isolated position in the railroad corridor. PMID:27973452

2.5D multi-view gait recognition based on point cloud registration.

PubMed

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-03-28

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM.

Motion data classification on the basis of dynamic time warping with a cloud point distance measure

NASA Astrophysics Data System (ADS)

Switonski, Adam; Josinski, Henryk; Zghidi, Hafedh; Wojciechowski, Konrad

2016-06-01

The paper deals with the problem of classification of model free motion data. The nearest neighbors classifier which is based on comparison performed by Dynamic Time Warping transform with cloud point distance measure is proposed. The classification utilizes both specific gait features reflected by a movements of subsequent skeleton joints and anthropometric data. To validate proposed approach human gait identification challenge problem is taken into consideration. The motion capture database containing data of 30 different humans collected in Human Motion Laboratory of Polish-Japanese Academy of Information Technology is used. The achieved results are satisfactory, the obtained accuracy of human recognition exceeds 90%. What is more, the applied cloud point distance measure does not depend on calibration process of motion capture system which results in reliable validation.

On the hydrophilicity of polyzwitterion poly (N,N-dimethyl-N-(3-(methacrylamido)propyl)ammoniopropane sulfonate) in water, deuterated water, and aqueous salt solutions.

PubMed

Hildebrand, Viet; Laschewsky, André; Zehm, Daniel

2014-01-01

A series of zwitterionic model polymers with defined molar masses up to 150,000 Da and defined end groups are prepared from sulfobetaine monomer N,N-dimethyl-N-(3-(methacrylamido)propyl)ammoniopropanesulfonate (SPP). Polymers are synthesized by reversible addition-fragmentation chain transfer polymerization (RAFT) using a functional chain transfer agent labeled with a fluorescent probe. Their upper critical solution temperature-type coil-to-globule phase transition in water, deuterated water, and various salt solutions is studied by turbidimetry. Cloud points increase with polyzwitterion concentration and molar mass, being considerably higher in D2O than in H2O. Moreover, cloud points are strongly affected by the amount and nature of added salts. Typically, they increase with increasing salt concentration up to a maximum value, whereas further addition of salt lowers the cloud points again, mostly down to below freezing point. The different salting-in and salting-out effects of the studied anions can be correlated with the Hofmeister series. In physiological sodium chloride solution and in phosphate buffered saline (PBS), the cloud point is suppressed even for high molar mass samples. Accordingly, SPP-polymers behave strongly hydrophilic under most conditions encountered in biomedical applications. However, the direct transfer of results from model studies in D2O, using, e.g. (1)H NMR or neutron scattering techniques, to 'normal' systems in H2O is not obvious.

Evaluating cloudiness in an AGCM with Cloud Vertical Structure classes and their radiative effects

NASA Astrophysics Data System (ADS)

Lee, D.; Cho, N.; Oreopoulos, L.; Barahona, D.

2017-12-01

Clouds are recognized not only as the main modulator of Earth's Radiation Budget but also as the atmospheric constituent carrying the largest uncertainty in future climate projections. The presentation will showcase a new framework for evaluating clouds and their radiative effects in Atmospheric Global Climate Models (AGCMs) using Cloud Vertical Structure (CVS) classes. We take advantage of a new CVS reference dataset recently created from CloudSat's 2B-CLDCLASS-LIDAR product and which assigns observed cloud vertical configurations to nine simplified CVS classes based on cloud co-occurrence in three standard atmospheric layers. These CVS classes can also be emulated in GEOS-5 using the subcolumn cloud generator currently paired with the RRTMG radiation package as an implementation of the McICA scheme. Comparisons between the observed and modeled climatologies of the frequency of occurrence of the various CVS classes provide a new vantage point for assessing the realism of GEOS-5 clouds. Furthermore, a comparison between observed and modeled cloud radiative effects according to their CVS is also possible thanks to the availability of CloudSat's 2B-FLXHR-LIDAR product and our ability to composite radiative fluxes by CVS class - both in the observed and modeled realm. This latter effort enables an investigation of whether the contribution of the various CVS classes to the Earth's radiation budget is represented realistically in GEOS-5. Making this new pathway of cloud evaluation available to the community is a major step towards the improved representation of clouds in climate models.

Exploring noctilucent cloud variability using the nudged and extended version of the Canadian Middle Atmosphere Model

NASA Astrophysics Data System (ADS)

Kuilman, Maartje; Karlsson, Bodil; Benze, Susanne; Megner, Linda

2017-11-01

Ice particles in the summer mesosphere - such as those connected to noctilucent clouds and polar mesospheric summer echoes - have since their discovery contributed to the uncovering of atmospheric processes on various scales ranging from interactions on molecular levels to global scale circulation patterns. While there are numerous model studies on mesospheric ice microphysics and how the clouds relate to the background atmosphere, there are at this point few studies using comprehensive global climate models to investigate observed variability and climatology of noctilucent clouds. In this study it is explored to what extent the large-scale inter-annual characteristics of noctilucent clouds are captured in a 30-year run - extending from 1979 to 2009 - of the nudged and extended version of the Canadian Middle Atmosphere Model (CMAM30). To construct and investigate zonal mean inter-seasonal variability in noctilucent cloud occurrence frequency and ice mass density in both hemispheres, a simple cloud model is applied in which it is assumed that the ice content is solely controlled by the local temperature and water vapor volume mixing ratio. The model results are compared to satellite observations, each having an instrument-specific sensitivity when it comes to detecting noctilucent clouds. It is found that the model is able to capture the onset dates of the NLC seasons in both hemispheres as well as the hemispheric differences in NLCs, such as weaker NLCs in the SH than in the NH and differences in cloud height. We conclude that the observed cloud climatology and zonal mean variability are well captured by the model.

Towards semi-automatic rock mass discontinuity orientation and set analysis from 3D point clouds

NASA Astrophysics Data System (ADS)

Guo, Jiateng; Liu, Shanjun; Zhang, Peina; Wu, Lixin; Zhou, Wenhui; Yu, Yinan

2017-06-01

Obtaining accurate information on rock mass discontinuities for deformation analysis and the evaluation of rock mass stability is important. Obtaining measurements for high and steep zones with the traditional compass method is difficult. Photogrammetry, three-dimensional (3D) laser scanning and other remote sensing methods have gradually become mainstream methods. In this study, a method that is based on a 3D point cloud is proposed to semi-automatically extract rock mass structural plane information. The original data are pre-treated prior to segmentation by removing outlier points. The next step is to segment the point cloud into different point subsets. Various parameters, such as the normal, dip/direction and dip, can be calculated for each point subset after obtaining the equation of the best fit plane for the relevant point subset. A cluster analysis (a point subset that satisfies some conditions and thus forms a cluster) is performed based on the normal vectors by introducing the firefly algorithm (FA) and the fuzzy c-means (FCM) algorithm. Finally, clusters that belong to the same discontinuity sets are merged and coloured for visualization purposes. A prototype system is developed based on this method to extract the points of the rock discontinuity from a 3D point cloud. A comparison with existing software shows that this method is feasible. This method can provide a reference for rock mechanics, 3D geological modelling and other related fields.

Stochastic Surface Mesh Reconstruction

NASA Astrophysics Data System (ADS)

Ozendi, M.; Akca, D.; Topan, H.

2018-05-01

A generic and practical methodology is presented for 3D surface mesh reconstruction from the terrestrial laser scanner (TLS) derived point clouds. It has two main steps. The first step deals with developing an anisotropic point error model, which is capable of computing the theoretical precisions of 3D coordinates of each individual point in the point cloud. The magnitude and direction of the errors are represented in the form of error ellipsoids. The following second step is focused on the stochastic surface mesh reconstruction. It exploits the previously determined error ellipsoids by computing a point-wise quality measure, which takes into account the semi-diagonal axis length of the error ellipsoid. The points only with the least errors are used in the surface triangulation. The remaining ones are automatically discarded.

ATLAS Cloud R&D

NASA Astrophysics Data System (ADS)

Panitkin, Sergey; Barreiro Megino, Fernando; Caballero Bejar, Jose; Benjamin, Doug; Di Girolamo, Alessandro; Gable, Ian; Hendrix, Val; Hover, John; Kucharczyk, Katarzyna; Medrano Llamas, Ramon; Love, Peter; Ohman, Henrik; Paterson, Michael; Sobie, Randall; Taylor, Ryan; Walker, Rodney; Zaytsev, Alexander; Atlas Collaboration

2014-06-01

The computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distributed computing. ATLAS sees grid and cloud computing as complementary technologies that will coexist at different levels of resource abstraction, and two years ago created an R&D working group to investigate the different integration scenarios. The ATLAS Cloud Computing R&D has been able to demonstrate the feasibility of offloading work from grid to cloud sites and, as of today, is able to integrate transparently various cloud resources into the PanDA workload management system. The ATLAS Cloud Computing R&D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R&D group has gained a significant insight into the cloud computing landscape and has identified points that still need to be addressed in order to fully utilize this technology. This contribution will explain the cloud integration models that are being evaluated and will discuss ATLAS' learning during the collaboration with leading commercial and academic cloud providers.

New Concepts for Refinement of Cumulus Parameterization in GCM's the Arakawa-Schubert Framework

NASA Technical Reports Server (NTRS)

Sud, Y. C.; Walker, G. K.; Lau, William (Technical Monitor)

2002-01-01

Several state-of-the-art models including the one employed in this study use the Arakawa-Schubert framework for moist convection, and Sundqvist formulation of stratiform. clouds, for moist physics, in-cloud condensation, and precipitation. Despite a variety of cloud parameterization methodologies developed by several modelers including the authors, most of the parameterized cloud-models have similar deficiencies. These consist of: (a) not enough shallow clouds, (b) too many deep clouds; (c) several layers of clouds in a vertically demoralized model as opposed to only a few levels of observed clouds, and (d) higher than normal incidence of double ITCZ (Inter-tropical Convergence Zone). Even after several upgrades consisting of a sophisticated cloud-microphysics and sub-grid scale orographic precipitation into the Data Assimilation Office (DAO)'s atmospheric model (called GEOS-2 GCM) at two different resolutions, we found that the above deficiencies remained persistent. The two empirical solutions often used to counter the aforestated deficiencies consist of a) diffusion of moisture and heat within the lower troposphere to artificially force the shallow clouds; and b) arbitrarily invoke evaporation of in-cloud water for low-level clouds. Even though helpful, these implementations lack a strong physical rationale. Our research shows that two missing physical conditions can ameliorate the aforestated cloud-parameterization deficiencies. First, requiring an ascending cloud airmass to be saturated at its starting point will not only make the cloud instantly buoyant all through its ascent, but also provide the essential work function (buoyancy energy) that would promote more shallow clouds. Second, we argue that training clouds that are unstable to a finite vertical displacement, even if neutrally buoyant in their ambient environment, must continue to rise and entrain causing evaporation of in-cloud water. These concepts have not been invoked in any of the cloud parameterization schemes so far. We introduced them into the DAO-GEOS-2 GCM with McRAS (Microphysics of Clouds with Relaxed Arakawa-Schubert Scheme).

Retrieval of effective cloud field parameters from radiometric data

NASA Astrophysics Data System (ADS)

Paulescu, Marius; Badescu, Viorel; Brabec, Marek

2017-06-01

Clouds play a key role in establishing the Earth's climate. Real cloud fields are very different and very complex in both morphological and microphysical senses. Consequently, the numerical description of the cloud field is a critical task for accurate climate modeling. This study explores the feasibility of retrieving the effective cloud field parameters (namely the cloud aspect ratio and cloud factor) from systematic radiometric measurements at high frequency (measurement is taken every 15 s). Two different procedures are proposed, evaluated, and discussed with respect to both physical and numerical restrictions. None of the procedures is classified as best; therefore, the specific advantages and weaknesses are discussed. It is shown that the relationship between the cloud shade and point cloudiness computed using the estimated cloud field parameters recovers the typical relationship derived from measurements.

From large-eddy simulation to multi-UAVs sampling of shallow cumulus clouds

NASA Astrophysics Data System (ADS)

Lamraoui, Fayçal; Roberts, Greg; Burnet, Frédéric

2016-04-01

In-situ sampling of clouds that can provide simultaneous measurements at satisfying spatio-temporal resolutions to capture 3D small scale physical processes continues to present challenges. This project (SKYSCANNER) aims at bringing together cloud sampling strategies using a swarm of unmanned aerial vehicles (UAVs) based on Large-eddy simulation (LES). The multi-UAV-based field campaigns with a personalized sampling strategy for individual clouds and cloud fields will significantly improve the understanding of the unresolved cloud physical processes. An extensive set of LES experiments for case studies from ARM-SGP site have been performed using MesoNH model at high resolutions down to 10 m. The carried out simulations led to establishing a macroscopic model that quantifies the interrelationship between micro- and macrophysical properties of shallow convective clouds. Both the geometry and evolution of individual clouds are critical to multi-UAV cloud sampling and path planning. The preliminary findings of the current project reveal several linear relationships that associate many cloud geometric parameters to cloud related meteorological variables. In addition, the horizontal wind speed indicates a proportional impact on cloud number concentration as well as triggering and prolonging the occurrence of cumulus clouds. In the framework of the joint collaboration that involves a Multidisciplinary Team (including institutes specializing in aviation, robotics and atmospheric science), this model will be a reference point for multi-UAVs sampling strategies and path planning.

A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors

DOE PAGES

Riihimaki, Laura D.; Comstock, Jennifer M.; Anderson, Kevin K.; ...

2016-06-10

Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty informationmore » on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. As a result, this is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.« less

A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors

NASA Astrophysics Data System (ADS)

Riihimaki, Laura D.; Comstock, Jennifer M.; Anderson, Kevin K.; Holmes, Aimee; Luke, Edward

2016-06-01

Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.

From the air to digital landscapes: generating reach-scale topographic models from aerial photography in gravel-bed rivers

NASA Astrophysics Data System (ADS)

Vericat, Damià; Narciso, Efrén; Béjar, Maria; Tena, Álvaro; Brasington, James; Gibbins, Chris; Batalla, Ramon J.

2014-05-01

Digital Terrain Models are fundamental to characterise landscapes, to support numerical modelling and to monitor topographic changes. Recent advances in topography, remote sensing and geomatics are providing new opportunities to obtain high density/quality and rapid topographic data. In this paper we present an integrated methodology to rapidly obtain reach scale topographic models of fluvial systems. This methodology has been tested and is being applied to develop event-scale terrain models of a 11-km river reach in the highly dynamic Upper Cinca (NE Iberian Peninsula). This research is conducted in the background of the project MorphSed. The methodology integrates (a) the acquisition of dense point clouds of the exposed floodplain (aerial photography and digital photogrammetry); (b) the registration of all observations to the same coordinate system (using RTK-GPS surveyed GCPs); (c) the acquisition of bathymetric data (using aDcp measurements integrated with RTK-GPS); (d) the intelligent decimation of survey observations (using the open source TopCat toolkit) and, finally, (e) data fusion (elaborating Digital Elevation Models). In this paper special emphasis is given to the acquisition and registration of point clouds. 3D point clouds are obtained from aerial photography and by means of automated digital photogrammetry. Aerial photographs are taken at 275 meters above the ground by means of a SLR digital camera manually operated from an autogyro. Four flight paths are defined in order to cover the 11 km long and 500 meters wide river reach. A total of 45 minutes are required to fly along these paths. Camera has been previously calibrated with the objective to ensure image resolution at around 5 cm. A total of 220 GCPs are deployed and RTK-GPS surveyed before the flight is conducted. Two people and one full workday are necessary to deploy and survey the full set of GCPs. Field data acquisition may be finalised in less than 2 days. Structure-from-Motion is subsequently applied in the lab using Agisoft PhotoScan, photographs are aligned and a 3d point cloud is generated. GCPs are used to geo-register all point clouds. This task may be time consuming since GCPs need to be identified in at least two of the pictures. A first automatic identification of GCPs positions is performed in the rest of the photos, although user supervision is necessary. Preliminary results show as geo-registration errors between 0.08 and and 0.10 meters can be obtained. The number of GCPs is being degraded and the quality of the point cloud assessed based on check points (the extracted GCPs). A critical analysis of GCPs density and scene locations is being performed (results in preparation). The results show that automated digital photogrammetry may provide new opportunities in the acquisition of topographic data at multiple temporal and spatial scales, being competitive with other more expensive techniques that, in turn, may require much more time to acquire field observations. SfM offers new opportunities to develop event-scale terrain models of fluvial systems suitable for hydraulic modelling and to study topographic change in highly dynamic environments.

3DNOW: Image-Based 3d Reconstruction and Modeling via Web

NASA Astrophysics Data System (ADS)

Tefera, Y.; Poiesi, F.; Morabito, D.; Remondino, F.; Nocerino, E.; Chippendale, P.

2018-05-01

This paper presents a web-based 3D imaging pipeline, namely 3Dnow, that can be used by anyone without the need of installing additional software other than a browser. By uploading a set of images through the web interface, 3Dnow can generate sparse and dense point clouds as well as mesh models. 3D reconstructed models can be downloaded with standard formats or previewed directly on the web browser through an embedded visualisation interface. In addition to reconstructing objects, 3Dnow offers the possibility to evaluate and georeference point clouds. Reconstruction statistics, such as minimum, maximum and average intersection angles, point redundancy and density can also be accessed. The paper describes all features available in the web service and provides an analysis of the computational performance using servers with different GPU configurations.

Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform - A case study in Wild Duck Lake Wetland, Beijing, China

NASA Astrophysics Data System (ADS)

Jing, Ran; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Deng, Lei

2017-12-01

Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (HAVG), standard deviation of height (HSD) and coefficient of variation of height (HCV) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: HAVG, HSD, HCV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated.

MLS data segmentation using Point Cloud Library procedures. (Polish Title: Segmentacja danych MLS z użyciem procedur Point Cloud Library)

NASA Astrophysics Data System (ADS)

Grochocka, M.

2013-12-01

Mobile laser scanning is dynamically developing measurement technology, which is becoming increasingly widespread in acquiring three-dimensional spatial information. Continuous technical progress based on the use of new tools, technology development, and thus the use of existing resources in a better way, reveals new horizons of extensive use of MLS technology. Mobile laser scanning system is usually used for mapping linear objects, and in particular the inventory of roads, railways, bridges, shorelines, shafts, tunnels, and even geometrically complex urban spaces. The measurement is done from the perspective of use of the object, however, does not interfere with the possibilities of movement and work. This paper presents the initial results of the segmentation data acquired by the MLS. The data used in this work was obtained as part of an inventory measurement infrastructure railway line. Measurement of point clouds was carried out using a profile scanners installed on the railway platform. To process the data, the tools of 'open source' Point Cloud Library was used. These tools allow to use templates of programming libraries. PCL is an open, independent project, operating on a large scale for processing 2D/3D image and point clouds. Software PCL is released under the terms of the BSD license (Berkeley Software Distribution License), which means it is a free for commercial and research use. The article presents a number of issues related to the use of this software and its capabilities. Segmentation data is based on applying the templates library pcl_ segmentation, which contains the segmentation algorithms to separate clusters. These algorithms are best suited to the processing point clouds, consisting of a number of spatially isolated regions. Template library performs the extraction of the cluster based on the fit of the model by the consensus method samples for various parametric models (planes, cylinders, spheres, lines, etc.). Most of the mathematical operation is carried out on the basis of Eigen library, a set of templates for linear algebra.

Challenges in Flying Quadrotor Unmanned Aerial Vehicle for 3d Indoor Reconstruction

NASA Astrophysics Data System (ADS)

Yan, J.; Grasso, N.; Zlatanova, S.; Braggaar, R. C.; Marx, D. B.

2017-09-01

Three-dimensional modelling plays a vital role in indoor 3D tracking, navigation, guidance and emergency evacuation. Reconstruction of indoor 3D models is still problematic, in part, because indoor spaces provide challenges less-documented than their outdoor counterparts. Challenges include obstacles curtailing image and point cloud capture, restricted accessibility and a wide array of indoor objects, each with unique semantics. Reconstruction of indoor environments can be achieved through a photogrammetric approach, e.g. by using image frames, aligned using recurring corresponding image points (CIP) to build coloured point clouds. Our experiments were conducted by flying a QUAV in three indoor environments and later reconstructing 3D models which were analysed under different conditions. Point clouds and meshes were created using Agisoft PhotoScan Professional. We concentrated on flight paths from two vantage points: 1) safety and security while flying indoors and 2) data collection needed for reconstruction of 3D models. We surmised that the main challenges in providing safe flight paths are related to the physical configuration of indoor environments, privacy issues, the presence of people and light conditions. We observed that the quality of recorded video used for 3D reconstruction has a high dependency on surface materials, wall textures and object types being reconstructed. Our results show that 3D indoor reconstruction predicated on video capture using a QUAV is indeed feasible, but close attention should be paid to flight paths and conditions ultimately influencing the quality of 3D models. Moreover, it should be decided in advance which objects need to be reconstructed, e.g. bare rooms or detailed furniture.

Cloud-based calculators for fast and reliable access to NOAA's geomagnetic field models

NASA Astrophysics Data System (ADS)

Woods, A.; Nair, M. C.; Boneh, N.; Chulliat, A.

2017-12-01

While the Global Positioning System (GPS) provides accurate point locations, it does not provide pointing directions. Therefore, the absolute directional information provided by the Earth's magnetic field is of primary importance for navigation and for the pointing of technical devices such as aircrafts, satellites and lately, mobile phones. The major magnetic sources that affect compass-based navigation are the Earth's core, its magnetized crust and the electric currents in the ionosphere and magnetosphere. NOAA/CIRES Geomagnetism (ngdc.noaa.gov/geomag/) group develops and distributes models that describe all these important sources to aid navigation. Our geomagnetic models are used in variety of platforms including airplanes, ships, submarines and smartphones. While the magnetic field from Earth's core can be described in relatively fewer parameters and is suitable for offline computation, the magnetic sources from Earth's crust, ionosphere and magnetosphere require either significant computational resources or real-time capabilities and are not suitable for offline calculation. This is especially important for small navigational devices or embedded systems, where computational resources are limited. Recognizing the need for a fast and reliable access to our geomagnetic field models, we developed cloud-based application program interfaces (APIs) for NOAA's ionospheric and magnetospheric magnetic field models. In this paper we will describe the need for reliable magnetic calculators, the challenges faced in running geomagnetic field models in the cloud in real-time and the feedback from our user community. We discuss lessons learned harvesting and validating the data which powers our cloud services, as well as our strategies for maintaining near real-time service, including load-balancing, real-time monitoring, and instance cloning. We will also briefly talk about the progress we achieved on NOAA's Big Earth Data Initiative (BEDI) funded project to develop API interface to our Enhanced Magnetic Model (EMM).

Microphysics in the Multi-Scale Modeling Systems with Unified Physics

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Chern, J.; Lamg, S.; Matsui, T.; Shen, B.; Zeng, X.; Shi, R.

2011-01-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the microphysics developments of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the heavy precipitation processes will be presented.

A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling.

PubMed

Li, Jilong; Cheng, Jianlin

2016-05-10

Generating tertiary structural models for a target protein from the known structure of its homologous template proteins and their pairwise sequence alignment is a key step in protein comparative modeling. Here, we developed a new stochastic point cloud sampling method, called MTMG, for multi-template protein model generation. The method first superposes the backbones of template structures, and the Cα atoms of the superposed templates form a point cloud for each position of a target protein, which are represented by a three-dimensional multivariate normal distribution. MTMG stochastically resamples the positions for Cα atoms of the residues whose positions are uncertain from the distribution, and accepts or rejects new position according to a simulated annealing protocol, which effectively removes atomic clashes commonly encountered in multi-template comparative modeling. We benchmarked MTMG on 1,033 sequence alignments generated for CASP9, CASP10 and CASP11 targets, respectively. Using multiple templates with MTMG improves the GDT-TS score and TM-score of structural models by 2.96-6.37% and 2.42-5.19% on the three datasets over using single templates. MTMG's performance was comparable to Modeller in terms of GDT-TS score, TM-score, and GDT-HA score, while the average RMSD was improved by a new sampling approach. The MTMG software is freely available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/mtmg.html.

A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling

PubMed Central

Li, Jilong; Cheng, Jianlin

2016-01-01

Generating tertiary structural models for a target protein from the known structure of its homologous template proteins and their pairwise sequence alignment is a key step in protein comparative modeling. Here, we developed a new stochastic point cloud sampling method, called MTMG, for multi-template protein model generation. The method first superposes the backbones of template structures, and the Cα atoms of the superposed templates form a point cloud for each position of a target protein, which are represented by a three-dimensional multivariate normal distribution. MTMG stochastically resamples the positions for Cα atoms of the residues whose positions are uncertain from the distribution, and accepts or rejects new position according to a simulated annealing protocol, which effectively removes atomic clashes commonly encountered in multi-template comparative modeling. We benchmarked MTMG on 1,033 sequence alignments generated for CASP9, CASP10 and CASP11 targets, respectively. Using multiple templates with MTMG improves the GDT-TS score and TM-score of structural models by 2.96–6.37% and 2.42–5.19% on the three datasets over using single templates. MTMG’s performance was comparable to Modeller in terms of GDT-TS score, TM-score, and GDT-HA score, while the average RMSD was improved by a new sampling approach. The MTMG software is freely available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/mtmg.html. PMID:27161489

a Low-Cost and Portable System for 3d Reconstruction of Texture-Less Objects

NASA Astrophysics Data System (ADS)

Hosseininaveh, A.; Yazdan, R.; Karami, A.; Moradi, M.; Ghorbani, F.

2015-12-01

The optical methods for 3D modelling of objects can be classified into two categories including image-based and range-based methods. Structure from Motion is one of the image-based methods implemented in commercial software. In this paper, a low-cost and portable system for 3D modelling of texture-less objects is proposed. This system includes a rotating table designed and developed by using a stepper motor and a very light rotation plate. The system also has eight laser light sources with very dense and strong beams which provide a relatively appropriate pattern on texture-less objects. In this system, regarding to the step of stepper motor, images are semi automatically taken by a camera. The images can be used in structure from motion procedures implemented in Agisoft software.To evaluate the performance of the system, two dark objects were used. The point clouds of these objects were obtained by spraying a light powders on the objects and exploiting a GOM laser scanner. Then these objects were placed on the proposed turntable. Several convergent images were taken from each object while the laser light sources were projecting the pattern on the objects. Afterward, the images were imported in VisualSFM as a fully automatic software package for generating an accurate and complete point cloud. Finally, the obtained point clouds were compared to the point clouds generated by the GOM laser scanner. The results showed the ability of the proposed system to produce a complete 3D model from texture-less objects.

Evaluation Model for Pavement Surface Distress on 3d Point Clouds from Mobile Mapping System

NASA Astrophysics Data System (ADS)

Aoki, K.; Yamamoto, K.; Shimamura, H.

2012-07-01

This paper proposes a methodology to evaluate the pavement surface distress for maintenance planning of road pavement using 3D point clouds from Mobile Mapping System (MMS). The issue on maintenance planning of road pavement requires scheduled rehabilitation activities for damaged pavement sections to keep high level of services. The importance of this performance-based infrastructure asset management on actual inspection data is globally recognized. Inspection methodology of road pavement surface, a semi-automatic measurement system utilizing inspection vehicles for measuring surface deterioration indexes, such as cracking, rutting and IRI, have already been introduced and capable of continuously archiving the pavement performance data. However, any scheduled inspection using automatic measurement vehicle needs much cost according to the instruments' specification or inspection interval. Therefore, implementation of road maintenance work, especially for the local government, is difficult considering costeffectiveness. Based on this background, in this research, the methodologies for a simplified evaluation for pavement surface and assessment of damaged pavement section are proposed using 3D point clouds data to build urban 3D modelling. The simplified evaluation results of road surface were able to provide useful information for road administrator to find out the pavement section for a detailed examination and for an immediate repair work. In particular, the regularity of enumeration of 3D point clouds was evaluated using Chow-test and F-test model by extracting the section where the structural change of a coordinate value was remarkably achieved. Finally, the validity of the current methodology was investigated by conducting a case study dealing with the actual inspection data of the local roads.

Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system.

PubMed

Seinfeld, John H; Bretherton, Christopher; Carslaw, Kenneth S; Coe, Hugh; DeMott, Paul J; Dunlea, Edward J; Feingold, Graham; Ghan, Steven; Guenther, Alex B; Kahn, Ralph; Kraucunas, Ian; Kreidenweis, Sonia M; Molina, Mario J; Nenes, Athanasios; Penner, Joyce E; Prather, Kimberly A; Ramanathan, V; Ramaswamy, Venkatachalam; Rasch, Philip J; Ravishankara, A R; Rosenfeld, Daniel; Stephens, Graeme; Wood, Robert

2016-05-24

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol-cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol-cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.

Improving Our Fundamental Understanding of the Role of Aerosol Cloud Interactions in the Climate System

NASA Technical Reports Server (NTRS)

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; Coe, Hugh; DeMott, Paul J.; Dunlea, Edward J.; Feingold, Graham; Ghan, Steven; Guenther, Alex B.; Kahn, Ralph;

Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system

DOE PAGES

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; ...

2016-05-24

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component of the overall global radiative forcing from pre-industrial time. General Circulation Models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions but significant challengesmore » exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol-cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. Lastly, we suggest strategies for improving estimates of aerosol-cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.« less

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

PubMed Central

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; Coe, Hugh; DeMott, Paul J.; Dunlea, Edward J.; Feingold, Graham; Ghan, Steven; Guenther, Alex B.; Kraucunas, Ian; Molina, Mario J.; Nenes, Athanasios; Penner, Joyce E.; Prather, Kimberly A.; Ramanathan, V.; Ramaswamy, Venkatachalam; Rasch, Philip J.; Ravishankara, A. R.; Rosenfeld, Daniel; Stephens, Graeme; Wood, Robert

2016-01-01

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty. PMID:27222566

Thermodynamic and cloud parameter retrieval using infrared spectral data

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Smith, William L., Sr.; Liu, Xu; Larar, Allen M.; Huang, Hung-Lung A.; Li, Jun; McGill, Matthew J.; Mango, Stephen A.

2005-01-01

High-resolution infrared radiance spectra obtained from near nadir observations provide atmospheric, surface, and cloud property information. A fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. The retrieval algorithm is presented along with its application to recent field experiment data from the NPOESS Airborne Sounding Testbed - Interferometer (NAST-I). The retrieval accuracy dependence on cloud properties is discussed. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with an accuracy of approximately 1.0 km. Preliminary NAST-I retrieval results from the recent Atlantic-THORPEX Regional Campaign (ATReC) are presented and compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL).

LiDAR Point Cloud and Stereo Image Point Cloud Fusion

DTIC Science & Technology

2013-09-01

LiDAR point cloud (right) highlighting linear edge features ideal for automatic registration...point cloud (right) highlighting linear edge features ideal for automatic registration. Areas where topography is being derived, unfortunately, do...with the least amount of automatic correlation errors was used. The following graphic (Figure 12) shows the coverage of the WV1 stereo triplet as

Automatic Rail Extraction and Celarance Check with a Point Cloud Captured by Mls in a Railway

NASA Astrophysics Data System (ADS)

Niina, Y.; Honma, R.; Honma, Y.; Kondo, K.; Tsuji, K.; Hiramatsu, T.; Oketani, E.

2018-05-01

Recently, MLS (Mobile Laser Scanning) has been successfully used in a road maintenance. In this paper, we present the application of MLS for the inspection of clearance along railway tracks of West Japan Railway Company. Point clouds around the track are captured by MLS mounted on a bogie and rail position can be determined by matching the shape of the ideal rail head with respect to the point cloud by ICP algorithm. A clearance check is executed automatically with virtual clearance model laid along the extracted rail. As a result of evaluation, the accuracy of extracting rail positions is less than 3 mm. With respect to the automatic clearance check, the objects inside the clearance and the ones related to a contact line is successfully detected by visual confirmation.

2.5D Multi-View Gait Recognition Based on Point Cloud Registration

PubMed Central

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-01-01

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM. PMID:24686727

Robust Spacecraft Component Detection in Point Clouds.

PubMed

Wei, Quanmao; Jiang, Zhiguo; Zhang, Haopeng

2018-03-21

Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density.

Robust Spacecraft Component Detection in Point Clouds

PubMed Central

Wei, Quanmao; Jiang, Zhiguo

2018-01-01

Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density. PMID:29561828

Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm

PubMed Central

Yan, Li; Xie, Hong; Chen, Changjun

2017-01-01

Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (TLS) point clouds (TLS-TLS point clouds) and alignment between TLS and mobile LiDAR scanning (MLS) point clouds (TLS-MLS point clouds). The scanning station position acquired by the TLS built-in GPS and the quasi-horizontal orientation of the LiDAR sensor in data acquisition are used as constraints to narrow the search space in GA. A new fitness function to evaluate the solutions for GA, named as Normalized Sum of Matching Scores, is proposed for accurate registration. Our method is divided into five steps: selection of matching points, initialization of population, transformation of matching points, calculation of fitness values, and genetic operation. The method is verified using a TLS-TLS data set and a TLS-MLS data set. The experimental results indicate that the RMSE of registration of TLS-TLS point clouds is 3~5 mm, and that of TLS-MLS point clouds is 2~4 cm. The registration integrating the existing well-known ICP with GA is further proposed to accelerate the optimization and its optimizing time decreases by about 50%. PMID:28850100

Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm.

PubMed

Yan, Li; Tan, Junxiang; Liu, Hua; Xie, Hong; Chen, Changjun

2017-08-29

Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (TLS) point clouds (TLS-TLS point clouds) and alignment between TLS and mobile LiDAR scanning (MLS) point clouds (TLS-MLS point clouds). The scanning station position acquired by the TLS built-in GPS and the quasi-horizontal orientation of the LiDAR sensor in data acquisition are used as constraints to narrow the search space in GA. A new fitness function to evaluate the solutions for GA, named as Normalized Sum of Matching Scores, is proposed for accurate registration. Our method is divided into five steps: selection of matching points, initialization of population, transformation of matching points, calculation of fitness values, and genetic operation. The method is verified using a TLS-TLS data set and a TLS-MLS data set. The experimental results indicate that the RMSE of registration of TLS-TLS point clouds is 3~5 mm, and that of TLS-MLS point clouds is 2~4 cm. The registration integrating the existing well-known ICP with GA is further proposed to accelerate the optimization and its optimizing time decreases by about 50%.

D Building FAÇADE Reconstruction Using Handheld Laser Scanning Data

NASA Astrophysics Data System (ADS)

Sadeghi, F.; Arefi, H.; Fallah, A.; Hahn, M.

2015-12-01

3D The three dimensional building modelling has been an interesting topic of research for decades and it seems that photogrammetry methods provide the only economic means to acquire truly 3D city data. According to the enormous developments of 3D building reconstruction with several applications such as navigation system, location based services and urban planning, the need to consider the semantic features (such as windows and doors) becomes more essential than ever, and therefore, a 3D model of buildings as block is not any more sufficient. To reconstruct the façade elements completely, we employed the high density point cloud data that obtained from the handheld laser scanner. The advantage of the handheld laser scanner with capability of direct acquisition of very dense 3D point clouds is that there is no need to derive three dimensional data from multi images using structure from motion techniques. This paper presents a grammar-based algorithm for façade reconstruction using handheld laser scanner data. The proposed method is a combination of bottom-up (data driven) and top-down (model driven) methods in which, at first the façade basic elements are extracted in a bottom-up way and then they are served as pre-knowledge for further processing to complete models especially in occluded and incomplete areas. The first step of data driven modelling is using the conditional RANSAC (RANdom SAmple Consensus) algorithm to detect façade plane in point cloud data and remove noisy objects like trees, pedestrians, traffic signs and poles. Then, the façade planes are divided into three depth layers to detect protrusion, indentation and wall points using density histogram. Due to an inappropriate reflection of laser beams from glasses, the windows appear like holes in point cloud data and therefore, can be distinguished and extracted easily from point cloud comparing to the other façade elements. Next step, is rasterizing the indentation layer that holds the windows and doors information. After rasterization process, the morphological operators are applied in order to remove small irrelevant objects. Next, the horizontal splitting lines are employed to determine floors and vertical splitting lines are employed to detect walls, windows, and doors. The windows, doors and walls elements which are named as terminals are clustered during classification process. Each terminal contains a special property as width. Among terminals, windows and doors are named the geometry tiles in definition of the vocabularies of grammar rules. Higher order structures that inferred by grouping the tiles resulted in the production rules. The rules with three dimensional modelled façade elements constitute formal grammar that is named façade grammar. This grammar holds all the information that is necessary to reconstruct façades in the style of the given building. Thus, it can be used to improve and complete façade reconstruction in areas with no or limited sensor data. Finally, a 3D reconstructed façade model is generated that the accuracy of its geometry size and geometry position depends on the density of the raw point cloud.

Extracting Topological Relations Between Indoor Spaces from Point Clouds

NASA Astrophysics Data System (ADS)

Tran, H.; Khoshelham, K.; Kealy, A.; Díaz-Vilariño, L.

2017-09-01

3D models of indoor environments are essential for many application domains such as navigation guidance, emergency management and a range of indoor location-based services. The principal components defined in different BIM standards contain not only building elements, such as floors, walls and doors, but also navigable spaces and their topological relations, which are essential for path planning and navigation. We present an approach to automatically reconstruct topological relations between navigable spaces from point clouds. Three types of topological relations, namely containment, adjacency and connectivity of the spaces are modelled. The results of initial experiments demonstrate the potential of the method in supporting indoor navigation.

Detection and Classification of Pole-Like Objects from Mobile Mapping Data

NASA Astrophysics Data System (ADS)

Fukano, K.; Masuda, H.

2015-08-01

Laser scanners on a vehicle-based mobile mapping system can capture 3D point-clouds of roads and roadside objects. Since roadside objects have to be maintained periodically, their 3D models are useful for planning maintenance tasks. In our previous work, we proposed a method for detecting cylindrical poles and planar plates in a point-cloud. However, it is often required to further classify pole-like objects into utility poles, streetlights, traffic signals and signs, which are managed by different organizations. In addition, our previous method may fail to extract low pole-like objects, which are often observed in urban residential areas. In this paper, we propose new methods for extracting and classifying pole-like objects. In our method, we robustly extract a wide variety of poles by converting point-clouds into wireframe models and calculating cross-sections between wireframe models and horizontal cutting planes. For classifying pole-like objects, we subdivide a pole-like object into five subsets by extracting poles and planes, and calculate feature values of each subset. Then we apply a supervised machine learning method using feature variables of subsets. In our experiments, our method could achieve excellent results for detection and classification of pole-like objects.

Automatic Classification of Trees from Laser Scanning Point Clouds

NASA Astrophysics Data System (ADS)

Sirmacek, B.; Lindenbergh, R.

2015-08-01

Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatically classifying laser scanning point clouds into 'tree' and 'non-tree' classes. Our method uses the 3D coordinates of the laser scanning points as input and generates a new point cloud which holds a label for each point indicating if it belongs to the 'tree' or 'non-tree' class. To do so, a grid surface is assigned to the lowest height level of the point cloud. The grids are filled with probability values which are calculated by checking the point density above the grid. Since the tree trunk locations appear with very high values in the probability matrix, selecting the local maxima of the grid surface help to detect the tree trunks. Further points are assigned to tree trunks if they appear in the close proximity of trunks. Since heavy mathematical computations (such as point cloud organization, detailed shape 3D detection methods, graph network generation) are not required, the proposed algorithm works very fast compared to the existing methods. The tree classification results are found reliable even on point clouds of cities containing many different objects. As the most significant weakness, false detection of light poles, traffic signs and other objects close to trees cannot be prevented. Nevertheless, the experimental results on mobile and airborne laser scanning point clouds indicate the possible usage of the algorithm as an important step for tree growth observation, tree counting and similar applications. While the laser scanning point cloud is giving opportunity to classify even very small trees, accuracy of the results is reduced in the low point density areas further away than the scanning location. These advantages and disadvantages of two laser scanning point cloud sources are discussed in detail.

Terrain Extraction by Integrating Terrestrial Laser Scanner Data and Spectral Information

NASA Astrophysics Data System (ADS)

Lau, C. L.; Halim, S.; Zulkepli, M.; Azwan, A. M.; Tang, W. L.; Chong, A. K.

2015-10-01

The extraction of true terrain points from unstructured laser point cloud data is an important process in order to produce an accurate digital terrain model (DTM). However, most of these spatial filtering methods just utilizing the geometrical data to discriminate the terrain points from nonterrain points. The point cloud filtering method also can be improved by using the spectral information available with some scanners. Therefore, the objective of this study is to investigate the effectiveness of using the three-channel (red, green and blue) of the colour image captured from built-in digital camera which is available in some Terrestrial Laser Scanner (TLS) for terrain extraction. In this study, the data acquisition was conducted at a mini replica landscape in Universiti Teknologi Malaysia (UTM), Skudai campus using Leica ScanStation C10. The spectral information of the coloured point clouds from selected sample classes are extracted for spectral analysis. The coloured point clouds which within the corresponding preset spectral threshold are identified as that specific feature point from the dataset. This process of terrain extraction is done through using developed Matlab coding. Result demonstrates that a higher spectral resolution passive image is required in order to improve the output. This is because low quality of the colour images captured by the sensor contributes to the low separability in spectral reflectance. In conclusion, this study shows that, spectral information is capable to be used as a parameter for terrain extraction.

EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation

NASA Astrophysics Data System (ADS)

Bony, Sandrine; Stevens, Bjorn; Ament, Felix; Bigorre, Sebastien; Chazette, Patrick; Crewell, Susanne; Delanoë, Julien; Emanuel, Kerry; Farrell, David; Flamant, Cyrille; Gross, Silke; Hirsch, Lutz; Karstensen, Johannes; Mayer, Bernhard; Nuijens, Louise; Ruppert, James H.; Sandu, Irina; Siebesma, Pier; Speich, Sabrina; Szczap, Frédéric; Totems, Julien; Vogel, Raphaela; Wendisch, Manfred; Wirth, Martin

2017-11-01

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air-sea interactions and convective organization.

EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation

NASA Astrophysics Data System (ADS)

Bony, Sandrine; Stevens, Bjorn; Ament, Felix; Bigorre, Sebastien; Chazette, Patrick; Crewell, Susanne; Delanoë, Julien; Emanuel, Kerry; Farrell, David; Flamant, Cyrille; Gross, Silke; Hirsch, Lutz; Karstensen, Johannes; Mayer, Bernhard; Nuijens, Louise; Ruppert, James H.; Sandu, Irina; Siebesma, Pier; Speich, Sabrina; Szczap, Frédéric; Totems, Julien; Vogel, Raphaela; Wendisch, Manfred; Wirth, Martin

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of tradecumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of tradecumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air-sea interactions and convective organization.

Using Multi-Scale Modeling Systems and Satellite Data to Study the Precipitation Processes

NASA Technical Reports Server (NTRS)

Tao, Wei--Kuo; Chern, J.; Lamg, S.; Matsui, T.; Shen, B.; Zeng, X.; Shi, R.

2010-01-01

In recent years, exponentially increasing computer power extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 sq km in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale models can be run in grid size similar to cloud resolving models through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model). (2) a regional scale model (a NASA unified weather research and forecast, W8F). (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling systems to study the interactions between clouds, precipitation, and aerosols will be presented. Also how to use the multi-satellite simulator to improve precipitation processes will be discussed.

Using Multi-Scale Modeling Systems to Study the Precipitation Processes

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2010-01-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the interactions between clouds, precipitation, and aerosols will be presented. Also how to use of the multi-satellite simulator to improve precipitation processes will be discussed.

Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data

PubMed Central

Ordóñez, Celestino; Cabo, Carlos; Sanz-Ablanedo, Enoc

2017-01-01

Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%. PMID:28640189

Estimating Aircraft Heading Based on Laserscanner Derived Point Clouds

NASA Astrophysics Data System (ADS)

Koppanyi, Z.; Toth, C., K.

2015-03-01

Using LiDAR sensors for tracking and monitoring an operating aircraft is a new application. In this paper, we present data processing methods to estimate the heading of a taxiing aircraft using laser point clouds. During the data acquisition, a Velodyne HDL-32E laser scanner tracked a moving Cessna 172 airplane. The point clouds captured at different times were used for heading estimation. After addressing the problem and specifying the equation of motion to reconstruct the aircraft point cloud from the consecutive scans, three methods are investigated here. The first requires a reference model to estimate the relative angle from the captured data by fitting different cross-sections (horizontal profiles). In the second approach, iterative closest point (ICP) method is used between the consecutive point clouds to determine the horizontal translation of the captured aircraft body. Regarding the ICP, three different versions were compared, namely, the ordinary 3D, 3-DoF 3D and 2-DoF 3D ICP. It was found that 2-DoF 3D ICP provides the best performance. Finally, the last algorithm searches for the unknown heading and velocity parameters by minimizing the volume of the reconstructed plane. The three methods were compared using three test datatypes which are distinguished by object-sensor distance, heading and velocity. We found that the ICP algorithm fails at long distances and when the aircraft motion direction perpendicular to the scan plane, but the first and the third methods give robust and accurate results at 40m object distance and at ~12 knots for a small Cessna airplane.

Ifcwall Reconstruction from Unstructured Point Clouds

NASA Astrophysics Data System (ADS)

Bassier, M.; Klein, R.; Van Genechten, B.; Vergauwen, M.

2018-05-01

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are processed and the wall topology is reconstructed. However, the preocedure is challenging due to noise, occlusions and the complexity of the input data.In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.

Automatic Generation of Indoor Navigable Space Using a Point Cloud and its Scanner Trajectory

NASA Astrophysics Data System (ADS)

Staats, B. R.; Diakité, A. A.; Voûte, R. L.; Zlatanova, S.

2017-09-01

Automatic generation of indoor navigable models is mostly based on 2D floor plans. However, in many cases the floor plans are out of date. Buildings are not always built according to their blue prints, interiors might change after a few years because of modified walls and doors, and furniture may be repositioned to the user's preferences. Therefore, new approaches for the quick recording of indoor environments should be investigated. This paper concentrates on laser scanning with a Mobile Laser Scanner (MLS) device. The MLS device stores a point cloud and its trajectory. If the MLS device is operated by a human, the trajectory contains information which can be used to distinguish different surfaces. In this paper a method is presented for the identification of walkable surfaces based on the analysis of the point cloud and the trajectory of the MLS scanner. This method consists of several steps. First, the point cloud is voxelized. Second, the trajectory is analysing and projecting to acquire seed voxels. Third, these seed voxels are generated into floor regions by the use of a region growing process. By identifying dynamic objects, doors and furniture, these floor regions can be modified so that each region represents a specific navigable space inside a building as a free navigable voxel space. By combining the point cloud and its corresponding trajectory, the walkable space can be identified for any type of building even if the interior is scanned during business hours.

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Li, X.; Khain, A.; Simpson, S.

2004-01-01

Cloud microphysics are inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, two detailed spectral-bin microphysical schemes were implemented into the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral-bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles (i.e., pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail). Each type is described by a special size distribution function containing many categories (i.e. 33 bins). Atmospheric aerosols are also described using number density size-distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep cloud systems in the west Pacific warm pool region, in the sub-tropics (Florida) and in the mid-latitude using identical thermodynamic conditions but with different concentrations of CCN: a low 'clean' concentration and a high 'dirty' concentration.

a Modeling Method of Fluttering Leaves Based on Point Cloud

NASA Astrophysics Data System (ADS)

Tang, J.; Wang, Y.; Zhao, Y.; Hao, W.; Ning, X.; Lv, K.; Shi, Z.; Zhao, M.

2017-09-01

Leaves falling gently or fluttering are common phenomenon in nature scenes. The authenticity of leaves falling plays an important part in the dynamic modeling of natural scenes. The leaves falling model has a widely applications in the field of animation and virtual reality. We propose a novel modeling method of fluttering leaves based on point cloud in this paper. According to the shape, the weight of leaves and the wind speed, three basic trajectories of leaves falling are defined, which are the rotation falling, the roll falling and the screw roll falling. At the same time, a parallel algorithm based on OpenMP is implemented to satisfy the needs of real-time in practical applications. Experimental results demonstrate that the proposed method is amenable to the incorporation of a variety of desirable effects.

Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds

NASA Astrophysics Data System (ADS)

Hamraz, Hamid; Contreras, Marco A.; Zhang, Jun

2017-08-01

Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies. We applied the proposed approach to the University of Kentucky Robinson Forest - a natural deciduous forest with complex and highly variable terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting understory trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented understory trees (increased from 1% to 16%), while barely affecting the overall segmentation quality of overstory trees. Results of vertical stratification of the canopy showed that the point density of understory canopy layers were suboptimal for performing a reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds would allow more improvements in segmenting understory trees. As shown by inspecting correlations of the results with forest structure, the segmentation approach is applicable to a variety of forest types.

A scalable and multi-purpose point cloud server (PCS) for easier and faster point cloud data management and processing

NASA Astrophysics Data System (ADS)

Cura, Rémi; Perret, Julien; Paparoditis, Nicolas

2017-05-01

In addition to more traditional geographical data such as images (rasters) and vectors, point cloud data are becoming increasingly available. Such data are appreciated for their precision and true three-Dimensional (3D) nature. However, managing point clouds can be difficult due to scaling problems and specificities of this data type. Several methods exist but are usually fairly specialised and solve only one aspect of the management problem. In this work, we propose a comprehensive and efficient point cloud management system based on a database server that works on groups of points (patches) rather than individual points. This system is specifically designed to cover the basic needs of point cloud users: fast loading, compressed storage, powerful patch and point filtering, easy data access and exporting, and integrated processing. Moreover, the proposed system fully integrates metadata (like sensor position) and can conjointly use point clouds with other geospatial data, such as images, vectors, topology and other point clouds. Point cloud (parallel) processing can be done in-base with fast prototyping capabilities. Lastly, the system is built on open source technologies; therefore it can be easily extended and customised. We test the proposed system with several billion points obtained from Lidar (aerial and terrestrial) and stereo-vision. We demonstrate loading speeds in the ˜50 million pts/h per process range, transparent-for-user and greater than 2 to 4:1 compression ratio, patch filtering in the 0.1 to 1 s range, and output in the 0.1 million pts/s per process range, along with classical processing methods, such as object detection.

Accurate 3D point cloud comparison and volumetric change analysis of Terrestrial Laser Scan data in a hard rock coastal cliff environment

NASA Astrophysics Data System (ADS)

Earlie, C. S.; Masselink, G.; Russell, P.; Shail, R.; Kingston, K.

2013-12-01

Our understanding of the evolution of hard rock coastlines is limited due to the episodic nature and ';slow' rate at which changes occur. High-resolution surveying techniques, such as Terrestrial Laser Scanning (TLS), have just begun to be adopted as a method of obtaining detailed point cloud data to monitor topographical changes over short periods of time (weeks to months). However, the difficulties involved in comparing consecutive point cloud data sets in a complex three-dimensional plane, such as occlusion due to surface roughness and positioning of data capture point as a result of a consistently changing environment (a beach profile), mean that comparing data sets can lead to errors in the region of 10 - 20 cm. Meshing techniques are often used for point cloud data analysis for simple surfaces, but in surfaces such as rocky cliff faces, this technique has been found to be ineffective. Recession rates of hard rock coastlines in the UK are typically determined using aerial photography or airborne LiDAR data, yet the detail of the important changes occurring to the cliff face and toe are missed using such techniques. In this study we apply an algorithm (M3C2 - Multiscale Model to Model Cloud Comparison), initially developed for analysing fluvial morphological change, that directly compares point to point cloud data using surface normals that are consistent with surface roughness and measure the change that occurs along the normal direction (Lague et al., 2013). The surfaces changes are analysed using a set of user defined scales based on surface roughness and registration error. Once the correct parameters are defined, the volumetric cliff face changes are calculated by integrating the mean distance between the point clouds. The analysis has been undertaken at two hard rock sites identified for their active erosion located on the UK's south west peninsular at Porthleven in south west Cornwall and Godrevy in north Cornwall. Alongside TLS point cloud data, in-situ measurements of the nearshore wave climate, using a pressure transducer, offshore wave climate from a directional wavebuoy, and rainfall records from nearby weather stations were collected. Combining beach elevation information from the georeferenced point clouds with a continuous time series of wave climate provides an indication of the variation in wave energy delivered to the cliff face. The rates of retreat were found to agree with the existing rates that are currently used in shoreline management. The additional geotechnical detail afforded by applying the M3C2 method to a hard rock environment provides not only a means of obtaining volumetric changes with confidence, but also a clear illustration of the locations of failure on the cliff face. Monthly cliff scans help to narrow down the timings of failure under energetic wave conditions or periods of heavy rainfall. Volumetric changes and sensitive regions to failure established using this method allows us to capture episodic changes to the cliff face at a high resolution (1 - 2 cm) that are otherwise missed using lower resolution techniques typically used for shoreline management, and to understand in greater detail the geotechnical behaviour of hard rock cliffs and determine rates of erosion with greater accuracy.

Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners

PubMed Central

Valero, Enrique; Adán, Antonio; Cerrada, Carlos

2012-01-01

In this paper we present a method that automatically yields Boundary Representation Models (B-rep) for indoors after processing dense point clouds collected by laser scanners from key locations through an existing facility. Our objective is particularly focused on providing single models which contain the shape, location and relationship of primitive structural elements of inhabited scenarios such as walls, ceilings and floors. We propose a discretization of the space in order to accurately segment the 3D data and generate complete B-rep models of indoors in which faces, edges and vertices are coherently connected. The approach has been tested in real scenarios with data coming from laser scanners yielding promising results. We have deeply evaluated the results by analyzing how reliably these elements can be detected and how accurately they are modeled. PMID:23443369

Validation of Model Simulations of Anvil Cirrus Properties During TWP-ICE: Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zipser, Edward J.

2013-05-20

This 3-year grant, with two extensions, resulted in a successful 5-year effort, led by Ph.D. student Adam Varble, to compare cloud resolving model (CRM) simulations with the excellent database obtained during the TWP-ICE field campaign. The objective, largely achieved, is to undertake these comparisons comprehensively and quantitatively, informing the community in ways that goes beyond pointing out errors in the models, but points out ways to improve both cloud dynamics and microphysics parameterizations in future modeling efforts. Under DOE support, Adam Varble, with considerable assistance from Dr. Ann Fridlind and others, entrained scientists who ran some 10 different CRMs andmore » 4 different limited area models (LAMs) using a variety of microphysics parameterizations, to ensure that the conclusions of the study will have considerable generality.« less

New from the Old - Measuring Coastal Cliff Change with Historical Oblique Aerial Photos

NASA Astrophysics Data System (ADS)

Warrick, J. A.; Ritchie, A.

2016-12-01

Oblique aerial photographs are commonly collected to document coastal landscapes. Here we show that these historical photographs can be used to develop topographic models with Structure-from-Motion (SfM) photogrammetric techniques if adequate photo-to-photo overlaps exist. Focusing on the 60-m high cliffs of Fort Funston, California, photographs from the California Coastal Records Project were combined with ground control points to develop topographic point clouds of the study area for five years between 2002 and 2010. Uncertainties in the results were assessed by comparing SfM-derived point clouds with airborne lidar data, and the differences between these data were related to the number and spatial distribution of ground control points used in the SfM analyses. With six or more ground control points the root mean squared error between the SfM and lidar data was less than 0.3 m (minimum = 0.18 m) and the mean systematic error was consistently less than 0.10 m. Because of the oblique orientation of the imagery, the SfM-derived point clouds provided coverage on vertical to overhanging portions of the cliff, and point densities from the SfM techniques averaged between 17 and 161 points/m2 on the cliff face. The time-series of topographic point clouds revealed many topographic changes, including landslides, rockfalls and the erosion of landslide talus along the Fort Funston beach. Thus, we concluded that historical oblique photographs, such as those generated by the California Coastal Records Project, can provide useful tools for mapping coastal topography and measuring coastal change.

Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning

NASA Astrophysics Data System (ADS)

Vetrivel, Anand; Gerke, Markus; Kerle, Norman; Nex, Francesco; Vosselman, George

2018-06-01

Oblique aerial images offer views of both building roofs and façades, and thus have been recognized as a potential source to detect severe building damages caused by destructive disaster events such as earthquakes. Therefore, they represent an important source of information for first responders or other stakeholders involved in the post-disaster response process. Several automated methods based on supervised learning have already been demonstrated for damage detection using oblique airborne images. However, they often do not generalize well when data from new unseen sites need to be processed, hampering their practical use. Reasons for this limitation include image and scene characteristics, though the most prominent one relates to the image features being used for training the classifier. Recently features based on deep learning approaches, such as convolutional neural networks (CNNs), have been shown to be more effective than conventional hand-crafted features, and have become the state-of-the-art in many domains, including remote sensing. Moreover, often oblique images are captured with high block overlap, facilitating the generation of dense 3D point clouds - an ideal source to derive geometric characteristics. We hypothesized that the use of CNN features, either independently or in combination with 3D point cloud features, would yield improved performance in damage detection. To this end we used CNN and 3D features, both independently and in combination, using images from manned and unmanned aerial platforms over several geographic locations that vary significantly in terms of image and scene characteristics. A multiple-kernel-learning framework, an effective way for integrating features from different modalities, was used for combining the two sets of features for classification. The results are encouraging: while CNN features produced an average classification accuracy of about 91%, the integration of 3D point cloud features led to an additional improvement of about 3% (i.e. an average classification accuracy of 94%). The significance of 3D point cloud features becomes more evident in the model transferability scenario (i.e., training and testing samples from different sites that vary slightly in the aforementioned characteristics), where the integration of CNN and 3D point cloud features significantly improved the model transferability accuracy up to a maximum of 7% compared with the accuracy achieved by CNN features alone. Overall, an average accuracy of 85% was achieved for the model transferability scenario across all experiments. Our main conclusion is that such an approach qualifies for practical use.

Reconstruction and analysis of hybrid composite shells using meshless methods

NASA Astrophysics Data System (ADS)

Bernardo, G. M. S.; Loja, M. A. R.

2017-06-01

The importance of focusing on the research of viable models to predict the behaviour of structures which may possess in some cases complex geometries is an issue that is growing in different scientific areas, ranging from the civil and mechanical engineering to the architecture or biomedical devices fields. In these cases, the research effort to find an efficient approach to fit laser scanning point clouds, to the desired surface, has been increasing, leading to the possibility of modelling as-built/as-is structures and components' features. However, combining the task of surface reconstruction and the implementation of a structural analysis model is not a trivial task. Although there are works focusing those different phases in separate, there is still an effective need to find approaches able to interconnect them in an efficient way. Therefore, achieving a representative geometric model able to be subsequently submitted to a structural analysis in a similar based platform is a fundamental step to establish an effective expeditious processing workflow. With the present work, one presents an integrated methodology based on the use of meshless approaches, to reconstruct shells described by points' clouds, and to subsequently predict their static behaviour. These methods are highly appropriate on dealing with unstructured points clouds, as they do not need to have any specific spatial or geometric requirement when implemented, depending only on the distance between the points. Details on the formulation, and a set of illustrative examples focusing the reconstruction of cylindrical and double-curvature shells, and its further analysis, are presented.

H-Ransac a Hybrid Point Cloud Segmentation Combining 2d and 3d Data

NASA Astrophysics Data System (ADS)

Adam, A.; Chatzilari, E.; Nikolopoulos, S.; Kompatsiaris, I.

2018-05-01

In this paper, we present a novel 3D segmentation approach operating on point clouds generated from overlapping images. The aim of the proposed hybrid approach is to effectively segment co-planar objects, by leveraging the structural information originating from the 3D point cloud and the visual information from the 2D images, without resorting to learning based procedures. More specifically, the proposed hybrid approach, H-RANSAC, is an extension of the well-known RANSAC plane-fitting algorithm, incorporating an additional consistency criterion based on the results of 2D segmentation. Our expectation that the integration of 2D data into 3D segmentation will achieve more accurate results, is validated experimentally in the domain of 3D city models. Results show that HRANSAC can successfully delineate building components like main facades and windows, and provide more accurate segmentation results compared to the typical RANSAC plane-fitting algorithm.

Image Capture with Synchronized Multiple-Cameras for Extraction of Accurate Geometries

NASA Astrophysics Data System (ADS)

Koehl, M.; Delacourt, T.; Boutry, C.

2016-06-01

This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing GoPro Hero4 cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (Faro Focus 3D) to allow the accuracy assessment.

Relationships between lower tropospheric stability, low cloud cover, and water vapor isotopic composition in the subtropical Pacific

NASA Astrophysics Data System (ADS)

Galewsky, J.

2017-12-01

Understanding the processes that govern the relationships between lower tropospheric stability and low-cloud cover is crucial for improved constraints on low-cloud feedbacks and for improving the parameterizations of low-cloud cover used in climate models. The stable isotopic composition of atmospheric water vapor is a sensitive recorder of the balance of moistening and drying processes that set the humidity of the lower troposphere and may thus provide a useful framework for improving our understanding low-cloud processes. In-situ measurements of water vapor isotopic composition collected at the NOAA Mauna Loa Observatory in Hawaii, along with twice-daily soundings from Hilo and remote sensing of cloud cover, show a clear inverse relationship between the estimated inversion strength (EIS) and the mixing ratios and water vapor δ -values, and a positive relationship between EIS, deuterium excess, and Δ δ D, defined as the difference between an observation and a reference Rayleigh distillation curve. These relationships are consistent with reduced moistening and an enhanced upper-tropospheric contribution above the trade inversion under high EIS conditions and stronger moistening under weaker EIS conditions. The cloud fraction, cloud liquid water path, and cloud-top pressure were all found to be higher under low EIS conditions. Inverse modeling of the isotopic data for the highest and lowest terciles of EIS conditions provide quantitative constraints on the cold-point temperatures and mixing fractions that govern the humidity above the trade inversion. The modeling shows the moistening fraction between moist boundary layer air and dry middle tropospheric air 24±1.5% under low EIS conditions is and 6±1.5% under high EIS conditions. A cold-point (last-saturation) temperature of -30C can match the observations for both low and high EIS conditions. The isotopic composition of the moistening source as derived from the inversion (-114±10‰ ) requires moderate fractionation from a pure marine source, indicating a link between inversion strength and moistening of the lower troposphere from the outflow of shallow convection. This approach can be applied in other settings and the results can be used to test parameterizations in climate models.

Power availability at terrestrial receptor sites for laser-power transmission from the satellite power system

NASA Technical Reports Server (NTRS)

Beverly, R. E., III

1982-01-01

A statistical model was developed for relating the temporal transmission parameters of a laser beam from a solar power satellite to observable meteorological data to determine the influence of weather on power reception at the earth-based receiver. Sites within 100 miles of existing high voltage transmission lines were examined and the model was developed for clear-sky and clouded conditions. The cases of total transmission through clouds at certain wavelengths, no transmission, and partial transmission were calculated for the cloud portion of the model. The study covered cirriform, stratiform, cumiliform, and mixed type clouds and the possibility of boring holes through the clouds with the beam. Utilization of weapons-quality beams for hole boring, was found to yield power availability increases of 9-33%, although no beneficial effects could be predicted in regions of persistent cloud cover. An efficiency of 80% was determined as possible if several receptor sites were available within 200-300 miles of each other, thereby allowing changes of reception point in cases of unacceptable meteorological conditions.

Towards Reconstructing a Doric Column in a Virtual Construction Site

NASA Astrophysics Data System (ADS)

Bartzis, D.

2017-02-01

This paper deals with the 3D reconstruction of ancient Greek architectural members, especially with the element of the Doric column. The case study for this project is the Choragic monument of Nicias on the South Slope of the Athenian Acropolis, from which a column drum, two capitals and smaller fragments are preserved. The first goal of this paper is to present some benefits of using 3D reconstruction methods not only in documentation but also in understanding of ancient Greek architectural members. The second goal is to take advantage of the produced point clouds. By using the Cloud Compare software, comparisons are made between the actual architectural members and an "ideal" point cloud of the whole column in its original form. Seeking for probable overlaps between the two point clouds could assist in estimating the original position of each member/fragment on the column. This method is expanded with more comparisons between the reference column model and other members/fragments around the Acropolis, which may have not yet been ascribed to the monument of Nicias.

Comparison of DSMs acquired by terrestrial laser scanning, UAV-based aerial images and ground-based optical images at the Super-Sauze landslide

NASA Astrophysics Data System (ADS)

Rothmund, Sabrina; Niethammer, Uwe; Walter, Marco; Joswig, Manfred

2013-04-01

In recent years, the high-resolution and multi-temporal 3D mapping of the Earth's surface using terrestrial laser scanning (TLS), ground-based optical images and especially low-cost UAV-based aerial images (Unmanned Aerial Vehicle) has grown in importance. This development resulted from the progressive technical improvement of the imaging systems and the freely available multi-view stereo (MVS) software packages. These different methods of data acquisition for the generation of accurate, high-resolution digital surface models (DSMs) were applied as part of an eight-week field campaign at the Super-Sauze landslide (South French Alps). An area of approximately 10,000 m² with long-term average displacement rates greater than 0.01 m/day has been investigated. The TLS-based point clouds were acquired at different viewpoints with an average point spacing between 10 to 40 mm and at different dates. On these days, more than 50 optical images were taken on points along a predefined line on the side part of the landslide by a low-cost digital compact camera. Additionally, aerial images were taken by a radio-controlled mini quad-rotor UAV equipped with another low-cost digital compact camera. The flight altitude ranged between 20 m and 250 m and produced a corresponding ground resolution between 0.6 cm and 7 cm. DGPS measurements were carried out as well in order to geo-reference and validate the point cloud data. To generate unscaled photogrammetric 3D point clouds from a disordered and tilted image set, we use the widespread open-source software package Bundler and PMVS2 (University of Washington). These multi-temporal DSMs are required on the one hand to determine the three-dimensional surface deformations and on the other hand it will be required for differential correction for orthophoto production. Drawing on the example of the acquired data at the Super-Sauze landslide, we demonstrate the potential but also the limitations of the photogrammetric point clouds. To determine the quality of the photogrammetric point cloud, these point clouds are compared with the TLS-based DSMs. The comparison shows that photogrammetric points accuracies are in the range of cm to dm, therefore don't reach the quality of the high-resolution TLS-based DSMs. Further, the validation of the photogrammetric point clouds reveals that some of them have internal curvature effects. The advantage of the photogrammetric 3D data acquisition is the use of low-cost equipment and less time-consuming data collection in the field. While the accuracy of the photogrammetric point clouds is not as high as TLS-based DSMs, the advantages of the former method are seen when applied in areas where dm-range is sufficient.

a Global Registration Algorithm of the Single-Closed Ring Multi-Stations Point Cloud

NASA Astrophysics Data System (ADS)

Yang, R.; Pan, L.; Xiang, Z.; Zeng, H.

2018-04-01

Aimed at the global registration problem of the single-closed ring multi-stations point cloud, a formula in order to calculate the error of rotation matrix was constructed according to the definition of error. The global registration algorithm of multi-station point cloud was derived to minimize the error of rotation matrix. And fast-computing formulas of transformation matrix with whose implementation steps and simulation experiment scheme was given. Compared three different processing schemes of multi-station point cloud, the experimental results showed that the effectiveness of the new global registration method was verified, and it could effectively complete the global registration of point cloud.

Generation of Ground Truth Datasets for the Analysis of 3d Point Clouds in Urban Scenes Acquired via Different Sensors

NASA Astrophysics Data System (ADS)

Xu, Y.; Sun, Z.; Boerner, R.; Koch, T.; Hoegner, L.; Stilla, U.

2018-04-01

In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

Temporal Analysis and Automatic Calibration of the Velodyne HDL-32E LiDAR System

NASA Astrophysics Data System (ADS)

Chan, T. O.; Lichti, D. D.; Belton, D.

2013-10-01

At the end of the first quarter of 2012, more than 600 Velodyne LiDAR systems had been sold worldwide for various robotic and high-accuracy survey applications. The ultra-compact Velodyne HDL-32E LiDAR has become a predominant sensor for many applications that require lower sensor size/weight and cost. For high accuracy applications, cost-effective calibration methods with minimal manual intervention are always desired by users. However, the calibrations are complicated by the Velodyne LiDAR's narrow vertical field of view and the very highly time-variant nature of its measurements. In the paper, the temporal stability of the HDL-32E is first analysed as the motivation for developing a new, automated calibration method. This is followed by a detailed description of the calibration method that is driven by a novel segmentation method for extracting vertical cylindrical features from the Velodyne point clouds. The proposed segmentation method utilizes the Velodyne point cloud's slice-like nature and first decomposes the point clouds into 2D layers. Then the layers are treated as 2D images and are processed with the Generalized Hough Transform which extracts the points distributed in circular patterns from the point cloud layers. Subsequently, the vertical cylindrical features can be readily extracted from the whole point clouds based on the previously extracted points. The points are passed to the calibration that estimates the cylinder parameters and the LiDAR's additional parameters simultaneously by constraining the segmented points to fit to the cylindrical geometric model in such a way the weighted sum of the adjustment residuals are minimized. The proposed calibration is highly automatic and this allows end users to obtain the time-variant additional parameters instantly and frequently whenever there are vertical cylindrical features presenting in scenes. The methods were verified with two different real datasets, and the results suggest that up to 78.43% accuracy improvement for the HDL-32E can be achieved using the proposed calibration method.

Cloud-In-Cell modeling of shocked particle-laden flows at a ``SPARSE'' cost

NASA Astrophysics Data System (ADS)

Taverniers, Soren; Jacobs, Gustaaf; Sen, Oishik; Udaykumar, H. S.

2017-11-01

A common tool for enabling process-scale simulations of shocked particle-laden flows is Eulerian-Lagrangian Particle-Source-In-Cell (PSIC) modeling where each particle is traced in its Lagrangian frame and treated as a mathematical point. Its dynamics are governed by Stokes drag corrected for high Reynolds and Mach numbers. The computational burden is often reduced further through a ``Cloud-In-Cell'' (CIC) approach which amalgamates groups of physical particles into computational ``macro-particles''. CIC does not account for subgrid particle fluctuations, leading to erroneous predictions of cloud dynamics. A Subgrid Particle-Averaged Reynolds-Stress Equivalent (SPARSE) model is proposed that incorporates subgrid interphase velocity and temperature perturbations. A bivariate Gaussian source distribution, whose covariance captures the cloud's deformation to first order, accounts for the particles' momentum and energy influence on the carrier gas. SPARSE is validated by conducting tests on the interaction of a particle cloud with the accelerated flow behind a shock. The cloud's average dynamics and its deformation over time predicted with SPARSE converge to their counterparts computed with reference PSIC models as the number of Gaussians is increased from 1 to 16. This work was supported by AFOSR Grant No. FA9550-16-1-0008.

Organization of the Tropical Convective Cloud Population by Humidity and the Critical Transition to Heavy Precipitation

NASA Astrophysics Data System (ADS)

Igel, M.

2015-12-01

The tropical atmosphere exhibits an abrupt statistical switch between non-raining and heavily raining states as column moisture increases across a wide range of length scales. Deep convection occurs at values of column humidity above the transition point and induces drying of moist columns. With a 1km resolution, large domain cloud resolving model run in RCE, what will be made clear here for the first time is how the entire tropical convective cloud population is affected by and feeds back to the pickup in heavy precipitation. Shallow convection can act to dry the low levels through weak precipitation or vertical redistribution of moisture, or to moisten toward a transition to deep convection. It is shown that not only can deep convection dehydrate the entire column, it can also dry just the lower layer through intense rain. In the latter case, deep stratiform cloud then forms to dry the upper layer through rain with anomalously high rates for its value of column humidity until both the total column moisture falls below the critical transition point and the upper levels are cloud free. Thus, all major tropical cloud types are shown to respond strongly to the same critical phase-transition point. This mutual response represents a potentially strong organizational mechanism for convection, and the frequency of and logical rules determining physical evolutions between these convective regimes will be discussed. The precise value of the point in total column moisture at which the transition to heavy precipitation occurs is shown to result from two independent thresholds in lower-layer and upper-layer integrated humidity.

Two new algorithms to combine kriging with stochastic modelling

NASA Astrophysics Data System (ADS)

Venema, Victor; Lindau, Ralf; Varnai, Tamas; Simmer, Clemens

2010-05-01

Two main groups of statistical methods used in the Earth sciences are geostatistics and stochastic modelling. Geostatistical methods, such as various kriging algorithms, aim at estimating the mean value for every point as well as possible. In case of sparse measurements, such fields have less variability at small scales and a narrower distribution as the true field. This can lead to biases if a nonlinear process is simulated driven by such a kriged field. Stochastic modelling aims at reproducing the statistical structure of the data in space and time. One of the stochastic modelling methods, the so-called surrogate data approach, replicates the value distribution and power spectrum of a certain data set. While stochastic methods reproduce the statistical properties of the data, the location of the measurement is not considered. This requires the use of so-called constrained stochastic models. Because radiative transfer through clouds is a highly nonlinear process, it is essential to model the distribution (e.g. of optical depth, extinction, liquid water content or liquid water path) accurately. In addition, the correlations within the cloud field are important, especially because of horizontal photon transport. This explains the success of surrogate cloud fields for use in 3D radiative transfer studies. Up to now, however, we could only achieve good results for the radiative properties averaged over the field, but not for a radiation measurement located at a certain position. Therefore we have developed a new algorithm that combines the accuracy of stochastic (surrogate) modelling with the positioning capabilities of kriging. In this way, we can automatically profit from the large geostatistical literature and software. This algorithm is similar to the standard iterative amplitude adjusted Fourier transform (IAAFT) algorithm, but has an additional iterative step in which the surrogate field is nudged towards the kriged field. The nudging strength is gradually reduced to zero during successive iterations. A second algorithm, which we call step-wise kriging, pursues the same aim. Each time the kriging algorithm estimates a value, noise is added to it, after which this new point is accounted for in the estimation of all the later points. In this way, the autocorrelation of the step-krigged field is close to that found in the pseudo measurements. The amount of noise is determined by the kriging uncertainty. The algorithms are tested on cloud fields from large eddy simulations (LES). On these clouds, a measurement is simulated. From these pseudo-measurements, we estimated the power spectrum for the surrogates, the semi-variogram for the (stepwise) kriging and the distribution. Furthermore, the pseudo-measurement is kriged. Because we work with LES clouds and the truth is known, we can validate the algorithm by performing 3D radiative transfer calculations on the original LES clouds and on the two new types of stochastic clouds. For comparison, also the radiative properties of the kriged fields and standard surrogate fields are computed. Preliminary results show that both algorithms reproduce the structure of the original clouds well, and the minima and maxima are located where the pseudo-measurements see them. The main problem for the quality of the structure and the root mean square error is the amount of data, which is especially very limited in case of just one zenith pointing measurement.

A GUI visualization system for airborne lidar image data to reconstruct 3D city model

NASA Astrophysics Data System (ADS)

Kawata, Yoshiyuki; Koizumi, Kohei

2015-10-01

A visualization toolbox system with graphical user interfaces (GUIs) was developed for the analysis of LiDAR point cloud data, as a compound object oriented widget application in IDL (Interractive Data Language). The main features in our system include file input and output abilities, data conversion capability from ascii formatted LiDAR point cloud data to LiDAR image data whose pixel value corresponds the altitude measured by LiDAR, visualization of 2D/3D images in various processing steps and automatic reconstruction ability of 3D city model. The performance and advantages of our graphical user interface (GUI) visualization system for LiDAR data are demonstrated.

Interpretation of multi-wavelength-retrieved cloud droplet effective radii in terms of cloud vertical inhomogeneity based on water cloud simulations using a spectral-bin microphysics cloud model

NASA Astrophysics Data System (ADS)

Matsui, T. N.; Suzuki, K.; Nakajima, T. Y.; Matsumae, Y.

2011-12-01

Clouds play an import role in energy balance and climate changes of the Earth. IPCC AR4, however, pointed out that cloud feedback is still the large source of uncertainty in climate estimates. In the recent decade, the new satellites with the active instruments (e.g. Cloudsat) represented a new epoch in earth observations. The active remote sensing is powerful for illustrating the vertical structures of clouds, but the passive remote sensing from satellite images also contribute to better understating of cloud system. For instance, Nakajima et al. (2010a) and Suzuki et al. (2010) illustrated transition of cloud growth, from cloud droplet to drizzle to rain, using the combine analysis of the cloud droplet size retrieved from passive images (MODIS) and the reflectivity profiles from Cloudsat. Furthermore, EarthCARE that is a new satellite launched years later is composed of not only the active but also passive instruments for the combined analysis. On the other hands, the methods to retrieve the advanced information of cloud properties are also required because many imagers have been operated and are now planned (e.g. GCOM-C/SGLI), and have the advantages such as wide observation width and more observation channels. Cloud droplet effective radius (CDR) and cloud optical thickness (COT) can be retrieved using a non-water-absorbing band (e.g. 0.86μm) and a water-absorbing band (1.6, 2.1, 3.7μm) of imagers under the assumptions such as the log-normal droplet size distribution and the plane-parallel cloud structure. However, the differences between three retrieved CDRs using 1.6, 2.1 or 3.7μm (R16, R21 and R37) are found in the satellite observations. Several studies pointed out that vertical/horizontal inhomogeneity of cloud structure, difference of penetration depth of water-absorbing bands, multi-modal droplet distribution and/or 3-D radiative transfer effect cause the CDR differences. In other words, the advanced information of clouds may lie hidden in the differences. Nakajima et al. (2010b) investigated the impact of the differences sensitivities to particle size and the penetration depth in an attempt to explain the CDR differences found in by using a simple two-layer cloud model with the bi-modal size distribution functions. Their results showed the sensitivity differences between 1.6, 2.1 and 3.7μm bands to droplet sizes and their vertical stratification. In this study, we further investigate the impact of the vertical inhomogeneity structure including the drizzle by using a spectral-bin microphysics cloud model. We apply the 1-D radiative transfer computation to the numerical cloud fields generated by the cloud model, and retrieve the CDRs from the reflectances thus simulated at each band. We then compare the statistics of these retrieved CDRs with the CDRs obtained from MODIS observations and derive the sensitivity functions of the retrieved CDRs to the particle size and the optical depth from the sets of the droplet distribution functions predicted by the model and the retrieved CDRs. This study is an attempt to interpret the CDR differences in terms of the cloud vertical structure and the cloud particle growth processes.

Implicit Shape Models for Object Detection in 3d Point Clouds

NASA Astrophysics Data System (ADS)

Velizhev, A.; Shapovalov, R.; Schindler, K.

2012-07-01

We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m2 of urban area in total.

Fusion of multi-temporal Airborne Snow Observatory (ASO) lidar data for mountainous vegetation ecosystems studies.

NASA Astrophysics Data System (ADS)

Ferraz, A.; Painter, T. H.; Saatchi, S.; Bormann, K. J.

2016-12-01

Fusion of multi-temporal Airborne Snow Observatory (ASO) lidar data for mountainous vegetation ecosystems studies The NASA Jet Propulsion Laboratory developed the Airborne Snow Observatory (ASO), a coupled scanning lidar system and imaging spectrometer, to quantify the spatial distribution of snow volume and dynamics over mountains watersheds (Painter et al., 2015). To do this, ASO weekly over-flights mountainous areas during snowfall and snowmelt seasons. In addition, there are additional flights in snow-off conditions to calculate Digital Terrain Models (DTM). In this study, we focus on the reliability of ASO lidar data to characterize the 3D forest vegetation structure. The density of a single point cloud acquisition is of nearly 1 pt/m2, which is not optimal to properly characterize vegetation. However, ASO covers a given study site up to 14 times a year that enables computing a high-resolution point cloud by merging single acquisitions. In this study, we present a method to automatically register ASO multi-temporal lidar 3D point clouds. Although flight specifications do not change between acquisition dates, lidar datasets might have significant planimetric shifts due to inaccuracies in platform trajectory estimation introduced by the GPS system and drifts of the IMU. There are a large number of methodologies that address the problem of 3D data registration (Gressin et al., 2013). Briefly, they look for common primitive features in both datasets such as buildings corners, structures like electric poles, DTM breaklines or deformations. However, they are not suited for our experiment. First, single acquisition point clouds have low density that makes the extraction of primitive features difficult. Second, the landscape significantly changes between flights due to snowfall and snowmelt. Therefore, we developed a method to automatically register point clouds using tree apexes as keypoints because they are features that are supposed to experience little change during winter season. We applied the method to 14 lidar datasets (12 snow-on and 2 snow-off) acquired over the Tuolumne River Basin (California) in the year of 2014. To assess the reliability of the merged point cloud, we analyze the quality of vegetation related products such as canopy height models (CHM) and vertical vegetation profiles.

A Multi-scale Modeling System with Unified Physics to Study Precipitation Processes

NASA Astrophysics Data System (ADS)

Tao, W. K.

2017-12-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), and (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF). The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the precipitation, processes and their sensitivity on model resolution and microphysics schemes will be presented. Also how to use of the multi-satellite simulator to improve precipitation processes will be discussed.

Using Multi-Scale Modeling Systems and Satellite Data to Study the Precipitation Processes

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Chern, J.; Lamg, S.; Matsui, T.; Shen, B.; Zeng, X.; Shi, R.

2011-01-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the recent developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the precipitating systems and hurricanes/typhoons will be presented. The high-resolution spatial and temporal visualization will be utilized to show the evolution of precipitation processes. Also how to use of the multi-satellite simulator tqimproy precipitation processes will be discussed.

A Method to Analyze How Various Parts of Clouds Influence Each Other's Brightness

NASA Technical Reports Server (NTRS)

Varnai, Tamas; Marshak, Alexander; Lau, William (Technical Monitor)

2001-01-01

This paper proposes a method for obtaining new information on 3D radiative effects that arise from horizontal radiative interactions in heterogeneous clouds. Unlike current radiative transfer models, it can not only calculate how 3D effects change radiative quantities at any given point, but can also determine which areas contribute to these 3D effects, to what degree, and through what mechanisms. After describing the proposed method, the paper illustrates its new capabilities both for detailed case studies and for the statistical processing of large datasets. Because the proposed method makes it possible, for the first time, to link a particular change in cloud properties to the resulting 3D effect, in future studies it can be used to develop new radiative transfer parameterizations that would consider 3D effects in practical applications currently limited to 1D theory-such as remote sensing of cloud properties and dynamical cloud modeling.

Impact of entrainment on cloud droplet spectra: theory, observations, and modeling

NASA Astrophysics Data System (ADS)

Grabowski, W.

2016-12-01

Understanding the impact of entrainment and mixing on microphysical properties of warm boundary layer clouds is an important aspect of the representation of such clouds in large-scale models of weather and climate. Entrainment leads to a reduction of the liquid water content in agreement with the fundamental thermodynamics, but its impact on the droplet spectrum is difficult to quantify in observations and modeling. For in-situ (e.g., aircraft) observations, it is impossible to follow air parcels and observe processes that lead to changes of the droplet spectrum in different regions of a cloud. For similar reasons traditional modeling methodologies (e.g., the Eulerian large eddy simulation) are not useful either. Moreover, both observations and modeling can resolve only relatively narrow range of spatial scales. Theory, typically focusing on differences between idealized concepts of homogeneous and inhomogeneous mixing, is also of a limited use for the multiscale turbulent mixing between a cloud and its environment. This presentation will illustrate the above points and argue that the Lagrangian large-eddy simulation with appropriate subgrid-scale scheme may provide key insights and eventually lead to novel parameterizations for large-scale models.

Reconstruction of 3d Models from Point Clouds with Hybrid Representation

NASA Astrophysics Data System (ADS)

Hu, P.; Dong, Z.; Yuan, P.; Liang, F.; Yang, B.

2018-05-01

The three-dimensional (3D) reconstruction of urban buildings from point clouds has long been an active topic in applications related to human activities. However, due to the structures significantly differ in terms of complexity, the task of 3D reconstruction remains a challenging issue especially for the freeform surfaces. In this paper, we present a new reconstruction algorithm which allows the 3D-models of building as a combination of regular structures and irregular surfaces, where the regular structures are parameterized plane primitives and the irregular surfaces are expressed as meshes. The extraction of irregular surfaces starts with an over-segmented method for the unstructured point data, a region growing approach based the adjacent graph of super-voxels is then applied to collapse these super-voxels, and the freeform surfaces can be clustered from the voxels filtered by a thickness threshold. To achieve these regular planar primitives, the remaining voxels with a larger flatness will be further divided into multiscale super-voxels as basic units, and the final segmented planes are enriched and refined in a mutually reinforcing manner under the framework of a global energy optimization. We have implemented the proposed algorithms and mainly tested on two point clouds that differ in point density and urban characteristic, and experimental results on complex building structures illustrated the efficacy of the proposed framework.

Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

PubMed Central

Rosnell, Tomi; Honkavaara, Eija

2012-01-01

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems’ SOCET SET classical commercial photogrammetric software and another is built using Microsoft®’s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation. PMID:22368479

Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera.

PubMed

Rosnell, Tomi; Honkavaara, Eija

2012-01-01

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems' SOCET SET classical commercial photogrammetric software and another is built using Microsoft(®)'s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation.

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Li, X.; Khain, A.; Simpson, S.; Johnson, D.; Remer, L.

2004-01-01

Cloud microphysics is inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, two detailed spectral-bin microphysical schemes were implemented into the Goddard Cumulus Ensembel (GCE) model. The formulation for the explicit spectral-bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e. pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e. 33 bins). Atmospheric aerosols are also described using number density size distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and in the mid-latitude continent with different concentrations of CCN: a low "c1ean"concentration and a high "dirty" concentration. In addition, differences and similarities between bulk microphysics and spectral-bin microphysical schemes will be examined and discussed.

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-resolving Model Simulations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Li, X.; Khain, A.; Simpson, S.; Johnson, D.; Remer, L.

2004-01-01

Cloud microphysics is inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, r d a U production, and rainfall rates for convective clouds. Recently, two detailed spectral-bin microphysical schemes were implemented into the Goddard Cumulus Ensembe1 (GCE) model. The formulation for the explicit spectral-bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e. pristine ice crystals (columnar and platelike), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e. 33 bins). Atmospheric aerosols are also described using number density size-distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and in the mid-latitude continent with different concentrations of CCN: a low "c1ean"concentration and a high "dirty" concentration. In addition, differences and similarities between bulk microphysics and spectral-bin microphysical schemes will be examined and discussed.

HCl in rocket exhaust clouds - Atmospheric dispersion, acid aerosol characteristics, and acid rain deposition

NASA Technical Reports Server (NTRS)

Pellett, G. L.; Sebacher, D. I.; Bendura, R. J.; Wornom, D. E.

1983-01-01

Both measurements and model calculations of the temporal dispersion of peak HCl (g + aq) concentration in Titan III exhaust clouds are found to be well characterized by one-term power-law decay expressions. The respective coefficients and decay exponents, however, are found to vary widely with meteorology. The HCl (g), HCl (g + aq), dewpoint, and temperature-pressure-altitude data for Titan III exhaust clouds are consistent with accurately calculated HCl/H2O vapor-liquid compositions for a model quasi-equilibrated flat surface aqueous aerosol. Some cloud evolution characteristics are also defined. Rapid and extensive condensation of aqueous acid clearly occurs during the first three min of cloud rise. Condensation is found to be intensified by the initial entrainment of relatively moist ambient air from lower levels, that is, from levels below eventual cloud stabilization. It is pointed out that if subsequent dilution air at stabilization altitude is significantly drier, a state of maximum condensation soon occurs, followed by an aerosol evaporation phase.

The Influence of Cloud Field Uniformity on Observed Cloud Amount

NASA Astrophysics Data System (ADS)

Riley, E.; Kleiss, J.; Kassianov, E.; Long, C. N.; Riihimaki, L.; Berg, L. K.

2017-12-01

Two ground-based measurements of cloud amount include cloud fraction (CF) obtained from time series of zenith-pointing radar-lidar observations and fractional sky cover (FSC) acquired from a Total Sky Imager (TSI). In comparison with the radars and lidars, the TSI has a considerably larger field of view (FOV 100° vs. 0.2°) and therefore is expected to have a different sensitivity to inhomogeneity in a cloud field. Radiative transfer calculations based on cloud properties retrieved from narrow-FOV overhead cloud observations may differ from shortwave and longwave flux observations due to spatial variability in local cloud cover. This bias will impede radiative closure for sampling reasons rather than the accuracy of cloud microphysics retrievals or radiative transfer calculations. Furthermore, the comparison between observed and modeled cloud amount from large eddy simulations (LES) models may be affected by cloud field inhomogeneity. The main goal of our study is to estimate the anticipated impact of cloud field inhomogeneity on the level of agreement between CF and FSC. We focus on shallow cumulus clouds observed at the U.S. Department of Energy Atmospheric Radiation Measurement Facility's Southern Great Plains (SGP) site in Oklahoma, USA. Our analysis identifies cloud field inhomogeneity using a novel metric that quantifies the spatial and temporal uniformity of FSC over 100-degree FOV TSI images. We demonstrate that (1) large differences between CF and FSC are partly attributable to increases in inhomogeneity and (2) using the uniformity metric can provide a meaningful assessment of uncertainties in observed cloud amount to aide in comparing ground-based measurements to radiative transfer or LES model outputs at SGP.

ARM Cloud Radar Simulator Package for Global Climate Models Value-Added Product

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yuying; Xie, Shaocheng

It has been challenging to directly compare U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility ground-based cloud radar measurements with climate model output because of limitations or features of the observing processes and the spatial gap between model and the single-point measurements. To facilitate the use of ARM radar data in numerical models, an ARM cloud radar simulator was developed to converts model data into pseudo-ARM cloud radar observations that mimic the instrument view of a narrow atmospheric column (as compared to a large global climate model [GCM] grid-cell), thus allowing meaningful comparison between model outputmore » and ARM cloud observations. The ARM cloud radar simulator value-added product (VAP) was developed based on the CloudSat simulator contained in the community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) (Bodas-Salcedo et al., 2011), which has been widely used in climate model evaluation with satellite data (Klein et al., 2013, Zhang et al., 2010). The essential part of the CloudSat simulator is the QuickBeam radar simulator that is used to produce CloudSat-like radar reflectivity, but is capable of simulating reflectivity for other radars (Marchand et al., 2009; Haynes et al., 2007). Adapting QuickBeam to the ARM cloud radar simulator within COSP required two primary changes: one was to set the frequency to 35 GHz for the ARM Ka-band cloud radar, as opposed to 94 GHz used for the CloudSat W-band radar, and the second was to invert the view from the ground to space so as to attenuate the beam correctly. In addition, the ARM cloud radar simulator uses a finer vertical resolution (100 m compared to 500 m for CloudSat) to resolve the more detailed structure of clouds captured by the ARM radars. The ARM simulator has been developed following the COSP workflow (Figure 1) and using the capabilities available in COSP wherever possible. The ARM simulator is written in Fortran 90, just as is the COSP. It is incorporated into COSP to facilitate use by the climate modeling community. In order to evaluate simulator output, the observational counterpart of the simulator output, radar reflectivity-height histograms (CFAD) is also generated from the ARM observations. This report includes an overview of the ARM cloud radar simulator VAP and the required simulator-oriented ARM radar data product (radarCFAD) for validating simulator output, as well as a user guide for operating the ARM radar simulator VAP.« less

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Weverberg, K.; Morcrette, C. J.; Petch, J.

Many numerical weather prediction (NWP) and climate models exhibit too warm lower tropospheres near the mid-latitude continents. This warm bias has been extensively studied before, but evidence about its origin remains inconclusive. Some studies point to deficiencies in the deep convective or low clouds. Other studies found an important contribution from errors in the land surface properties. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. Documenting these radiation errors is hence an important step towards understanding and alleviating themore » warm bias. This paper presents an attribution study to quantify the net radiation biases in 9 model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, integrated water vapor (IWV) and aerosols are quantified, using an array of radiation measurement stations near the ARM SGP site. Furthermore, an in depth-analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface SW radiation is overestimated (LW underestimated) in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation in all but one model, which has a dominant albedo issue. Using a cloud regime analysis, it was shown that missing deep cloud events and/or simulating deep clouds with too weak cloud-radiative effects account for most of these cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud, but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly however, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, the deep cloud problem in many models could be related to too weak convective cloud detrainment and too large precipitation efficiencies. This does not rule out that previously documented issues with the evaporative fraction contribute to the warm bias as well, since the majority of the models underestimate the surface rain rates overall, as they miss the observed large nocturnal precipitation peak.« less

Comparative verification between GEM model and official aviation terminal forecasts

NASA Technical Reports Server (NTRS)

Miller, Robert G.

1988-01-01

The Generalized Exponential Markov (GEM) model uses the local standard airways observation (SAO) to predict hour-by-hour the following elements: temperature, pressure, dew point depression, first and second cloud-layer height and amount, ceiling, total cloud amount, visibility, wind, and present weather conditions. GEM is superior to persistence at all projections for all elements in a large independent sample. A minute-by-minute GEM forecasting system utilizing the Automated Weather Observation System (AWOS) is under development.

a Method of 3d Measurement and Reconstruction for Cultural Relics in Museums

NASA Astrophysics Data System (ADS)

Zheng, S.; Zhou, Y.; Huang, R.; Zhou, L.; Xu, X.; Wang, C.

2012-07-01

Three-dimensional measurement and reconstruction during conservation and restoration of cultural relics have become an essential part of a modem museum regular work. Although many kinds of methods including laser scanning, computer vision and close-range photogrammetry have been put forward, but problems still exist, such as contradiction between cost and good result, time and fine effect. Aimed at these problems, this paper proposed a structure-light based method for 3D measurement and reconstruction of cultural relics in museums. Firstly, based on structure-light principle, digitalization hardware has been built and with its help, dense point cloud of cultural relics' surface can be easily acquired. To produce accurate 3D geometry model from point cloud data, multi processing algorithms have been developed and corresponding software has been implemented whose functions include blunder detection and removal, point cloud alignment and merge, 3D mesh construction and simplification. Finally, high-resolution images are captured and the alignment of these images and 3D geometry model is conducted and realistic, accurate 3D model is constructed. Based on such method, a complete system including hardware and software are built. Multi-kinds of cultural relics have been used to test this method and results prove its own feature such as high efficiency, high accuracy, easy operation and so on.

Physical modeling of 3D and 4D laser imaging

NASA Astrophysics Data System (ADS)

Anna, Guillaume; Hamoir, Dominique; Hespel, Laurent; Lafay, Fabien; Rivière, Nicolas; Tanguy, Bernard

2010-04-01

Laser imaging offers potential for observation, for 3D terrain-mapping and classification as well as for target identification, including behind vegetation, camouflage or glass windows, at day and night, and under all-weather conditions. First generation systems deliver 3D point clouds. The threshold detection is largely affected by the local opto-geometric characteristics of the objects, leading to inaccuracies in the distances measured, and by partial occultation, leading to multiple echos. Second generation systems circumvent these limitations by recording the temporal waveforms received by the system, so that data processing can improve the telemetry and the point cloud better match the reality. Future algorithms may exploit the full potential of the 4D full-waveform data. Hence, being able to simulate point-cloud (3D) and full-waveform (4D) laser imaging is key. We have developped a numerical model for predicting the output data of 3D or 4D laser imagers. The model does account for the temporal and transverse characteristics of the laser pulse (i.e. of the "laser bullet") emitted by the system, its propagation through turbulent and scattering atmosphere, its interaction with the objects present in the field of view, and the characteristics of the optoelectronic reception path of the system.

3D modeling of building indoor spaces and closed doors from imagery and point clouds.

PubMed

Díaz-Vilariño, Lucía; Khoshelham, Kourosh; Martínez-Sánchez, Joaquín; Arias, Pedro

2015-02-03

3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.

Motion Estimation System Utilizing Point Cloud Registration

NASA Technical Reports Server (NTRS)

Chen, Qi (Inventor)

2016-01-01

A system and method of estimation motion of a machine is disclosed. The method may include determining a first point cloud and a second point cloud corresponding to an environment in a vicinity of the machine. The method may further include generating a first extended gaussian image (EGI) for the first point cloud and a second EGI for the second point cloud. The method may further include determining a first EGI segment based on the first EGI and a second EGI segment based on the second EGI. The method may further include determining a first two dimensional distribution for points in the first EGI segment and a second two dimensional distribution for points in the second EGI segment. The method may further include estimating motion of the machine based on the first and second two dimensional distributions.

Point Cloud Analysis for Conservation and Enhancement of Modernist Architecture

NASA Astrophysics Data System (ADS)

Balzani, M.; Maietti, F.; Mugayar Kühl, B.

2017-02-01

Documentation of cultural assets through improved acquisition processes for advanced 3D modelling is one of the main challenges to be faced in order to address, through digital representation, advanced analysis on shape, appearance and conservation condition of cultural heritage. 3D modelling can originate new avenues in the way tangible cultural heritage is studied, visualized, curated, displayed and monitored, improving key features such as analysis and visualization of material degradation and state of conservation. An applied research focused on the analysis of surface specifications and material properties by means of 3D laser scanner survey has been developed within the project of Digital Preservation of FAUUSP building, Faculdade de Arquitetura e Urbanismo da Universidade de São Paulo, Brazil. The integrated 3D survey has been performed by the DIAPReM Center of the Department of Architecture of the University of Ferrara in cooperation with the FAUUSP. The 3D survey has allowed the realization of a point cloud model of the external surfaces, as the basis to investigate in detail the formal characteristics, geometric textures and surface features. The digital geometric model was also the basis for processing the intensity values acquired by laser scanning instrument; this method of analysis was an essential integration to the macroscopic investigations in order to manage additional information related to surface characteristics displayable on the point cloud.

Architectural Heritage Documentation by Using Low Cost Uav with Fisheye Lens: Otag-I Humayun in Istanbul as a Case Study

NASA Astrophysics Data System (ADS)

Yastikli, N.; Özerdem, Ö. Z.

2017-11-01

The digital documentation of architectural heritage is important for monitoring, preserving, managing as well as 3B BIM modelling, time-space VR (virtual reality) applications. The unmanned aerial vehicles (UAVs) have been widely used in these application thanks to rapid developments in technology which enable the high resolution images with resolutions in millimeters. Moreover, it has become possible to produce highly accurate 3D point clouds with structure from motion (SfM) and multi-view stereo (MVS), to obtain a surface reconstruction of a realistic 3D architectural heritage model by using high-overlap images and 3D modeling software such as Context capture, Pix4Dmapper, Photoscan. In this study, digital documentation of Otag-i Humayun (The Ottoman Empire Sultan's Summer Palace) located in Davutpaşa, Istanbul/Turkey is aimed using low cost UAV. The data collections have been made with low cost UAS 3DR Solo UAV with GoPro Hero 4 with fisheye lens. The data processing was accomplished by using commercial Pix4D software. The dense point clouds, a true orthophoto and 3D solid model of the Otag-i Humayun were produced results. The quality check of the produced point clouds has been performed. The obtained result from Otag-i Humayun in Istanbul proved that, the low cost UAV with fisheye lens can be successfully used for architectural heritage documentation.

Validation of Point Clouds Segmentation Algorithms Through Their Application to Several Case Studies for Indoor Building Modelling

NASA Astrophysics Data System (ADS)

Macher, H.; Landes, T.; Grussenmeyer, P.

2016-06-01

Laser scanners are widely used for the modelling of existing buildings and particularly in the creation process of as-built BIM (Building Information Modelling). However, the generation of as-built BIM from point clouds involves mainly manual steps and it is consequently time consuming and error-prone. Along the path to automation, a three steps segmentation approach has been developed. This approach is composed of two phases: a segmentation into sub-spaces namely floors and rooms and a plane segmentation combined with the identification of building elements. In order to assess and validate the developed approach, different case studies are considered. Indeed, it is essential to apply algorithms to several datasets and not to develop algorithms with a unique dataset which could influence the development with its particularities. Indoor point clouds of different types of buildings will be used as input for the developed algorithms, going from an individual house of almost one hundred square meters to larger buildings of several thousand square meters. Datasets provide various space configurations and present numerous different occluding objects as for example desks, computer equipments, home furnishings and even wine barrels. For each dataset, the results will be illustrated. The analysis of the results will provide an insight into the transferability of the developed approach for the indoor modelling of several types of buildings.

Pointo - a Low Cost Solution to Point Cloud Processing

NASA Astrophysics Data System (ADS)

Houshiar, H.; Winkler, S.

2017-11-01

With advance in technology access to data especially 3D point cloud data becomes more and more an everyday task. 3D point clouds are usually captured with very expensive tools such as 3D laser scanners or very time consuming methods such as photogrammetry. Most of the available softwares for 3D point cloud processing are designed for experts and specialists in this field and are usually very large software packages containing variety of methods and tools. This results in softwares that are usually very expensive to acquire and also very difficult to use. Difficulty of use is caused by complicated user interfaces that is required to accommodate a large list of features. The aim of these complex softwares is to provide a powerful tool for a specific group of specialist. However they are not necessary required by the majority of the up coming average users of point clouds. In addition to complexity and high costs of these softwares they generally rely on expensive and modern hardware and only compatible with one specific operating system. Many point cloud customers are not point cloud processing experts or willing to spend the high acquisition costs of these expensive softwares and hardwares. In this paper we introduce a solution for low cost point cloud processing. Our approach is designed to accommodate the needs of the average point cloud user. To reduce the cost and complexity of software our approach focuses on one functionality at a time in contrast with most available softwares and tools that aim to solve as many problems as possible at the same time. Our simple and user oriented design improve the user experience and empower us to optimize our methods for creation of an efficient software. In this paper we introduce Pointo family as a series of connected softwares to provide easy to use tools with simple design for different point cloud processing requirements. PointoVIEWER and PointoCAD are introduced as the first components of the Pointo family to provide a fast and efficient visualization with the ability to add annotation and documentation to the point clouds.

Novel Methods for Measuring LiDAR

NASA Astrophysics Data System (ADS)

Ayrey, E.; Hayes, D. J.; Fraver, S.; Weiskittel, A.; Cook, B.; Kershaw, J.

2017-12-01

The estimation of forest biometrics from airborne LiDAR data has become invaluable for quantifying forest carbon stocks, forest and wildlife ecology research, and sustainable forest management. The area-based approach is arguably the most common method for developing enhanced forest inventories from LiDAR. It involves taking a series of vertical height measurements of the point cloud, then using those measurements with field measured data to develop predictive models. Unfortunately, there is considerable variation in methodology for collecting point cloud data, which can vary in pulse density, seasonality, canopy penetrability, and instrument specifications. Today there exists a wealth of public LiDAR data, however the variation in acquisition parameters makes forest inventory prediction by traditional means unreliable across the different datasets. The goal of this project is to test a series of novel point cloud measurements developed along a conceptual spectrum of human interpretability, and then to use the best measurements to develop regional enhanced forest inventories on Northern New England's and Atlantic Canada's public LiDAR. Similarly to a field-based inventory, individual tree crowns are being segmented, and summary statistics are being used as covariates. Established competition and structural indices are being generated using each tree's relationship to one another, whilst existing allometric equations are being used to estimate diameter and biomass of each tree measured in the LiDAR. Novel metrics measuring light interception, clusteredness, and rugosity are also being measured as predictors. On the other end of the human interpretability spectrum, convolutional neural networks are being employed to directly measure both the canopy height model, and the point clouds by scanning each using two and three dimensional kernals trained to identify features useful for predicting biological attributes such as biomass. Predictive models will be trained and tested against one another using 28 different sites and over 42 different LiDAR acquisitions. The optimal model will then be used to generate regional wall-to-wall forest inventories at a 10 m resolution.

Comparison of the different approaches to generate holograms from data acquired with a Kinect sensor

NASA Astrophysics Data System (ADS)

Kang, Ji-Hoon; Leportier, Thibault; Ju, Byeong-Kwon; Song, Jin Dong; Lee, Kwang-Hoon; Park, Min-Chul

2017-05-01

Data of real scenes acquired in real-time with a Kinect sensor can be processed with different approaches to generate a hologram. 3D models can be generated from a point cloud or a mesh representation. The advantage of the point cloud approach is that computation process is well established since it involves only diffraction and propagation of point sources between parallel planes. On the other hand, the mesh representation enables to reduce the number of elements necessary to represent the object. Then, even though the computation time for the contribution of a single element increases compared to a simple point, the total computation time can be reduced significantly. However, the algorithm is more complex since propagation of elemental polygons between non-parallel planes should be implemented. Finally, since a depth map of the scene is acquired at the same time than the intensity image, a depth layer approach can also be adopted. This technique is appropriate for a fast computation since propagation of an optical wavefront from one plane to another can be handled efficiently with the fast Fourier transform. Fast computation with depth layer approach is convenient for real time applications, but point cloud method is more appropriate when high resolution is needed. In this study, since Kinect can be used to obtain both point cloud and depth map, we examine the different approaches that can be adopted for hologram computation and compare their performance.

a Hadoop-Based Algorithm of Generating dem Grid from Point Cloud Data

NASA Astrophysics Data System (ADS)

Jian, X.; Xiao, X.; Chengfang, H.; Zhizhong, Z.; Zhaohui, W.; Dengzhong, Z.

2015-04-01

Airborne LiDAR technology has proven to be the most powerful tools to obtain high-density, high-accuracy and significantly detailed surface information of terrain and surface objects within a short time, and from which the Digital Elevation Model of high quality can be extracted. Point cloud data generated from the pre-processed data should be classified by segmentation algorithms, so as to differ the terrain points from disorganized points, then followed by a procedure of interpolating the selected points to turn points into DEM data. The whole procedure takes a long time and huge computing resource due to high-density, that is concentrated on by a number of researches. Hadoop is a distributed system infrastructure developed by the Apache Foundation, which contains a highly fault-tolerant distributed file system (HDFS) with high transmission rate and a parallel programming model (Map/Reduce). Such a framework is appropriate for DEM generation algorithms to improve efficiency. Point cloud data of Dongting Lake acquired by Riegl LMS-Q680i laser scanner was utilized as the original data to generate DEM by a Hadoop-based algorithms implemented in Linux, then followed by another traditional procedure programmed by C++ as the comparative experiment. Then the algorithm's efficiency, coding complexity, and performance-cost ratio were discussed for the comparison. The results demonstrate that the algorithm's speed depends on size of point set and density of DEM grid, and the non-Hadoop implementation can achieve a high performance when memory is big enough, but the multiple Hadoop implementation can achieve a higher performance-cost ratio, while point set is of vast quantities on the other hand.

Coupling between lower-tropospheric convective mixing and low-level clouds: Physical mechanisms and dependence on convection scheme.

PubMed

Vial, Jessica; Bony, Sandrine; Dufresne, Jean-Louis; Roehrig, Romain

2016-12-01

Several studies have pointed out the dependence of low-cloud feedbacks on the strength of the lower-tropospheric convective mixing. By analyzing a series of single-column model experiments run by a climate model using two different convective parametrizations, this study elucidates the physical mechanisms through which marine boundary-layer clouds depend on this mixing in the present-day climate and under surface warming. An increased lower-tropospheric convective mixing leads to a reduction of low-cloud fraction. However, the rate of decrease strongly depends on how the surface latent heat flux couples to the convective mixing and to boundary-layer cloud radiative effects: (i) on the one hand, the latent heat flux is enhanced by the lower-tropospheric drying induced by the convective mixing, which damps the reduction of the low-cloud fraction, (ii) on the other hand, the latent heat flux is reduced as the lower troposphere stabilizes under the effect of reduced low-cloud radiative cooling, which enhances the reduction of the low-cloud fraction. The relative importance of these two different processes depends on the closure of the convective parameterization. The convective scheme that favors the coupling between latent heat flux and low-cloud radiative cooling exhibits a stronger sensitivity of low-clouds to convective mixing in the present-day climate, and a stronger low-cloud feedback in response to surface warming. In this model, the low-cloud feedback is stronger when the present-day convective mixing is weaker and when present-day clouds are shallower and more radiatively active. The implications of these insights for constraining the strength of low-cloud feedbacks observationally is discussed.

Coupling between lower‐tropospheric convective mixing and low‐level clouds: Physical mechanisms and dependence on convection scheme

PubMed Central

Bony, Sandrine; Dufresne, Jean‐Louis; Roehrig, Romain

2016-01-01

Abstract Several studies have pointed out the dependence of low‐cloud feedbacks on the strength of the lower‐tropospheric convective mixing. By analyzing a series of single‐column model experiments run by a climate model using two different convective parametrizations, this study elucidates the physical mechanisms through which marine boundary‐layer clouds depend on this mixing in the present‐day climate and under surface warming. An increased lower‐tropospheric convective mixing leads to a reduction of low‐cloud fraction. However, the rate of decrease strongly depends on how the surface latent heat flux couples to the convective mixing and to boundary‐layer cloud radiative effects: (i) on the one hand, the latent heat flux is enhanced by the lower‐tropospheric drying induced by the convective mixing, which damps the reduction of the low‐cloud fraction, (ii) on the other hand, the latent heat flux is reduced as the lower troposphere stabilizes under the effect of reduced low‐cloud radiative cooling, which enhances the reduction of the low‐cloud fraction. The relative importance of these two different processes depends on the closure of the convective parameterization. The convective scheme that favors the coupling between latent heat flux and low‐cloud radiative cooling exhibits a stronger sensitivity of low‐clouds to convective mixing in the present‐day climate, and a stronger low‐cloud feedback in response to surface warming. In this model, the low‐cloud feedback is stronger when the present‐day convective mixing is weaker and when present‐day clouds are shallower and more radiatively active. The implications of these insights for constraining the strength of low‐cloud feedbacks observationally is discussed. PMID:28239438

Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.

PubMed

De Queiroz, Ricardo; Chou, Philip A

2016-06-01

In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time and with the recent possibility of real time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably to the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this work represents the state-of-the-art in intra-frame compression of point clouds for real-time 3D video.

The Global Evolution of Giant Molecular Clouds. I. Model Formulation and Quasi-Equilibrium Behavior

NASA Astrophysics Data System (ADS)

Krumholz, Mark R.; Matzner, Christopher D.; McKee, Christopher F.

2006-12-01

We present semianalytic dynamical models for giant molecular clouds evolving under the influence of H II regions launched by newborn star clusters. In contrast to previous work, we neither assume that clouds are in virial or energetic equilibrium, nor do we ignore the effects of star formation feedback. The clouds, which we treat as spherical, can expand and contract homologously. Photoionization drives mass ejection; the recoil of cloud material both stirs turbulent motions and leads to an effective confining pressure. The balance between these effects and the decay of turbulent motions through isothermal shocks determines clouds' dynamical and energetic evolution. We find that for realistic values of the rates of turbulent dissipation, photoevaporation, and energy injection by H II regions, the massive clouds where most molecular gas in the Galaxy resides live for a few crossing times, in good agreement with recent observational estimates that large clouds in Local Group galaxies survive roughly 20-30 Myr. During this time clouds remain close to equilibrium, with virial parameters of 1-3 and column densities near 1022 H atoms cm-2, also in agreement with observed cloud properties. Over their lives they convert 5%-10% of their mass into stars, after which point most clouds are destroyed when a large H II region unbinds them. In contrast, small clouds like those found in the solar neighborhood only survive ~1 crossing time before being destroyed.

Assessing modelled spatial distributions of ice water path using satellite data

NASA Astrophysics Data System (ADS)

Eliasson, S.; Buehler, S. A.; Milz, M.; Eriksson, P.; John, V. O.

2010-05-01

The climate models used in the IPCC AR4 show large differences in monthly mean cloud ice. The most valuable source of information that can be used to potentially constrain the models is global satellite data. For this, the data sets must be long enough to capture the inter-annual variability of Ice Water Path (IWP). PATMOS-x was used together with ISCCP for the annual cycle evaluation in Fig. 7 while ECHAM-5 was used for the correlation with other models in Table 3. A clear distinction between ice categories in satellite retrievals, as desired from a model point of view, is currently impossible. However, long-term satellite data sets may still be used to indicate the climatology of IWP spatial distribution. We evaluated satellite data sets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, to determine which data sets can be used to evaluate the climate models. IWP data from CloudSat cloud profiling radar provides the most advanced data set on clouds. As CloudSat data are too short to evaluate the model data directly, it was mainly used here to evaluate IWP from the other satellite data sets. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the best of the two. As PATMOS-x extends over more than 25 years and is in fairly close agreement with CloudSat, it was chosen as the reference data set for the model evaluation. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is the GCM from IPCC AR4 closest to satellite observations.

Integration of Geodata in Documenting Castle Ruins

NASA Astrophysics Data System (ADS)

Delis, P.; Wojtkowska, M.; Nerc, P.; Ewiak, I.; Lada, A.

2016-06-01

Textured three dimensional models are currently the one of the standard methods of representing the results of photogrammetric works. A realistic 3D model combines the geometrical relations between the structure's elements with realistic textures of each of its elements. Data used to create 3D models of structures can be derived from many different sources. The most commonly used tool for documentation purposes, is a digital camera and nowadays terrestrial laser scanning (TLS). Integration of data acquired from different sources allows modelling and visualization of 3D models historical structures. Additional aspect of data integration is possibility of complementing of missing points for example in point clouds. The paper shows the possibility of integrating data from terrestrial laser scanning with digital imagery and an analysis of the accuracy of the presented methods. The paper describes results obtained from raw data consisting of a point cloud measured using terrestrial laser scanning acquired from a Leica ScanStation2 and digital imagery taken using a Kodak DCS Pro 14N camera. The studied structure is the ruins of the Ilza castle in Poland.

A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment

PubMed Central

Liu, Jian; Liang, Huawei; Wang, Zhiling; Chen, Xiangcheng

2015-01-01

The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the drivable area. A framework for the online modeling of the driving environment using a multi-beam LIDAR, i.e., a Velodyne HDL-64E LIDAR, which describes the 3D environment in the form of a point cloud, is reported in this article. First, ground segmentation is performed via multi-feature extraction of the raw data grabbed by the Velodyne LIDAR to satisfy the requirement of online environment modeling. Curbs and dynamic road obstacles are detected and tracked in different manners. Curves are fitted for curb points, and points are clustered into bundles whose form and kinematics parameters are calculated. The Kalman filter is used to track dynamic obstacles, whereas the snake model is employed for curbs. Results indicate that the proposed framework is robust under various environments and satisfies the requirements for online processing. PMID:26404290

Normalized Implicit Radial Models for Scattered Point Cloud Data without Normal Vectors

DTIC Science & Technology

2009-03-23

points by shrinking a discrete membrane, Computer Graphics Forum, Vol. 24-4, 2005, pp. 791-808 [8] Floater , M. S., Reimers, M.: Meshless...Parameterization and Surface Reconstruction, Computer Aided Geometric Design 18, 2001, pp 77-92 [9] Floater , M. S.: Parameterization of Triangulations and...Unorganized Points, In: Tutorials on Multiresolution in Geometric Modelling, A. Iske, E. Quak and M. S. Floater (eds.), Springer , 2002, pp. 287-316 [10

Assessment of the Quality of Digital Terrain Model Produced from Unmanned Aerial System Imagery

NASA Astrophysics Data System (ADS)

Kosmatin Fras, M.; Kerin, A.; Mesarič, M.; Peterman, V.; Grigillo, D.

2016-06-01

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

a 33GHZ and 95GHZ Cloud Profiling Radar System (cprs): Preliminary Estimates of Particle Size in Precipitation and Clouds.

NASA Astrophysics Data System (ADS)

Sekelsky, Stephen Michael

1995-11-01

The Microwave Remote Sensing Laboratory (MIRSL) st the University of Massachusetts has developed a unique single antenna, dual-frequency polarimetric Cloud Profiling Radar System (CPRS). This project was funded by the Department of Energy's Atmospheric Radiation Measurement (ARM) program, and was intended to help fill the void of ground-based remote sensors capable of characterizing cloud microphysical properties. CPRS is unique in that it can simultaneously measure the complex power backscattered from clouds at 33 GHz and 95 GHz through the same aperture. Both the 33 GHz and 95 GHz channels can transmit pulse-to-pulse selectable vertical or horizontal polarization, and simultaneously record both the copolarized and crosspolarized backscatter. CPRS Doppler, polarimetric and dual-wavelength reflectivity measurements combined with in situ cloud measurements should lead to the development of empirical models that can more accurately classify cloud-particle phase and habit, and make better quantitative estimates of particle size distribution parameters. This dissertation describes the CPRS hardware, and presents colocated 33 GHz and 95 GHz measurements that illustrate the use of dual-frequency measurements to estimate particle size when Mie scattering, is observed in backscatter from rain and ice-phase clouds. Polarimetric measurements are presented as a means of discriminating cloud phase (ice-water) and estimating crystal shape in cirrus clouds. Polarimetric and dual-wavelength observations of insects are also presented with a brief discussion of their impact on the interpretation of precipitation and liquid cloud measurements. In precipitation, Diermendjian's equations for Mie backscatter (1) and the Marshal-Palmer drop-size distribution are used to develop models relating differences in the reflectivity and mean velocity at 33 GHz and 95 GHz to the microphysical parameters of rain. These models are then used to estimate mean droplet size from CPRS measurements of drizzle, which were collected in July, 1993 during the system's first field test in Lincoln, NE. The dissertation also presents cirrus cloud and other measurements collected during the DOE-sponsored Remote Cloud Sensing Intensive Operations Period (RCS-IOP) experiment in April, 1994. Zenith-pointing cirrus measurements show small differences in 33 GHz and 95 GHz reflectivity, as models have predicted (2). Depolarization was also detected in a few cases when ice crystals precipitated from the base of a cloud. On May 29, 1994 CPRS observed a convective storm that produced a cirrus anvil cloud and hail. These storms are one 'engine' producing cirrus clouds and are currently a topic of intensive research by climatologists. Both zenith-pointing and range-height data formats are presented. Measurements of depolarization above the melting/layer are compared to in situ observations of particle size and shape. The RCS-IOP experiment also provided a first opportunity to verify our calibration with aircraft in situ measurements, and to compare our cloud measurements to those collected by other remote sensors. (Abstract shortened by UMI.).

Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds.

PubMed

Abraham, Leandro; Bromberg, Facundo; Forradellas, Raymundo

2018-04-01

Muscle activation level is currently being captured using impractical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non-invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras. A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturbations in the point of view of the capturing device, greatly simplifying the installation process for end-users. In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g), the best variant of the proposed methodology achieved mean absolute errors of about 9.21% MVC - an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical. The results prove that the correlations between the external geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy. Copyright © 2018 Elsevier Ltd. All rights reserved.

D Data Acquisition Based on Opencv for Close-Range Photogrammetry Applications

NASA Astrophysics Data System (ADS)

Jurjević, L.; Gašparović, M.

2017-05-01

Development of the technology in the area of the cameras, computers and algorithms for 3D the reconstruction of the objects from the images resulted in the increased popularity of the photogrammetry. Algorithms for the 3D model reconstruction are so advanced that almost anyone can make a 3D model of photographed object. The main goal of this paper is to examine the possibility of obtaining 3D data for the purposes of the close-range photogrammetry applications, based on the open source technologies. All steps of obtaining 3D point cloud are covered in this paper. Special attention is given to the camera calibration, for which two-step process of calibration is used. Both, presented algorithm and accuracy of the point cloud are tested by calculating the spatial difference between referent and produced point clouds. During algorithm testing, robustness and swiftness of obtaining 3D data is noted, and certainly usage of this and similar algorithms has a lot of potential in the real-time application. That is the reason why this research can find its application in the architecture, spatial planning, protection of cultural heritage, forensic, mechanical engineering, traffic management, medicine and other sciences.

ROOFN3D: Deep Learning Training Data for 3d Building Reconstruction

NASA Astrophysics Data System (ADS)

Wichmann, A.; Agoub, A.; Kada, M.

2018-05-01

Machine learning methods have gained in importance through the latest development of artificial intelligence and computer hardware. Particularly approaches based on deep learning have shown that they are able to provide state-of-the-art results for various tasks. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a new 3D point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. It can be used, among others, to train semantic segmentation networks or to learn the structure of buildings and the geometric model construction. Further details about RoofN3D and the developed data preparation framework, which enables the automatic derivation of training data, are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to unstructured and not gridded 3D point cloud data are presented.

FPFH-based graph matching for 3D point cloud registration

NASA Astrophysics Data System (ADS)

Zhao, Jiapeng; Li, Chen; Tian, Lihua; Zhu, Jihua

2018-04-01

Correspondence detection is a vital step in point cloud registration and it can help getting a reliable initial alignment. In this paper, we put forward an advanced point feature-based graph matching algorithm to solve the initial alignment problem of rigid 3D point cloud registration with partial overlap. Specifically, Fast Point Feature Histograms are used to determine the initial possible correspondences firstly. Next, a new objective function is provided to make the graph matching more suitable for partially overlapping point cloud. The objective function is optimized by the simulated annealing algorithm for final group of correct correspondences. Finally, we present a novel set partitioning method which can transform the NP-hard optimization problem into a O(n3)-solvable one. Experiments on the Stanford and UWA public data sets indicates that our method can obtain better result in terms of both accuracy and time cost compared with other point cloud registration methods.

Accuracy analysis of point cloud modeling for evaluating concrete specimens

NASA Astrophysics Data System (ADS)

D'Amico, Nicolas; Yu, Tzuyang

2017-04-01

Photogrammetric methods such as structure from motion (SFM) have the capability to acquire accurate information about geometric features, surface cracks, and mechanical properties of specimens and structures in civil engineering. Conventional approaches to verify the accuracy in photogrammetric models usually require the use of other optical techniques such as LiDAR. In this paper, geometric accuracy of photogrammetric modeling is investigated by studying the effects of number of photos, radius of curvature, and point cloud density (PCD) on estimated lengths, areas, volumes, and different stress states of concrete cylinders and panels. Four plain concrete cylinders and two plain mortar panels were used for the study. A commercially available mobile phone camera was used in collecting all photographs. Agisoft PhotoScan software was applied in photogrammetric modeling of all concrete specimens. From our results, it was found that the increase of number of photos does not necessarily improve the geometric accuracy of point cloud models (PCM). It was also found that the effect of radius of curvature is not significant when compared with the ones of number of photos and PCD. A PCD threshold of 15.7194 pts/cm3 is proposed to construct reliable and accurate PCM for condition assessment. At this PCD threshold, all errors for estimating lengths, areas, and volumes were less than 5%. Finally, from the study of mechanical property of a plain concrete cylinder, we have found that the increase of stress level inside the concrete cylinder can be captured by the increase of radial strain in its PCM.

Accuracy assessment of modeling architectural structures and details using terrestrial laser scanning

NASA Astrophysics Data System (ADS)

Kedzierski, M.; Walczykowski, P.; Orych, A.; Czarnecka, P.

2015-08-01

One of the most important aspects when performing architectural documentation of cultural heritage structures is the accuracy of both the data and the products which are generated from these data: documentation in the form of 3D models or vector drawings. The paper describes an assessment of the accuracy of modelling data acquired using a terrestrial phase scanner in relation to the density of a point cloud representing the surface of different types of construction materials typical for cultural heritage structures. This analysis includes the impact of the scanning geometry: the incidence angle of the laser beam and the scanning distance. For the purposes of this research, a test field consisting of samples of different types of construction materials (brick, wood, plastic, plaster, a ceramic tile, sheet metal) was built. The study involved conducting measurements at different angles and from a range of distances for chosen scanning densities. Data, acquired in the form of point clouds, were then filtered and modelled. An accuracy assessment of the 3D model was conducted by fitting it with the point cloud. The reflection intensity of each type of material was also analyzed, trying to determine which construction materials have the highest reflectance coefficients, and which have the lowest reflection coefficients, and in turn how this variable changes for different scanning parameters. Additionally measurements were taken of a fragment of a building in order to compare the results obtained in laboratory conditions, with those taken in field conditions.

The Isprs Benchmark on Indoor Modelling

NASA Astrophysics Data System (ADS)

Khoshelham, K.; Díaz Vilariño, L.; Peter, M.; Kang, Z.; Acharya, D.

2017-09-01

Automated generation of 3D indoor models from point cloud data has been a topic of intensive research in recent years. While results on various datasets have been reported in literature, a comparison of the performance of different methods has not been possible due to the lack of benchmark datasets and a common evaluation framework. The ISPRS benchmark on indoor modelling aims to address this issue by providing a public benchmark dataset and an evaluation framework for performance comparison of indoor modelling methods. In this paper, we present the benchmark dataset comprising several point clouds of indoor environments captured by different sensors. We also discuss the evaluation and comparison of indoor modelling methods based on manually created reference models and appropriate quality evaluation criteria. The benchmark dataset is available for download at: http://www2.isprs.org/commissions/comm4/wg5/benchmark-on-indoor-modelling.html.

Patient-specific atrium models for training and pre-procedure surgical planning

NASA Astrophysics Data System (ADS)

Laing, Justin; Moore, John; Bainbridge, Daniel; Drangova, Maria; Peters, Terry

2017-03-01

Minimally invasive cardiac procedures requiring a trans-septal puncture such as atrial ablation and MitraClip® mitral valve repair are becoming increasingly common. These procedures are performed on the beating heart, and require clinicians to rely on image-guided techniques. For cases of complex or diseased anatomy, in which fluoroscopic and echocardiography images can be difficult to interpret, clinicians may benefit from patient-specific atrial models that can be used for training, surgical planning, and the validation of new devices and guidance techniques. Computed tomography (CT) images of a patient's heart were segmented and used to generate geometric models to create a patient-specific atrial phantom. Using rapid prototyping, the geometric models were converted into physical representations and used to build a mold. The atria were then molded using tissue-mimicking materials and imaged using CT. The resulting images were segmented and used to generate a point cloud data set that could be registered to the original patient data. The absolute distance of the two point clouds was compared and evaluated to determine the model's accuracy. The result when comparing the molded model point cloud to the original data set, resulted in a maximum Euclidean distance error of 4.5 mm, an average error of 0.5 mm and a standard deviation of 0.6 mm. Using our workflow for creating atrial models, potential complications, particularly for complex repairs, may be accounted for in pre-operative planning. The information gained by clinicians involved in planning and performing the procedure should lead to shorter procedural times and better outcomes for patients.

Probabilistic verification of cloud fraction from three different products with CALIPSO

NASA Astrophysics Data System (ADS)

Jung, B. J.; Descombes, G.; Snyder, C.

2017-12-01

In this study, we present how Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) can be used for probabilistic verification of cloud fraction, and apply this probabilistic approach to three cloud fraction products: a) The Air Force Weather (AFW) World Wide Merged Cloud Analysis (WWMCA), b) Satellite Cloud Observations and Radiative Property retrieval Systems (SatCORPS) from NASA Langley Research Center, and c) Multi-sensor Advection Diffusion nowCast (MADCast) from NCAR. Although they differ in their details, both WWMCA and SatCORPS retrieve cloud fraction from satellite observations, mainly of infrared radiances. MADCast utilizes in addition a short-range forecast of cloud fraction (provided by the Model for Prediction Across Scales, assuming cloud fraction is advected as a tracer) and a column-by-column particle filter implemented within the Gridpoint Statistical Interpolation (GSI) data-assimilation system. The probabilistic verification considers the retrieved or analyzed cloud fractions as predicting the probability of cloud at any location within a grid cell and the 5-km vertical feature mask (VFM) from CALIPSO level-2 products as a point observation of cloud.

Smart Point Cloud: Definition and Remaining Challenges

NASA Astrophysics Data System (ADS)

Poux, F.; Hallot, P.; Neuville, R.; Billen, R.

2016-10-01

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the smart point cloud. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.

Motion-Compensated Compression of Dynamic Voxelized Point Clouds.

PubMed

De Queiroz, Ricardo L; Chou, Philip A

2017-05-24

Dynamic point clouds are a potential new frontier in visual communication systems. A few articles have addressed the compression of point clouds, but very few references exist on exploring temporal redundancies. This paper presents a novel motion-compensated approach to encoding dynamic voxelized point clouds at low bit rates. A simple coder breaks the voxelized point cloud at each frame into blocks of voxels. Each block is either encoded in intra-frame mode or is replaced by a motion-compensated version of a block in the previous frame. The decision is optimized in a rate-distortion sense. In this way, both the geometry and the color are encoded with distortion, allowing for reduced bit-rates. In-loop filtering is employed to minimize compression artifacts caused by distortion in the geometry information. Simulations reveal that this simple motion compensated coder can efficiently extend the compression range of dynamic voxelized point clouds to rates below what intra-frame coding alone can accommodate, trading rate for geometry accuracy.

Solubilization of phenanthrene above cloud point of Brij 30: a new application in biodegradation.

PubMed

Pantsyrnaya, T; Delaunay, S; Goergen, J L; Guseva, E; Boudrant, J

2013-06-01

In the present study a new application of solubilization of phenanthrene above cloud point of Brij 30 in biodegradation was developed. It was shown that a temporal solubilization of phenanthrene above cloud point of Brij 30 (5wt%) permitted to obtain a stable increase of the solubility of phenanthrene even when the temperature was decreased to culture conditions of used microorganism Pseudomonas putida (28°C). A higher initial concentration of soluble phenanthrene was obtained after the cloud point treatment: 200 against 120μM without treatment. All soluble phenanthrene was metabolized and a higher final concentration of its major metabolite - 1-hydroxy-2-naphthoic acid - (160 against 85μM) was measured in the culture medium in the case of a preliminary cloud point treatment. Therefore a temporary solubilization at cloud point might have a perspective application in the enhancement of biodegradation of polycyclic aromatic hydrocarbons. Copyright © 2013 Elsevier Ltd. All rights reserved.

A portable low-cost 3D point cloud acquiring method based on structure light

NASA Astrophysics Data System (ADS)

Gui, Li; Zheng, Shunyi; Huang, Xia; Zhao, Like; Ma, Hao; Ge, Chao; Tang, Qiuxia

2018-03-01

A fast and low-cost method of acquiring 3D point cloud data is proposed in this paper, which can solve the problems of lack of texture information and low efficiency of acquiring point cloud data with only one pair of cheap cameras and projector. Firstly, we put forward a scene adaptive design method of random encoding pattern, that is, a coding pattern is projected onto the target surface in order to form texture information, which is favorable for image matching. Subsequently, we design an efficient dense matching algorithm that fits the projected texture. After the optimization of global algorithm and multi-kernel parallel development with the fusion of hardware and software, a fast acquisition system of point-cloud data is accomplished. Through the evaluation of point cloud accuracy, the results show that point cloud acquired by the method proposed in this paper has higher precision. What`s more, the scanning speed meets the demand of dynamic occasion and has better practical application value.

Modelling ice microphysics of mixed-phase clouds

NASA Astrophysics Data System (ADS)

Ahola, J.; Raatikainen, T.; Tonttila, J.; Romakkaniemi, S.; Kokkola, H.; Korhonen, H.

2017-12-01

The low-level Arctic mixed-phase clouds have a significant role for the Arctic climate due to their ability to absorb and reflect radiation. Since the climate change is amplified in polar areas, it is vital to apprehend the mixed-phase cloud processes. From a modelling point of view, this requires a high spatiotemporal resolution to capture turbulence and the relevant microphysical processes, which has shown to be difficult.In order to solve this problem about modelling mixed-phase clouds, a new ice microphysics description has been developed. The recently published large-eddy simulation cloud model UCLALES-SALSA offers a good base for a feasible solution (Tonttila et al., Geosci. Mod. Dev., 10:169-188, 2017). The model includes aerosol-cloud interactions described with a sectional SALSA module (Kokkola et al., Atmos. Chem. Phys., 8, 2469-2483, 2008), which represents a good compromise between detail and computational expense.Newly, the SALSA module has been upgraded to include also ice microphysics. The dynamical part of the model is based on well-known UCLA-LES model (Stevens et al., J. Atmos. Sci., 56, 3963-3984, 1999) which can be used to study cloud dynamics on a fine grid.The microphysical description of ice is sectional and the included processes consist of formation, growth and removal of ice and snow particles. Ice cloud particles are formed by parameterized homo- or heterogeneous nucleation. The growth mechanisms of ice particles and snow include coagulation and condensation of water vapor. Autoconversion from cloud ice particles to snow is parameterized. The removal of ice particles and snow happens by sedimentation and melting.The implementation of ice microphysics is tested by initializing the cloud simulation with atmospheric observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC). The results are compared to the model results shown in the paper of Ovchinnikov et al. (J. Adv. Model. Earth Syst., 6, 223-248, 2014) and they show a good match. One of the advantages of UCLALES-SALSA is that it can be used to quantify the effect of aerosol scavenging on cloud properties in a precise way.

Joint classification and contour extraction of large 3D point clouds

NASA Astrophysics Data System (ADS)

Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

2017-08-01

We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours. Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with >109 points.

Study of the Radiative Properties of Inhomogeneous Stratocumulus Clouds

NASA Technical Reports Server (NTRS)

Batey, Michael

1996-01-01

Clouds play an important role in the radiation budget of the atmosphere. A good understanding of how clouds interact with solar radiation is necessary when considering their effects in both general circulation models and climate models. This study examined the radiative properties of clouds in both an inhomogeneous cloud system, and a simplified cloud system through the use of a Monte Carlo model. The purpose was to become more familiar with the radiative properties of clouds, especially absorption, and to investigate the excess absorption of solar radiation from observations over that calculated from theory. The first cloud system indicated that the absorptance actually decreased as the cloud's inhomogeneity increased, and that cloud forcing does not indicate any changes. The simplified cloud system looked at two different cases of absorption of solar radiation in the cloud. The absorptances calculated from the Monte Carlo is compared to a correction method for calculating absorptances and found that the method can over or underestimate absorptances at cloud edges. Also the cloud edge effects due to solar radiation points to a possibility of overestimating the retrieved optical depth at the edge, and indicates a possible way to correct for it. The effective cloud fraction (Ne) for a long time has been calculated from a cloud's reflectance. From the reflectance it has been observed that the N, for most cloud geometries is greater than the actual cloud fraction (Nc) making a cloud appear wider than it is optically. Recent studies we have performed used a Monte Carlo model to calculate the N, of a cloud using not only the reflectance but also the absorptance. The derived Ne's from the absorptance in some of the Monte Carlo runs did not give the same results as derived from the reflectance. This study also examined the inhomogeneity of clouds to find a relationship between larger and smaller scales, or wavelengths, of the cloud. Both Fourier transforms and wavelet transforms were used to analyze the liquid water content of marine stratocumulus clouds taken during the ASTEX project. From the analysis it was found that the energy in the cloud is not uniformly distributed but is greater at the larger scales than at the smaller scales. This was determined by examining the slope of the power spectrum, and by comparing the variability at two scales from a wavelet analysis.

High-Precision Registration of Point Clouds Based on Sphere Feature Constraints.

PubMed

Huang, Junhui; Wang, Zhao; Gao, Jianmin; Huang, Youping; Towers, David Peter

2016-12-30

Point cloud registration is a key process in multi-view 3D measurements. Its precision affects the measurement precision directly. However, in the case of the point clouds with non-overlapping areas or curvature invariant surface, it is difficult to achieve a high precision. A high precision registration method based on sphere feature constraint is presented to overcome the difficulty in the paper. Some known sphere features with constraints are used to construct virtual overlapping areas. The virtual overlapping areas provide more accurate corresponding point pairs and reduce the influence of noise. Then the transformation parameters between the registered point clouds are solved by an optimization method with weight function. In that case, the impact of large noise in point clouds can be reduced and a high precision registration is achieved. Simulation and experiments validate the proposed method.

High-Precision Registration of Point Clouds Based on Sphere Feature Constraints

PubMed Central

Huang, Junhui; Wang, Zhao; Gao, Jianmin; Huang, Youping; Towers, David Peter

2016-01-01

Point cloud registration is a key process in multi-view 3D measurements. Its precision affects the measurement precision directly. However, in the case of the point clouds with non-overlapping areas or curvature invariant surface, it is difficult to achieve a high precision. A high precision registration method based on sphere feature constraint is presented to overcome the difficulty in the paper. Some known sphere features with constraints are used to construct virtual overlapping areas. The virtual overlapping areas provide more accurate corresponding point pairs and reduce the influence of noise. Then the transformation parameters between the registered point clouds are solved by an optimization method with weight function. In that case, the impact of large noise in point clouds can be reduced and a high precision registration is achieved. Simulation and experiments validate the proposed method. PMID:28042846

Biotoxicity and bioavailability of hydrophobic organic compounds solubilized in nonionic surfactant micelle phase and cloud point system.

PubMed

Pan, Tao; Liu, Chunyan; Zeng, Xinying; Xin, Qiao; Xu, Meiying; Deng, Yangwu; Dong, Wei

2017-06-01

A recent work has shown that hydrophobic organic compounds solubilized in the micelle phase of some nonionic surfactants present substrate toxicity to microorganisms with increasing bioavailability. However, in cloud point systems, biotoxicity is prevented, because the compounds are solubilized into a coacervate phase, thereby leaving a fraction of compounds with cells in a dilute phase. This study extends the understanding of the relationship between substrate toxicity and bioavailability of hydrophobic organic compounds solubilized in nonionic surfactant micelle phase and cloud point system. Biotoxicity experiments were conducted with naphthalene and phenanthrene in the presence of mixed nonionic surfactants Brij30 and TMN-3, which formed a micelle phase or cloud point system at different concentrations. Saccharomyces cerevisiae, unable to degrade these compounds, was used for the biotoxicity experiments. Glucose in the cloud point system was consumed faster than in the nonionic surfactant micelle phase, indicating that the solubilized compounds had increased toxicity to cells in the nonionic surfactant micelle phase. The results were verified by subsequent biodegradation experiments. The compounds were degraded faster by PAH-degrading bacterium in the cloud point system than in the micelle phase. All these results showed that biotoxicity of the hydrophobic organic compounds increases with bioavailability in the surfactant micelle phase but remains at a low level in the cloud point system. These results provide a guideline for the application of cloud point systems as novel media for microbial transformation or biodegradation.

An open, interoperable, transdisciplinary approach to a point cloud data service using OGC standards and open source software.

NASA Astrophysics Data System (ADS)

Steer, Adam; Trenham, Claire; Druken, Kelsey; Evans, Benjamin; Wyborn, Lesley

2017-04-01

High resolution point clouds and other topology-free point data sources are widely utilised for research, management and planning activities. A key goal for research and management users is making these data and common derivatives available in a way which is seamlessly interoperable with other observed and modelled data. The Australian National Computational Infrastructure (NCI) stores point data from a range of disciplines, including terrestrial and airborne LiDAR surveys, 3D photogrammetry, airborne and ground-based geophysical observations, bathymetric observations and 4D marine tracers. These data are stored alongside a significant store of Earth systems data including climate and weather, ecology, hydrology, geoscience and satellite observations, and available from NCI's National Environmental Research Data Interoperability Platform (NERDIP) [1]. Because of the NERDIP requirement for interoperability with gridded datasets, the data models required to store these data may not conform to the LAS/LAZ format - the widely accepted community standard for point data storage and transfer. The goal for NCI is making point data discoverable, accessible and useable in ways which allow seamless integration with earth observation datasets and model outputs - in turn assisting researchers and decision-makers in the often-convoluted process of handling and analyzing massive point datasets. With a use-case of providing a web data service and supporting a derived product workflow, NCI has implemented and tested a web-based point cloud service using the Open Geospatial Consortium (OGC) Web Processing Service [2] as a transaction handler between a web-based client and server-side computing tools based on a native Linux operating system. Using this model, the underlying toolset for driving a data service is flexible and can take advantage of NCI's highly scalable research cloud. Present work focusses on the Point Data Abstraction Library (PDAL) [3] as a logical choice for efficiently handling LAS/LAZ based point workflows, and native HDF5 libraries for handling point data kept in HDF5-based structures (eg NetCDF4, SPDlib [4]). Points stored in database tables (eg postgres-pointcloud [5]) will be considered as testing continues. Visualising and exploring massive point datasets in a web browser alongside multiple datasets has been demonstrated by the entwine-3D tiles project [6]. This is a powerful interface which enables users to investigate and select appropriate data, and is also being investigated as a potential front-end to a WPS-based point data service. In this work we show preliminary results for a WPS-based point data access system, in preparation for demonstration at FOSS4G 2017, Boston (http://2017.foss4g.org/) [1] http://nci.org.au/data-collections/nerdip/ [2] http://www.opengeospatial.org/standards/wps [3] http://www.pdal.io [4] http://www.spdlib.org/doku.php [5] https://github.com/pgpointcloud/pointcloud [6] http://cesium.entwine.io

Generating Accurate 3d Models of Architectural Heritage Structures Using Low-Cost Camera and Open Source Algorithms

NASA Astrophysics Data System (ADS)

Zacharek, M.; Delis, P.; Kedzierski, M.; Fryskowska, A.

2017-05-01

These studies have been conductedusing non-metric digital camera and dense image matching algorithms, as non-contact methods of creating monuments documentation.In order toprocess the imagery, few open-source software and algorithms of generating adense point cloud from images have been executed. In the research, the OSM Bundler, VisualSFM software, and web application ARC3D were used. Images obtained for each of the investigated objects were processed using those applications, and then dense point clouds and textured 3D models were created. As a result of post-processing, obtained models were filtered and scaled.The research showedthat even using the open-source software it is possible toobtain accurate 3D models of structures (with an accuracy of a few centimeters), but for the purpose of documentation and conservation of cultural and historical heritage, such accuracy can be insufficient.

Using mid-range laser scanners to digitize cultural-heritage sites.

PubMed

Spring, Adam P; Peters, Caradoc; Minns, Tom

2010-01-01

Here, we explore new, more accessible ways of modeling 3D data sets that both professionals and amateurs can employ in areas such as architecture, forensics, geotechnics, cultural heritage, and even hobbyist modeling. To support our arguments, we present images from a recent case study in digital preservation of cultural heritage using a mid-range laser scanner. Our appreciation of the increasing variety of methods for capturing 3D spatial data inspired our research. Available methods include photogrammetry, airborne lidar, sonar, total stations (a combined electronic and optical survey instrument), and midand close-range scanning.1 They all can produce point clouds of varying density. In our case study, the point cloud produced by a mid-range scanner demonstrates how open source software can make modeling and disseminating data easier. Normally, researchers would model this data using expensive specialized software, and the data wouldn't extend beyond the laser-scanning community.

Modeling the Electric Potential and Surface Charge Density Near Charged Thunderclouds

NASA Astrophysics Data System (ADS)

Neel, Matthew Stephen

2018-03-01

Thundercloud charge separation, or the process by which the bottom portion of a cloud gathers charge and the top portion of the cloud gathers the opposite charge, is still not thoroughly understood. Whatever the mechanism, though, a charge separation definitely exists and can lead to electrostatic discharge via cloud-to-cloud lightning and cloud-to-ground lightning. We wish to examine the latter form, in which upward leaders from Earth connect with downward leaders from the cloud to form a plasma channel and produce lightning. Much of the literature indicates that the lower part of a thundercloud becomes negatively charged while the upper part becomes positively charged via convective charging, although the opposite polarity can certainly exist along with various, complex intra-cloud currents. It is estimated that >90% of cloud-to-ground lightning is "negative lightning," or the flow of charges from the bottom of the cloud, while the remaining <10% of lightning strikes is "positive lightning," or the flow of charges from the top of the cloud. We wish to understand the electric potential surrounding charged thunderclouds as well as the resulting charge density on the surface of Earth below them. In this paper we construct a simple and adaptable model that captures the very basic features of the cloud/ground system and that exhibits conditions favorable for both forms of lightning. In this way, we provide a practical application of electrostatic dipole physics as well as the method of images that can serve as a starting point for further modeling and analysis by students.

Measurement and reconstruction of the leaflet geometry for a pericardial artificial heart valve.

PubMed

Jiang, Hongjun; Campbell, Gord; Xi, Fengfeng

2005-03-01

This paper describes the measurement and reconstruction of the leaflet geometry for a pericardial heart valve. Tasks involved include mapping the leaflet geometries by laser digitizing and reconstructing the 3D freeform leaflet surface based on a laser scanned profile. The challenge is to design a prosthetic valve that maximizes the benefits offered to the recipient as compared to the normally operating naturally-occurring valve. This research was prompted by the fact that artificial heart valve bioprostheses do not provide long life durability comparable to the natural heart valve, together with the anticipated benefits associated with defining the valve geometries, especially the leaflet geometries for the bioprosthetic and human valves, in order to create a replicate valve fabricated from synthetic materials. Our method applies the concept of reverse engineering in order to reconstruct the freeform surface geometry. A Brown & Shape coordinate measuring machine (CMM) equipped with a HyMARC laser-digitizing system was used to measure the leaflet profiles of a Baxter Carpentier-Edwards pericardial heart valve. The computer software, Polyworks was used to pre-process the raw data obtained from the scanning, which included merging images, eliminating duplicate points, and adding interpolated points. Three methods, creating a mesh model from cloud points, creating a freeform surface from cloud points, and generating a freeform surface by B-splines are presented in this paper to reconstruct the freeform leaflet surface. The mesh model created using Polyworks can be used for rapid prototyping and visualization. To fit a freeform surface to cloud points is straightforward but the rendering of a smooth surface is usually unpredictable. A surface fitted by a group of B-splines fitted to cloud points was found to be much smoother. This method offers the possibility of manually adjusting the surface curvature, locally. However, the process is complex and requires additional manipulation. Finally, this paper presents a reverse engineered design for the pericardial heart valve which contains three identical leaflets with reconstructed geometry.

Observational constraints on mixed-phase clouds imply higher climate sensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Ivy; Storelvmo, Trude; Zelinka, Mark D.

Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO 2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here, in this paper, we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. Finally, wemore » point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback.« less

Observational constraints on mixed-phase clouds imply higher climate sensitivity

DOE PAGES

Tan, Ivy; Storelvmo, Trude; Zelinka, Mark D.

2016-04-08

Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO 2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here, in this paper, we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. Finally, wemore » point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback.« less

Observational constraints on mixed-phase clouds imply higher climate sensitivity.

PubMed

Tan, Ivy; Storelvmo, Trude; Zelinka, Mark D

2016-04-08

Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. We point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback. Copyright © 2016, American Association for the Advancement of Science.

An interfacial mechanism for cloud droplet formation on organic aerosols

DOE PAGES

Ruehl, C. R.; Davies, J. F.; Wilson, K. R.

2016-03-25

Accurate predictions of aerosol/cloud interactions require simple, physically accurate parameterizations of the cloud condensation nuclei (CCN) activity of aerosols. Current models assume that organic aerosol species contribute to CCN activity by lowering water activity. We measured droplet diameters at the point of CCN activation for particles composed of dicarboxylic acids or secondary organic aerosol and ammonium sulfate. Droplet activation diameters were 40 to 60% larger than predicted if the organic was assumed to be dissolved within the bulk droplet, suggesting that a new mechanism is needed to explain cloud droplet formation. A compressed film model explains how surface tension depressionmore » by interfacial organic molecules can alter the relationship between water vapor supersaturation and droplet size (i.e., the Köhler curve), leading to the larger diameters observed at activation.« less

An interfacial mechanism for cloud droplet formation on organic aerosols

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruehl, C. R.; Davies, J. F.; Wilson, K. R.

Accurate predictions of aerosol/cloud interactions require simple, physically accurate parameterizations of the cloud condensation nuclei (CCN) activity of aerosols. Current models assume that organic aerosol species contribute to CCN activity by lowering water activity. We measured droplet diameters at the point of CCN activation for particles composed of dicarboxylic acids or secondary organic aerosol and ammonium sulfate. Droplet activation diameters were 40 to 60% larger than predicted if the organic was assumed to be dissolved within the bulk droplet, suggesting that a new mechanism is needed to explain cloud droplet formation. A compressed film model explains how surface tension depressionmore » by interfacial organic molecules can alter the relationship between water vapor supersaturation and droplet size (i.e., the Köhler curve), leading to the larger diameters observed at activation.« less

An interfacial mechanism for cloud droplet formation on organic aerosols.

PubMed

Ruehl, Christopher R; Davies, James F; Wilson, Kevin R

2016-03-25

Accurate predictions of aerosol/cloud interactions require simple, physically accurate parameterizations of the cloud condensation nuclei (CCN) activity of aerosols. Current models assume that organic aerosol species contribute to CCN activity by lowering water activity. We measured droplet diameters at the point of CCN activation for particles composed of dicarboxylic acids or secondary organic aerosol and ammonium sulfate. Droplet activation diameters were 40 to 60% larger than predicted if the organic was assumed to be dissolved within the bulk droplet, suggesting that a new mechanism is needed to explain cloud droplet formation. A compressed film model explains how surface tension depression by interfacial organic molecules can alter the relationship between water vapor supersaturation and droplet size (i.e., the Köhler curve), leading to the larger diameters observed at activation. Copyright © 2016, American Association for the Advancement of Science.

Features of Point Clouds Synthesized from Multi-View ALOS/PRISM Data and Comparisons with LiDAR Data in Forested Areas

NASA Technical Reports Server (NTRS)

Ni, Wenjian; Ranson, Kenneth Jon; Zhang, Zhiyu; Sun, Guoqing

2014-01-01

LiDAR waveform data from airborne LiDAR scanners (ALS) e.g. the Land Vegetation and Ice Sensor (LVIS) havebeen successfully used for estimation of forest height and biomass at local scales and have become the preferredremote sensing dataset. However, regional and global applications are limited by the cost of the airborne LiDARdata acquisition and there are no available spaceborne LiDAR systems. Some researchers have demonstrated thepotential for mapping forest height using aerial or spaceborne stereo imagery with very high spatial resolutions.For stereo imageswith global coverage but coarse resolution newanalysis methods need to be used. Unlike mostresearch based on digital surface models, this study concentrated on analyzing the features of point cloud datagenerated from stereo imagery. The synthesizing of point cloud data from multi-view stereo imagery increasedthe point density of the data. The point cloud data over forested areas were analyzed and compared to small footprintLiDAR data and large-footprint LiDAR waveform data. The results showed that the synthesized point clouddata from ALOSPRISM triplets produce vertical distributions similar to LiDAR data and detected the verticalstructure of sparse and non-closed forests at 30mresolution. For dense forest canopies, the canopy could be capturedbut the ground surface could not be seen, so surface elevations from other sourceswould be needed to calculatethe height of the canopy. A canopy height map with 30 m pixels was produced by subtracting nationalelevation dataset (NED) fromthe averaged elevation of synthesized point clouds,which exhibited spatial featuresof roads, forest edges and patches. The linear regression showed that the canopy height map had a good correlationwith RH50 of LVIS data with a slope of 1.04 and R2 of 0.74 indicating that the canopy height derived fromPRISM triplets can be used to estimate forest biomass at 30 m resolution.

Cloud Type Classification (cldtype) Value-Added Product

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flynn, Donna; Shi, Yan; Lim, K-S

The Cloud Type (cldtype) value-added product (VAP) provides an automated cloud type classification based on macrophysical quantities derived from vertically pointing lidar and radar. Up to 10 layers of clouds are classified into seven cloud types based on predetermined and site-specific thresholds of cloud top, base and thickness. Examples of thresholds for selected U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility sites are provided in Tables 1 and 2. Inputs for the cldtype VAP include lidar and radar cloud boundaries obtained from the Active Remotely Sensed Cloud Location (ARSCL) and Surface Meteorological Systems (MET) data. Rainmore » rates from MET are used to determine when radar signal attenuation precludes accurate cloud detection. Temporal resolution and vertical resolution for cldtype are 1 minute and 30 m respectively and match the resolution of ARSCL. The cldtype classification is an initial step for further categorization of clouds. It was developed for use by the Shallow Cumulus VAP to identify potential periods of interest to the LASSO model and is intended to find clouds of interest for a variety of users.« less

An ensemble constrained variational analysis of atmospheric forcing data and its application to evaluate clouds in CAM5: Ensemble 3DCVA and Its Application

DOE PAGES

Tang, Shuaiqi; Zhang, Minghua; Xie, Shaocheng

2016-01-05

Large-scale atmospheric forcing data can greatly impact the simulations of atmospheric process models including Large Eddy Simulations (LES), Cloud Resolving Models (CRMs) and Single-Column Models (SCMs), and impact the development of physical parameterizations in global climate models. This study describes the development of an ensemble variationally constrained objective analysis of atmospheric large-scale forcing data and its application to evaluate the cloud biases in the Community Atmospheric Model (CAM5). Sensitivities of the variational objective analysis to background data, error covariance matrix and constraint variables are described and used to quantify the uncertainties in the large-scale forcing data. Application of the ensemblemore » forcing in the CAM5 SCM during March 2000 intensive operational period (IOP) at the Southern Great Plains (SGP) of the Atmospheric Radiation Measurement (ARM) program shows systematic biases in the model simulations that cannot be explained by the uncertainty of large-scale forcing data, which points to the deficiencies of physical parameterizations. The SCM is shown to overestimate high clouds and underestimate low clouds. These biases are found to also exist in the global simulation of CAM5 when it is compared with satellite data.« less

An ensemble constrained variational analysis of atmospheric forcing data and its application to evaluate clouds in CAM5: Ensemble 3DCVA and Its Application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Shuaiqi; Zhang, Minghua; Xie, Shaocheng

Large-scale atmospheric forcing data can greatly impact the simulations of atmospheric process models including Large Eddy Simulations (LES), Cloud Resolving Models (CRMs) and Single-Column Models (SCMs), and impact the development of physical parameterizations in global climate models. This study describes the development of an ensemble variationally constrained objective analysis of atmospheric large-scale forcing data and its application to evaluate the cloud biases in the Community Atmospheric Model (CAM5). Sensitivities of the variational objective analysis to background data, error covariance matrix and constraint variables are described and used to quantify the uncertainties in the large-scale forcing data. Application of the ensemblemore » forcing in the CAM5 SCM during March 2000 intensive operational period (IOP) at the Southern Great Plains (SGP) of the Atmospheric Radiation Measurement (ARM) program shows systematic biases in the model simulations that cannot be explained by the uncertainty of large-scale forcing data, which points to the deficiencies of physical parameterizations. The SCM is shown to overestimate high clouds and underestimate low clouds. These biases are found to also exist in the global simulation of CAM5 when it is compared with satellite data.« less

Impact of Surface Active Ionic Liquids on the Cloud Points of Nonionic Surfactants and the Formation of Aqueous Micellar Two-Phase Systems.

PubMed

Vicente, Filipa A; Cardoso, Inês S; Sintra, Tânia E; Lemus, Jesus; Marques, Eduardo F; Ventura, Sónia P M; Coutinho, João A P

2017-09-21

Aqueous micellar two-phase systems (AMTPS) hold a large potential for cloud point extraction of biomolecules but are yet poorly studied and characterized, with few phase diagrams reported for these systems, hence limiting their use in extraction processes. This work reports a systematic investigation of the effect of different surface-active ionic liquids (SAILs)-covering a wide range of molecular properties-upon the clouding behavior of three nonionic Tergitol surfactants. Two different effects of the SAILs on the cloud points and mixed micelle size have been observed: ILs with a more hydrophilic character and lower critical packing parameter (CPP < 1 / 2 ) lead to the formation of smaller micelles and concomitantly increase the cloud points; in contrast, ILs with a more hydrophobic character and higher CPP (CPP ≥ 1) induce significant micellar growth and a decrease in the cloud points. The latter effect is particularly interesting and unusual for it was accepted that cloud point reduction is only induced by inorganic salts. The effects of nonionic surfactant concentration, SAIL concentration, pH, and micelle ζ potential are also studied and rationalized.

Modeling right-lateral offset of a Late Pleistocene terrace riser along the Polaris fault using ground based LiDAR imagery

NASA Astrophysics Data System (ADS)

Howle, J. F.; Bawden, G. W.; Hunter, L. E.; Rose, R. S.

2009-12-01

High resolution (centimeter level) three-dimensional point-cloud imagery of offset glacial outwash deposits were collected by using ground based tripod LiDAR (T-LiDAR) to characterize the cumulative fault slip across the recently identified Polaris fault (Hunter et al., 2009) near Truckee, California. The type-section site for the Polaris fault is located 6.5 km east of Truckee where progressive right-lateral displacement of middle to late Pleistocene deposits is evident. Glacial outwash deposits, aggraded during the Tioga glaciation, form a flat lying ‘fill’ terrace on both the north and south sides of the modern Truckee River. During the Tioga deglaciation melt water incised into the terrace producing fluvial scarps or terrace risers (Birkeland, 1964). Subsequently, the terrace risers on both banks have been right-laterally offset by the Polaris fault. By using T-LiDAR on an elevated tripod (4.25 m high), we collected 3D high-resolution (thousands of points per square meter; ± 4 mm) point-cloud imagery of the offset terrace risers. Vegetation was removed from the data using commercial software, and large protruding boulders were manually deleted to generate a bare-earth point-cloud dataset with an average data density of over 240 points per square meter. From the bare-earth point cloud we mathematically reconstructed a pristine terrace/scarp morphology on both sides of the fault, defined coupled sets of piercing points, and extracted a corresponding displacement vector. First, the Polaris fault was approximated as a vertical plane that bisects the offset terrace risers, as well as bisecting linear swales and tectonic depressions in the outwash terrace. Then, piercing points to the vertical fault plane were constructed from the geometry of the geomorphic elements on either side of the fault. On each side of the fault, the best-fit modeled outwash plane is projected laterally and the best-fit modeled terrace riser projected upward to a virtual intersection in space, creating a vector. These constructed vectors were projected to intersection with the fault plane, defining statistically significant piercing points. The distance between the coupled set of piercing points, within the plane of the fault, is the cumulative displacement vector. To assess the variability of the modeled geomorphic surfaces, including surface roughness and nonlinearity, we generated a suite of displacement models by systematically incorporating larger areas of the model domain symmetrically about the fault. Preliminary results of 10 models yield an average cumulative displacement of 5.6 m (1 Std Dev = 0.31 m). As previously described, Tioga deglaciation melt water incised into the outwash terrace leaving terrace risers that were subsequently offset by the Polaris fault. Therefore, the age of the Tioga outwash terrace represents a maximum limiting age of the tectonic displacement. Using regional age constraints of 15 to 13 kya for the Tioga outwash terrace (Benson et al., 1990; Clark and Gillespie, 1997; James et al., 2002) and the above model results, we estimate a preliminary minimum fault slip rate of 0.40 ± 0.05 mm/yr for the Polaris type-section site.

Topographic lidar survey of the Chandeleur Islands, Louisiana, February 6, 2012

USGS Publications Warehouse

Guy, Kristy K.; Plant, Nathaniel G.; Bonisteel-Cormier, Jamie M.

2014-01-01

This Data Series Report contains lidar elevation data collected February 6, 2012, for Chandeleur Islands, Louisiana. Point cloud data in lidar data exchange format (LAS) and bare earth digital elevation models (DEMs) in ERDAS Imagine raster format (IMG) are available as downloadable files. The point cloud data—data points described in three dimensions—were processed to extract bare earth data; therefore, the point cloud data are organized into the following classes: 1– and 17–unclassified, 2–ground, 9–water, and 10–breakline proximity. Digital Aerial Solutions, LLC, (DAS) was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were acquired at a horizontal spacing (or nominal pulse spacing) of 0.5 meters (m) or less. The USGS conducted two ground surveys in small areas on the Chandeleur Islands on February 5, 2012. DAS calculated a root mean square error (RMSEz) of 0.034 m by comparing the USGS ground survey point data to triangulated irregular network (TIN) models built from the lidar elevation data. This lidar survey was conducted to document the topography and topographic change of the Chandeleur Islands. The survey supports detailed studies of Louisiana, Mississippi and Alabama barrier islands that resolve annual and episodic changes in beaches, berms and dunes associated with processes driven by storms, sea-level rise, and even human restoration activities. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

Preliminary results of fluid dynamic model calculation of convective motion induced by solar heating at the Venus cloud top level.

NASA Astrophysics Data System (ADS)

Lee, Yeon Joo; Imamura, Takeshi; Maejima, Yasumitsu; Sugiyama, Ko-ichiro

The thick cloud layer of Venus reflects solar radiation effectively, resulting in a Bond albedo of 76% (Moroz et al., 1985). Most of the incoming solar flux is absorbed in the upper cloud layer at 60-70 km altitude. An unknown UV absorber is a major sink of the solar energy at the cloud top level. It produces about 40-60% of the total solar heating near the cloud tops, depending on its vertical structure (Crisp et al., 1986; Lee et al., in preparation). UV images of Venus show a clear difference in morphology between laminar flow shaped clouds on the morning side and convective-like cells on the afternoon side of the planet in the equatorial region (Titov et al., 2012). This difference is probably related to strong solar heating at the cloud tops at the sub-solar point, rather than the influence from deeper level convection in the low and middle cloud layers (Imamura et al., 2014). Also, small difference in cloud top structures may trigger horizontal convection at this altitude, because various cloud top structures can significantly alter the solar heating and thermal cooling rates at the cloud tops (Lee et al., in preparation). Performing radiative forcing calculations for various cloud top structures using a radiative transfer model (SHDOM), we investigate the effect of solar heating at the cloud tops on atmospheric dynamics. We use CReSS (Cloud Resolving Storm Simulator), and consider the altitude range from 35 km to 90 km, covering a full cloud deck.

Point Cloud Management Through the Realization of the Intelligent Cloud Viewer Software

NASA Astrophysics Data System (ADS)

Costantino, D.; Angelini, M. G.; Settembrini, F.

2017-05-01

The paper presents a software dedicated to the elaboration of point clouds, called Intelligent Cloud Viewer (ICV), made in-house by AESEI software (Spin-Off of Politecnico di Bari), allowing to view point cloud of several tens of millions of points, also on of "no" very high performance systems. The elaborations are carried out on the whole point cloud and managed by means of the display only part of it in order to speed up rendering. It is designed for 64-bit Windows and is fully written in C ++ and integrates different specialized modules for computer graphics (Open Inventor by SGI, Silicon Graphics Inc), maths (BLAS, EIGEN), computational geometry (CGAL, Computational Geometry Algorithms Library), registration and advanced algorithms for point clouds (PCL, Point Cloud Library), advanced data structures (BOOST, Basic Object Oriented Supporting Tools), etc. ICV incorporates a number of features such as, for example, cropping, transformation and georeferencing, matching, registration, decimation, sections, distances calculation between clouds, etc. It has been tested on photographic and TLS (Terrestrial Laser Scanner) data, obtaining satisfactory results. The potentialities of the software have been tested by carrying out the photogrammetric survey of the Castel del Monte which was already available in previous laser scanner survey made from the ground by the same authors. For the aerophotogrammetric survey has been adopted a flight height of approximately 1000ft AGL (Above Ground Level) and, overall, have been acquired over 800 photos in just over 15 minutes, with a covering not less than 80%, the planned speed of about 90 knots.

Understanding the Effect of Aerosol Properties on Cloud Droplet Formation during TCAP Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cziczo, Daniel

2016-05-01

The formation of clouds is an essential element in understanding the Earth’s radiative budget. Liquid water clouds form when the relative humidity exceeds saturation and condensedphase water nucleates on atmospheric particulate matter. The effect of aerosol properties such as size, morphology, and composition on cloud droplet formation has been studied theoretically as well as in the laboratory and field. Almost without exception these studies have been limited to parallel measurements of aerosol properties and cloud formation or collection of material after the cloud has formed, at which point nucleation information has been lost. Studies of this sort are adequate whenmore » a large fraction of the aerosol activates, but correlations and resulting model parameterizations are much more uncertain at lower supersaturations and activated fractions.« less

Evaluation of terrestrial photogrammetric point clouds derived from thermal imagery

NASA Astrophysics Data System (ADS)

Metcalf, Jeremy P.; Olsen, Richard C.

2016-05-01

Computer vision and photogrammetric techniques have been widely applied to digital imagery producing high density 3D point clouds. Using thermal imagery as input, the same techniques can be applied to infrared data to produce point clouds in 3D space, providing surface temperature information. The work presented here is an evaluation of the accuracy of 3D reconstruction of point clouds produced using thermal imagery. An urban scene was imaged over an area at the Naval Postgraduate School, Monterey, CA, viewing from above as with an airborne system. Terrestrial thermal and RGB imagery were collected from a rooftop overlooking the site using a FLIR SC8200 MWIR camera and a Canon T1i DSLR. In order to spatially align each dataset, ground control points were placed throughout the study area using Trimble R10 GNSS receivers operating in RTK mode. Each image dataset is processed to produce a dense point cloud for 3D evaluation.

An ARM data-oriented diagnostics package to evaluate the climate model simulation

NASA Astrophysics Data System (ADS)

Zhang, C.; Xie, S.

2016-12-01

A set of diagnostics that utilize long-term high frequency measurements from the DOE Atmospheric Radiation Measurement (ARM) program is developed for evaluating the regional simulation of clouds, radiation and precipitation in climate models. The diagnostics results are computed and visualized automatically in a python-based package that aims to serve as an easy entry point for evaluating climate simulations using the ARM data, as well as the CMIP5 multi-model simulations. Basic performance metrics are computed to measure the accuracy of mean state and variability of simulated regional climate. The evaluated physical quantities include vertical profiles of clouds, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, radiative fluxes, aerosol and cloud microphysical properties. Process-oriented diagnostics focusing on individual cloud and precipitation-related phenomena are developed for the evaluation and development of specific model physical parameterizations. Application of the ARM diagnostics package will be presented in the AGU session. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, IM release number is: LLNL-ABS-698645.

Geovisualisation of relief in a virtual reality system on the basis of low-level aerial imagery

NASA Astrophysics Data System (ADS)

Halik, Łukasz; Smaczyński, Maciej

2017-12-01

The aim of the following paper was to present the geomatic process of transforming low-level aerial imagery obtained with unmanned aerial vehicles (UAV) into a digital terrain model (DTM) and implementing the model into a virtual reality system (VR). The object of the study was a natural aggretage heap of an irregular shape and denivelations up to 11 m. Based on the obtained photos, three point clouds (varying in the level of detail) were generated for the 20,000-m2-area. For further analyses, the researchers selected the point cloud with the best ratio of accuracy to output file size. This choice was made based on seven control points of the heap surveyed in the field and the corresponding points in the generated 3D model. The obtained several-centimetre differences between the control points in the field and the ones from the model might testify to the usefulness of the described algorithm for creating large-scale DTMs for engineering purposes. Finally, the chosen model was implemented into the VR system, which enables the most lifelike exploration of 3D terrain plasticity in real time, thanks to the first person view mode (FPV). In this mode, the user observes an object with the aid of a Head- mounted display (HMD), experiencing the geovisualisation from the inside, and virtually analysing the terrain as a direct animator of the observations.

Carbon Dioxide Clouds at High Altitude in the Tropics and in an Early Dense Martian Atmosphere

NASA Technical Reports Server (NTRS)

Colaprete, Anthony; Toon, Owen B.

2001-01-01

We use a time dependent, microphysical cloud model to study the formation of carbon dioxide clouds in the Martian atmosphere. Laboratory studies by Glandor et al. show that high critical supersaturations are required for cloud particle nucleation and that surface kinetic growth is not limited. These conditions, which are similar to those for cirrus clouds on Earth, lead to the formation of carbon dioxide ice particles with radii greater than 500 micrometers and concentrations of less than 0.1 cm(exp -3) for typical atmospheric conditions. Within the current Martian atmosphere, CO2 cloud formation is possible at the poles during winter and at high altitudes in the tropics during periods of increased atmospheric dust loading. In both cases, temperature perturbations of several degrees below the CO2 saturation temperature are required to nucleate new cloud particles suggesting that dynamical processes are the most common initiators of carbon dioxide clouds rather than diabatic cooling. The microphysical cloud model, coupled to a two-stream radiative transfer model, is used to reexamine the impact of CO2 clouds on the surface temperature within a dense CO2 atmosphere. The formation of carbon dioxide clouds leads to a warmer surface than what would be expected for clear sky conditions. The amount of warming is sensitive to the presence of dust and water vapor in the atmosphere, both of which act to dampen cloud effects. The radiative warming associated with cloud formation, as well as latent heating, work to dissipate the clouds when present. Thus, clouds never last for periods much longer than several days, limiting their overall effectiveness for warming the surface. The time average cloud optical depth is approximately unity leading to a 5-10 K warming, depending on the surface pressure. However, the surface temperature does not rise about the freezing point of liquid water even for pressures as high as 5 bars, at a solar luminosity of 75% the current value.

Automated estimation of leaf distribution for individual trees based on TLS point clouds

NASA Astrophysics Data System (ADS)

Koma, Zsófia; Rutzinger, Martin; Bremer, Magnus

2017-04-01

Light Detection and Ranging (LiDAR) especially the ground based LiDAR (Terrestrial Laser Scanning - TLS) is an operational used and widely available measurement tool supporting forest inventory updating and research in forest ecology. High resolution point clouds from TLS already represent single leaves which can be used for a more precise estimation of Leaf Area Index (LAI) and for higher accurate biomass estimation. However, currently the methodology for extracting single leafs from the unclassified point clouds for individual trees is still missing. The aim of this study is to present a novel segmentation approach in order to extract single leaves and derive features related to leaf morphology (such as area, slope, length and width) of each single leaf from TLS point cloud data. For the study two exemplary single trees were scanned in leaf-on condition on the university campus of Innsbruck during calm wind conditions. A northern red oak (Quercus rubra) was scanned by a discrete return recording Optech ILRIS-3D TLS scanner and a tulip tree (Liliodendron tulpifera) with Riegl VZ-6000 scanner. During the scanning campaign a reference dataset was measured parallel to scanning. In this case 230 leaves were randomly collected around the lower branches of the tree and photos were taken. The developed workflow steps were the following: in the first step normal vectors and eigenvalues were calculated based on the user specified neighborhood. Then using the direction of the largest eigenvalue outliers i.e. ghost points were removed. After that region growing segmentation based on the curvature and angles between normal vectors was applied on the filtered point cloud. On each segment a RANSAC plane fitting algorithm was applied in order to extract the segment based normal vectors. Using the related features of the calculated segments the stem and branches were labeled as non-leaf and other segments were classified as leaf. The validation of the different segmentation parameters was evaluated as the following: i) the sum area of the collected leaves and the point cloud, ii) the segmented leaf length-width ratio iii) the distribution of the leaf area for the segmented and the reference-ones were compared and the ideal parameter-set was found. The results show that the leaves can be captured with the developed workflow and the slope can be determined robustly for the segmented leaves. However, area, length and width values are systematically depending on the angle and the distance from the scanner. For correction of the systematic underestimation, more systematic measurement or LiDAR simulation is required for further detailed analysis. The results of leaf segmentation algorithm show high potential in generating more precise tree models with correctly located leaves in order to extract more precise input model for biological modeling of LAI or atmospheric corrections studies. The presented workflow also can be used in monitoring the change of angle of the leaves due to sun irradiation, water balance, and day-night rhythm.

a Framework for Voxel-Based Global Scale Modeling of Urban Environments

NASA Astrophysics Data System (ADS)

Gehrung, Joachim; Hebel, Marcus; Arens, Michael; Stilla, Uwe

2016-10-01

The generation of 3D city models is a very active field of research. Modeling environments as point clouds may be fast, but has disadvantages. These are easily solvable by using volumetric representations, especially when considering selective data acquisition, change detection and fast changing environments. Therefore, this paper proposes a framework for the volumetric modeling and visualization of large scale urban environments. Beside an architecture and the right mix of algorithms for the task, two compression strategies for volumetric models as well as a data quality based approach for the import of range measurements are proposed. The capabilities of the framework are shown on a mobile laser scanning dataset of the Technical University of Munich. Furthermore the loss of the compression techniques is evaluated and their memory consumption is compared to that of raw point clouds. The presented results show that generation, storage and real-time rendering of even large urban models are feasible, even with off-the-shelf hardware.

A comparison of multi-view 3D reconstruction of a rock wall using several cameras and a laser scanner

NASA Astrophysics Data System (ADS)

Thoeni, K.; Giacomini, A.; Murtagh, R.; Kniest, E.

2014-06-01

This work presents a comparative study between multi-view 3D reconstruction using various digital cameras and a terrestrial laser scanner (TLS). Five different digital cameras were used in order to estimate the limits related to the camera type and to establish the minimum camera requirements to obtain comparable results to the ones of the TLS. The cameras used for this study range from commercial grade to professional grade and included a GoPro Hero 1080 (5 Mp), iPhone 4S (8 Mp), Panasonic Lumix LX5 (9.5 Mp), Panasonic Lumix ZS20 (14.1 Mp) and Canon EOS 7D (18 Mp). The TLS used for this work was a FARO Focus 3D laser scanner with a range accuracy of ±2 mm. The study area is a small rock wall of about 6 m height and 20 m length. The wall is partly smooth with some evident geological features, such as non-persistent joints and sharp edges. Eight control points were placed on the wall and their coordinates were measured by using a total station. These coordinates were then used to georeference all models. A similar number of images was acquired from a distance of between approximately 5 to 10 m, depending on field of view of each camera. The commercial software package PhotoScan was used to process the images, georeference and scale the models, and to generate the dense point clouds. Finally, the open-source package CloudCompare was used to assess the accuracy of the multi-view results. Each point cloud obtained from a specific camera was compared to the point cloud obtained with the TLS. The latter is taken as ground truth. The result is a coloured point cloud for each camera showing the deviation in relation to the TLS data. The main goal of this study is to quantify the quality of the multi-view 3D reconstruction results obtained with various cameras as objectively as possible and to evaluate its applicability to geotechnical problems.

Iterative Most-Likely Point Registration (IMLP): A Robust Algorithm for Computing Optimal Shape Alignment

PubMed Central

Billings, Seth D.; Boctor, Emad M.; Taylor, Russell H.

2015-01-01

We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP’s probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes. PMID:25748700

Acquiring Semantically Meaningful Models for Robotic Localization, Mapping and Target Recognition

DTIC Science & Technology

2014-12-21

information, including suggesstions for reducing this burden, to Washington Headquarters Services , Directorate for Information Operations and Reports, 1215...Representations • Point features tracking • Recovery of relative motion, visual odometry • Loop closure • Environment models, sparse clouds of points...that co- occur with the object of interest Chair-Background Table-Background Object Level Segmentation Jaccard Index Silber .[5] 15.12 RenFox[4

Using Information From Prior Satellite Scans to Improve Cloud Detection Near the Day-Night Terminator

NASA Technical Reports Server (NTRS)

Yost, Christopher R.; Minnis, Patrick; Trepte, Qing Z.; Palikonda, Rabindra; Ayers, Jeffrey K.; Spangenberg, Doulas A.

2012-01-01

With geostationary satellite data it is possible to have a continuous record of diurnal cycles of cloud properties for a large portion of the globe. Daytime cloud property retrieval algorithms are typically superior to nighttime algorithms because daytime methods utilize measurements of reflected solar radiation. However, reflected solar radiation is difficult to accurately model for high solar zenith angles where the amount of incident radiation is small. Clear and cloudy scenes can exhibit very small differences in reflected radiation and threshold-based cloud detection methods have more difficulty setting the proper thresholds for accurate cloud detection. Because top-of-atmosphere radiances are typically more accurately modeled outside the terminator region, information from previous scans can help guide cloud detection near the terminator. This paper presents an algorithm that uses cloud fraction and clear and cloudy infrared brightness temperatures from previous satellite scan times to improve the performance of a threshold-based cloud mask near the terminator. Comparisons of daytime, nighttime, and terminator cloud fraction derived from Geostationary Operational Environmental Satellite (GOES) radiance measurements show that the algorithm greatly reduces the number of false cloud detections and smoothes the transition from the daytime to the nighttime clod detection algorithm. Comparisons with the Geoscience Laser Altimeter System (GLAS) data show that using this algorithm decreases the number of false detections by approximately 20 percentage points.

Partitioning CloudSat Ice Water Content for Comparison with Upper-Tropospheric Ice in Global Atmospheric Models

NASA Astrophysics Data System (ADS)

Chen, W. A.; Woods, C. P.; Li, J. F.; Waliser, D. E.; Chern, J.; Tao, W.; Jiang, J. H.; Tompkins, A. M.

2010-12-01

CloudSat provides important estimates of vertically resolved ice water content (IWC) on a global scale based on radar reflectivity. These estimates of IWC have proven beneficial in evaluating the representations of ice clouds in global models. An issue when performing model-data comparisons of IWC particularly germane to this investigation, is the question of which component(s) of the frozen water mass are represented by retrieval estimates and how they relate to what is represented in models. The present study developed and applied a new technique to partition CloudSat total IWC into small and large ice hydrometeors, based on the CloudSat-retrieved ice particle size distribution (PSD) parameters. The new method allows one to make relevant model-data comparisons and provides new insights into the model’s representation of atmospheric IWC. The partitioned CloudSat IWC suggests that the small ice particles contribute to 20-30% of the total IWC in the upper troposphere when a threshold size of 100 μm is used. Sensitivity measures with respect to the threshold size, the PSD parameters, and the retrieval algorithms are presented. The new dataset is compared to model estimates, pointing to areas for model improvement. Cloud ice analyses from the European Centre for Medium-Range Weather Forecasts model agree well with the small IWC from CloudSat. The finite-volume multi-scale modeling framework model underestimates total IWC at 147 and 215 hPa, while overestimating the fractional contribution from the small ice species. These results are discussed in terms of their applications to, and implications for, the evaluation of global atmospheric models, providing constraints on the representations of cloud feedback and precipitation in global models, which in turn can help reduce uncertainties associated with climate change projections. Figure 1. A sample lognormal ice number distribution (red curve), and the corresponding mass distribution (black curve). The dotted line represents the cutoff size for IWC partitioning (Dc = 100 µm as an example). The partial integrals of the mass distribution for particles smaller and larger than Dc correspond to IWC<100 (green area) and IWC>100 (blue area), respectively.

Geometric identification and damage detection of structural elements by terrestrial laser scanner

NASA Astrophysics Data System (ADS)

Hou, Tsung-Chin; Liu, Yu-Wei; Su, Yu-Min

2016-04-01

In recent years, three-dimensional (3D) terrestrial laser scanning technologies with higher precision and higher capability are developing rapidly. The growing maturity of laser scanning has gradually approached the required precision as those have been provided by traditional structural monitoring technologies. Together with widely available fast computation for massive point cloud data processing, 3D laser scanning can serve as an efficient structural monitoring alternative for civil engineering communities. Currently most research efforts have focused on integrating/calculating the measured multi-station point cloud data, as well as modeling/establishing the 3D meshes of the scanned objects. Very little attention has been spent on extracting the information related to health conditions and mechanical states of structures. In this study, an automated numerical approach that integrates various existing algorithms for geometric identification and damage detection of structural elements were established. Specifically, adaptive meshes were employed for classifying the point cloud data of the structural elements, and detecting the associated damages from the calculated eigenvalues in each area of the structural element. Furthermore, kd-tree was used to enhance the searching efficiency of plane fitting which were later used for identifying the boundaries of structural elements. The results of geometric identification were compared with M3C2 algorithm provided by CloudCompare, as well as validated by LVDT measurements of full-scale reinforced concrete beams tested in laboratory. It shows that 3D laser scanning, through the established processing approaches of the point cloud data, can offer a rapid, nondestructive, remote, and accurate solution for geometric identification and damage detection of structural elements.

Point Cloud Classification of Tesserae from Terrestrial Laser Data Combined with Dense Image Matching for Archaeological Information Extraction

NASA Astrophysics Data System (ADS)

Poux, F.; Neuville, R.; Billen, R.

2017-08-01

Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

Assessing the consistency of UAV-derived point clouds and images acquired at different altitudes

NASA Astrophysics Data System (ADS)

Ozcan, O.

2016-12-01

Unmanned Aerial Vehicles (UAVs) offer several advantages in terms of cost and image resolution compared to terrestrial photogrammetry and satellite remote sensing system. Nowadays, UAVs that bridge the gap between the satellite scale and field scale applications were initiated to be used in various application areas to acquire hyperspatial and high temporal resolution imageries due to working capacity and acquiring in a short span of time with regard to conventional photogrammetry methods. UAVs have been used for various fields such as for the creation of 3-D earth models, production of high resolution orthophotos, network planning, field monitoring and agricultural lands as well. Thus, geometric accuracy of orthophotos and volumetric accuracy of point clouds are of capital importance for land surveying applications. Correspondingly, Structure from Motion (SfM) photogrammetry, which is frequently used in conjunction with UAV, recently appeared in environmental sciences as an impressive tool allowing for the creation of 3-D models from unstructured imagery. In this study, it was aimed to reveal the spatial accuracy of the images acquired from integrated digital camera and the volumetric accuracy of Digital Surface Models (DSMs) which were derived from UAV flight plans at different altitudes using SfM methodology. Low-altitude multispectral overlapping aerial photography was collected at the altitudes of 30 to 100 meters and georeferenced with RTK-GPS ground control points. These altitudes allow hyperspatial imagery with the resolutions of 1-5 cm depending upon the sensor being used. Preliminary results revealed that the vertical comparison of UAV-derived point clouds with respect to GPS measurements pointed out an average distance at cm-level. Larger values are found in areas where instantaneous changes in surface are present.

Absorption of Solar Radiation by Clouds: An Overview

NASA Technical Reports Server (NTRS)

Tsay, Si-Chee; Einaudi, Franco (Technical Monitor)

2000-01-01

This talk provides an overview of the subject of absorption of solar radiation by clouds in the earth's atmosphere. The paper summarizes the available evidence which points to disagreements between theoretical and observed values of cloud absorption (and reflections). The importance of these discrepancies, particularly to remote sensing of clouds as well as to studies of cloud physics and earth radiation budgets, is emphasized. Existing cloud absorption and reflection measurements are reviewed and the persistent differences that exist between calculated and measured near-infrared cloud albedos are highlighted. Various explanations for these reflection and absorption discrepancies are discussed under two separate paths: a theoretician's approach and an experimentalist's approach. Examples for the former approach include model accuracy tests, large-droplet hypothesis, excess absorbing aerosol, enhanced water vapor continuum absorption, and effects of cloud inhomogeneity. The latter approach focuses on discussions of instrumental device, calibration, operational strategy, and signal/noise separation. A recommendation for future activities on this subject will be given.

Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory

NASA Astrophysics Data System (ADS)

Soilán, Mario; Riveiro, Belén; Martínez-Sánchez, Joaquín; Arias, Pedro

2016-04-01

Nowadays, mobile laser scanning has become a valid technology for infrastructure inspection. This technology permits collecting accurate 3D point clouds of urban and road environments and the geometric and semantic analysis of data became an active research topic in the last years. This paper focuses on the detection of vertical traffic signs in 3D point clouds acquired by a LYNX Mobile Mapper system, comprised of laser scanning and RGB cameras. Each traffic sign is automatically detected in the LiDAR point cloud, and its main geometric parameters can be automatically extracted, therefore aiding the inventory process. Furthermore, the 3D position of traffic signs are reprojected on the 2D images, which are spatially and temporally synced with the point cloud. Image analysis allows for recognizing the traffic sign semantics using machine learning approaches. The presented method was tested in road and urban scenarios in Galicia (Spain). The recall results for traffic sign detection are close to 98%, and existing false positives can be easily filtered after point cloud projection. Finally, the lack of a large, publicly available Spanish traffic sign database is pointed out.

A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window

NASA Astrophysics Data System (ADS)

Xin, Meiting; Li, Bing; Yan, Xiao; Chen, Lei; Wei, Xiang

2018-02-01

A robust coarse-to-fine registration method based on the backpropagation (BP) neural network and shift window technology is proposed in this study. Specifically, there are three steps: coarse alignment between the model data and measured data, data simplification based on the BP neural network and point reservation in the contour region of point clouds, and fine registration with the reweighted iterative closest point algorithm. In the process of rough alignment, the initial rotation matrix and the translation vector between the two datasets are obtained. After performing subsequent simplification operations, the number of points can be reduced greatly. Therefore, the time and space complexity of the accurate registration can be significantly reduced. The experimental results show that the proposed method improves the computational efficiency without loss of accuracy.

Prototype methodology for obtaining cloud seeding guidance from HRRR model data

NASA Astrophysics Data System (ADS)

Dawson, N.; Blestrud, D.; Kunkel, M. L.; Waller, B.; Ceratto, J.

2017-12-01

Weather model data, along with real time observations, are critical to determine whether atmospheric conditions are prime for super-cooled liquid water during cloud seeding operations. Cloud seeding groups can either use operational forecast models, or run their own model on a computer cluster. A custom weather model provides the most flexibility, but is also expensive. For programs with smaller budgets, openly-available operational forecasting models are the de facto method for obtaining forecast data. The new High-Resolution Rapid Refresh (HRRR) model (3 x 3 km grid size), developed by the Earth System Research Laboratory (ESRL), provides hourly model runs with 18 forecast hours per run. While the model cannot be fine-tuned for a specific area or edited to provide cloud-seeding-specific output, model output is openly available on a near-real-time basis. This presentation focuses on a prototype methodology for using HRRR model data to create maps which aid in near-real-time cloud seeding decision making. The R programming language is utilized to run a script on a Windows® desktop/laptop computer either on a schedule (such as every half hour) or manually. The latest HRRR model run is downloaded from NOAA's Operational Model Archive and Distribution System (NOMADS). A GRIB-filter service, provided by NOMADS, is used to obtain surface and mandatory pressure level data for a subset domain which greatly cuts down on the amount of data transfer. Then, a set of criteria, identified by the Idaho Power Atmospheric Science Group, is used to create guidance maps. These criteria include atmospheric stability (lapse rates), dew point depression, air temperature, and wet bulb temperature. The maps highlight potential areas where super-cooled liquid water may exist, reasons as to why cloud seeding should not be attempted, and wind speed at flight level.

a Gross Error Elimination Method for Point Cloud Data Based on Kd-Tree

NASA Astrophysics Data System (ADS)

Kang, Q.; Huang, G.; Yang, S.

2018-04-01

Point cloud data has been one type of widely used data sources in the field of remote sensing. Key steps of point cloud data's pro-processing focus on gross error elimination and quality control. Owing to the volume feature of point could data, existed gross error elimination methods need spend massive memory both in space and time. This paper employed a new method which based on Kd-tree algorithm to construct, k-nearest neighbor algorithm to search, settled appropriate threshold to determine with result turns out a judgement that whether target point is or not an outlier. Experimental results show that, our proposed algorithm will help to delete gross error in point cloud data and facilitate to decrease memory consumption, improve efficiency.

Atmospheric Retrieval Analysis of the Directly Imaged Exoplanet HR 8799b

NASA Astrophysics Data System (ADS)

Lee, Jae-Min; Heng, Kevin; Irwin, Patrick G. J.

2013-12-01

Directly imaged exoplanets are unexplored laboratories for the application of the spectral and temperature retrieval method, where the chemistry and composition of their atmospheres are inferred from inverse modeling of the available data. As a pilot study, we focus on the extrasolar gas giant HR 8799b, for which more than 50 data points are available. We upgrade our non-linear optimal estimation retrieval method to include a phenomenological model of clouds that requires the cloud optical depth and monodisperse particle size to be specified. Previous studies have focused on forward models with assumed values of the exoplanetary properties; there is no consensus on the best-fit values of the radius, mass, surface gravity, and effective temperature of HR 8799b. We show that cloud-free models produce reasonable fits to the data if the atmosphere is of super-solar metallicity and non-solar elemental abundances. Intermediate cloudy models with moderate values of the cloud optical depth and micron-sized particles provide an equally reasonable fit to the data and require a lower mean molecular weight. We report our best-fit values for the radius, mass, surface gravity, and effective temperature of HR 8799b. The mean molecular weight is about 3.8, while the carbon-to-oxygen ratio is about unity due to the prevalence of carbon monoxide. Our study emphasizes the need for robust claims about the nature of an exoplanetary atmosphere to be based on analyses involving both photometry and spectroscopy and inferred from beyond a few photometric data points, such as are typically reported for hot Jupiters.

Valorisation of urban elements through 3D models generated from image matching point clouds and augmented reality visualization based in mobile platforms

NASA Astrophysics Data System (ADS)

Marques, Luís.; Roca Cladera, Josep; Tenedório, José António

2017-10-01

The use of multiple sets of images with high level of overlapping to extract 3D point clouds has increased progressively in recent years. There are two main fundamental factors in the origin of this progress. In first, the image matching algorithms has been optimised and the software available that supports the progress of these techniques has been constantly developed. In second, because of the emergent paradigm of smart cities which has been promoting the virtualization of urban spaces and their elements. The creation of 3D models for urban elements is extremely relevant for urbanists to constitute digital archives of urban elements and being especially useful for enrich maps and databases or reconstruct and analyse objects/areas through time, building and recreating scenarios and implementing intuitive methods of interaction. These characteristics assist, for example, higher public participation creating a completely collaborative solution system, envisioning processes, simulations and results. This paper is organized in two main topics. The first deals with technical data modelling obtained by terrestrial photographs: planning criteria for obtaining photographs, approving or rejecting photos based on their quality, editing photos, creating masks, aligning photos, generating tie points, extracting point clouds, generating meshes, building textures and exporting results. The application of these procedures results in 3D models for the visualization of urban elements of the city of Barcelona. The second concerns the use of Augmented Reality through mobile platforms allowing to understand the city origins and the relation with the actual city morphology, (en)visioning solutions, processes and simulations, making possible for the agents in several domains, to fundament their decisions (and understand them) achieving a faster and wider consensus.

A SEMI-ANALYTICAL MODEL OF VISIBLE-WAVELENGTH PHASE CURVES OF EXOPLANETS AND APPLICATIONS TO KEPLER- 7 B AND KEPLER- 10 B

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Renyu; Demory, Brice-Olivier; Seager, Sara

2015-03-20

Kepler has detected numerous exoplanet transits by measuring stellar light in a single visible-wavelength band. In addition to detection, the precise photometry provides phase curves of exoplanets, which can be used to study the dynamic processes on these planets. However, the interpretation of these observations can be complicated by the fact that visible-wavelength phase curves can represent both thermal emission and scattering from the planets. Here we present a semi-analytical model framework that can be applied to study Kepler and future visible-wavelength phase curve observations of exoplanets. The model efficiently computes reflection and thermal emission components for both rocky andmore » gaseous planets, considering both homogeneous and inhomogeneous surfaces or atmospheres. We analyze the phase curves of the gaseous planet Kepler- 7 b and the rocky planet Kepler- 10 b using the model. In general, we find that a hot exoplanet’s visible-wavelength phase curve having a significant phase offset can usually be explained by two classes of solutions: one class requires a thermal hot spot shifted to one side of the substellar point, and the other class requires reflective clouds concentrated on the same side of the substellar point. Particularly for Kepler- 7 b, reflective clouds located on the west side of the substellar point can best explain its phase curve. The reflectivity of the clear part of the atmosphere should be less than 7% and that of the cloudy part should be greater than 80%, and the cloud boundary should be located at 11° ± 3° to the west of the substellar point. We suggest single-band photometry surveys could yield valuable information on exoplanet atmospheres and surfaces.« less

Digital elevation modeling via curvature interpolation for lidar data

USDA-ARS?s Scientific Manuscript database

Digital elevation model (DEM) is a three-dimensional (3D) representation of a terrain's surface - for a planet (including Earth), moon, or asteroid - created from point cloud data which measure terrain elevation. Its modeling requires surface reconstruction for the scattered data, which is an ill-p...

Object Detection using the Kinect

DTIC Science & Technology

2012-03-01

Kinect camera and point cloud data from the Kinect’s structured light stereo system (figure 1). We obtain reasonable results using a single prototype...same manner we present in this report. For example, at Willow Garage , Steder uses a 3-D feature he developed to classify objects directly from point...detecting backpacks using the data available from the Kinect sensor. 4 3.1 Point Cloud Filtering Dense point clouds derived from stereo are notoriously

Reply to "Comment on the Paper ''On the Determination of Electron Polytrope Indices Within Coronal Mass Ejections in the Solar Wind'"'. Appendix 5

NASA Technical Reports Server (NTRS)

Gosling, J. T.; Riley, P.; Skoug, R. M.

2001-01-01

We strongly disagree with the essence of the Osherovich (hereafter Osherovich) comment on one of our papers. The following paragraphs provide the basis of our disagreement and elaborate on why we believe that none of the concluding statements in his Comment are true. Our most important point is that one can apply the model developed by Osherovich and colleagues to real data obtained at a single point in space to determine the polytropic index within magnetic clouds if and only if the highly idealized assumptions of that model conform to physical reality. There is good reason to believe that those assumptions do not provide an accurate physical description of real magnetic clouds in the spherically expanding solar wind.

Fast rockfall hazard assessment along a road section using the new LYNX Mobile Mapper Lidar

NASA Astrophysics Data System (ADS)

Dario, Carrea; Celine, Longchamp; Michel, Jaboyedoff; Marc, Choffet; Marc-Henri, Derron; Clement, Michoud; Andrea, Pedrazzini; Dario, Conforti; Michael, Leslar; William, Tompkinson

2010-05-01

The terrestrial laser scanning (TLS) is an active remote sensing technique providing high resolution point clouds of the topography. The high resolution digital elevations models (HRDEM) derived of these point clouds are an important tool for the stability analysis of slopes. The LYNX Mobile Mapper is a new TLS generation developed by Optech. Its particularity is to be mounted on a vehicle and providing a 360° high density point cloud at 200-khz measurement rate in a very short acquisition time. It is composed of two sensors improving the resolution and reducing the laser shadowing. The spatial resolution is better than 10 cm at 10 m range and at a velocity of 50 km/h and the reflectivity of the signal is around 20% at a distance of 200 m. The Lidar is also equipped with a DGPS and an inertial measurement unit (IMU) which gives real time position and georeferences directly the point cloud. Thanks to its ability to provide a continuous data set from an extended area along a road, this TLS system is useful for rockfall hazard assessment. In addition, this new scanner decrease considerably the time spent in the field and the postprocessing is reduced thanks to resultant georeferenced data. Nevertheless, its application is limited to an area close to the road. The LYNX has been tested near Pontarlier (France) along roads sections affected by rockfall. Regarding to the tectonic context, the studied area is located in the Folded Jura mainly composed of limestone. The result is a very detailed point cloud with a point spacing of 4 cm. The LYNX presents detailed topography on which a structural analysis has been carried out using COLTOP-3D. It allows obtaining a full structural description along the road. In addition, kinematic tests coupled with probabilistic analysis give a susceptibility map of the road cut or natural cliffs above the road. Comparisons with field survey confirm the Lidar approach.

Updating a preoperative surface model with information from real-time tracked 2D ultrasound using a Poisson surface reconstruction algorithm

NASA Astrophysics Data System (ADS)

Sun, Deyu; Rettmann, Maryam E.; Holmes, David R.; Linte, Cristian A.; Packer, Douglas; Robb, Richard A.

2014-03-01

In this work, we propose a method for intraoperative reconstruction of a left atrial surface model for the application of cardiac ablation therapy. In this approach, the intraoperative point cloud is acquired by a tracked, 2D freehand intra-cardiac echocardiography device, which is registered and merged with a preoperative, high resolution left atrial surface model built from computed tomography data. For the surface reconstruction, we introduce a novel method to estimate the normal vector of the point cloud from the preoperative left atrial model, which is required for the Poisson Equation Reconstruction algorithm. In the current work, the algorithm is evaluated using a preoperative surface model from patient computed tomography data and simulated intraoperative ultrasound data. Factors such as intraoperative deformation of the left atrium, proportion of the left atrial surface sampled by the ultrasound, sampling resolution, sampling noise, and registration error were considered through a series of simulation experiments.

A Modular Approach to Video Designation of Manipulation Targets for Manipulators

DTIC Science & Technology

2014-05-12

side view of a ray going through a point cloud of a water bottle sitting on the ground. The bottom left image shows the same point cloud after it has...System (ROS), Point Cloud Library (PCL), and OpenRAVE were used to a great extent to help promote reusability of the code developed during this

How do Polar Stratospheric Clouds Form?

NASA Technical Reports Server (NTRS)

Drdla, Katja; Gandrud, Bruce; Baumgardner, Darrel; Herman, Robert; Gore, Warren J. (Technical Monitor)

2000-01-01

SOLVE measurements have been compared with results from a microphysical model to understand the composition and formation of the polar stratospheric clouds (PSCs) observed during SOLVE. Evidence that the majority of the particles remain liquid throughout the winter will be presented. However, a small fraction of the particles do freeze, and the presence of these frozen particles can not be explained by current theories, in which the only freezing mechanism is homogeneous freezing to ice below the ice frost point. Alternative formation mechanisms, in particular homogeneous freezing above the ice frost point and heterogeneous freezing, have been explored using the microphysical model. Both nitric acid trihydrate (NAT) and nitric acid dihydrate (NAD) have been considered as possible compositions for the solid-phase nitric acid aerosols. Comparisons between the model results and the SOLVE measurements will be used to constrain the possible formation mechanisms. Other effects of these frozen particles will also be discussed, in particular denitrification.

Digital Invasions: from Point Clouds to Historical Building Object Modeling H-Bom of a Unesco Whl Site

NASA Astrophysics Data System (ADS)

Chiabrando, F.; Lo Turco, M.; Santagati, C.

2017-02-01

The paper here presented shows the outcomes of a research/didactic activity carried out within a workshop titled "Digital Invasions. From point cloud to Heritage Building Information Modeling" held at Politecnico di Torino (29th September-5th October 2016). The term digital invasions refers to an Italian bottom up project born in the 2013 with the aim of promoting innovative digital ways for the enhancement of Cultural Heritage by the co-creation of cultural contents and its sharing through social media platforms. At this regard, we have worked with students of Architectural Master of Science degree, training them with a multidisciplinary teaching team (Architectural Representation, History of Architecture, Restoration, Digital Communication and Geomatics). The aim was also to test if our students could be involved in a sort of niche crowdsourcing for the creation of a library of H-BOMS (Historical-Building Object Modeling) of architectural elements.

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Khain, A.; Simpson, S.; Johnson, D.; Li, X.; Remer, L.

2003-01-01

Cloud microphysics are inevitable affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distribution parameterized as spectral bin microphysics are needed to explicitly study the effect of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates convective clouds. Recently, two detailed spectral-bin microphysical schemes were implemented into the Goddard Cumulus Ensembel (GCE) model. The formulation for the explicit spectral-bim microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e., pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), groupel and frozen drops/hall] Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions.A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep cloud systems in the west Pacific warm pool region and in the mid-latitude using identical thermodynamic conditions but with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. Besides the initial differences in aerosol concentration, preliminary results indicate that the low CCN concentration case produces rainfall at the surface sooner than the high CCN case but has less cloud water mass aloft. Because the spectral-bim model explicitly calculates and allows for the examination of both the mass and number concentration of cpecies in each size category, a detailed analysis of the instantaneous size spectrum can be obtained for the two cases. It is shown that since the low CCN case produces fever droplets, larger size develop due to greater condencational and collectional growth, leading to a broader size spectrum in comparison to the high CCN case.

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Khain, A.; Simpson, S.; Johnson, D.; Li, X.; Remer, L.

2003-01-01

Cloud microphysics are inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, two detailed spectral-bin microphysical schemes were implemented into the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral-bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e.,pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e. 33 bins). Atmospheric aerosols are also described using number density size-distribution functions.A spectral-bin microphysical model is very expensive from a from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region using identical thermodynamic conditions but with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. Besides the initial differences in aerosol concentration, preliminary results indicate that the low CCN concentration case produces rainfall at the surface sooner than the high CCN case but has less cloud water mass aloft. Because the spectral-bin model explicitly calculates and allows for the examination of both the mass and number concentration of species in each size categor, a detailed analysis of the instantaneous size spectrum can be obtained for the two cases. It is shown that since the low CCN case produces fewer droplets, larger sized develop due to the greater condensational and collectional growth, leading to a broader size spectrum in comparison to the high CCN case.

Automatic Matching of Large Scale Images and Terrestrial LIDAR Based on App Synergy of Mobile Phone

NASA Astrophysics Data System (ADS)

Xia, G.; Hu, C.

2018-04-01

The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.

The dust cloud of the century

NASA Astrophysics Data System (ADS)

Robock, A.

1983-02-01

The structure and composition of the dust cloud from the 4 April 1982 eruption of the El Chichon volcano in Chiapas state, Mexico, is examined and the possible effects of the dust cloud on the world's weather patterns are discussed. Observations of the cloud using a variety of methods are evaluated, including data from the GOES and NOAA-7 weather satellites, vertically pointing lidar measurements, the SME satellite, and the Nimbus-7 satellite. Studies of the gaseous and particulate composition of the cloud reveal the presence of large amounts of sulfuric acid particles, which have a long mean residence time in the atmosphere and have a large effect on the amount of solar radiation received at the earth's surface by scattering several percent of the radiation back to space. Estimates of the effect of this cloud on surface air temperature changes are presented based on findings from climate models.

Photogrammetric point cloud compression for tactical networks

NASA Astrophysics Data System (ADS)

Madison, Andrew C.; Massaro, Richard D.; Wayant, Clayton D.; Anderson, John E.; Smith, Clint B.

2017-05-01

We report progress toward the development of a compression schema suitable for use in the Army's Common Operating Environment (COE) tactical network. The COE facilitates the dissemination of information across all Warfighter echelons through the establishment of data standards and networking methods that coordinate the readout and control of a multitude of sensors in a common operating environment. When integrated with a robust geospatial mapping functionality, the COE enables force tracking, remote surveillance, and heightened situational awareness to Soldiers at the tactical level. Our work establishes a point cloud compression algorithm through image-based deconstruction and photogrammetric reconstruction of three-dimensional (3D) data that is suitable for dissimination within the COE. An open source visualization toolkit was used to deconstruct 3D point cloud models based on ground mobile light detection and ranging (LiDAR) into a series of images and associated metadata that can be easily transmitted on a tactical network. Stereo photogrammetric reconstruction is then conducted on the received image stream to reveal the transmitted 3D model. The reported method boasts nominal compression ratios typically on the order of 250 while retaining tactical information and accurate georegistration. Our work advances the scope of persistent intelligence, surveillance, and reconnaissance through the development of 3D visualization and data compression techniques relevant to the tactical operations environment.

Using LIDAR and UAV-derived point clouds to evaluate surface roughness in a gravel-bed braided river (Vénéon river, French Alps)

NASA Astrophysics Data System (ADS)

Vázquez Tarrío, Daniel; Borgniet, Laurent; Recking, Alain; Liebault, Frédéric; Vivier, Marie

2016-04-01

The present research is focused on the Vénéon river at Plan du Lac (Massif des Ecrins, France), an alpine braided gravel bed stream with a glacio-nival hydrological regime. It drains a catchment area of 316 km2. The present research is focused in a 2.5 km braided reach placed immediately upstream of a small hydropower dam. An airbone LIDAR survey was accomplished in October, 2014 by EDF (the company managing the small hydropower dam), and data coming from this LIDAR survey were available for the present research. Point density of the LIDAR-derived 3D-point cloud was between 20-50 points/m2, with a vertical precision of 2-3 cm over flat surfaces. Moreover, between April and Juin, 2015, we carried out a photogrammetrical campaign based in aerial images taken with an UAV-drone. The UAV-derived point-cloud has a point density of 200-300 points/m2, and a vertical precision over flat control surfaces comparable to that of the LIDAR point cloud (2-3 cm). Simultaneously to the UAV campaign, we took several Wolman samples with the aim of characterizing the grain size distribution of bed sediment. Wolman samples were taken following a geomorphological criterion (unit bars, head/tail of compound bars). Furthermore, some of the Wolman samples were repeated with the aim of defining the uncertainty of our sampling protocol. LIDAR and UAV-derived point clouds were treated in order to check whether both point-clouds were correctly co-aligned. After that, we estimated bed roughness using the detrended standard deviation of heights, in a 40-cm window. For all this data treatment we used CloudCompare. Then, we measured the distribution of roughness in the same geomorphological units where we took the Wolman samples, and we compared with the grain size distributions measured in the field: differences between UAV-point cloud roughness distributions and measured-grain size distribution (~1-2 cm) are in the same order of magnitude of the differences found between the repeated Wolman samples (~0.5-1.5 cm). Differences with LIDAR-derived roughness distributions are only slightly higher, which could be due to the lower point density of the LIDAR point clouds.

A Method for Obtaining High Frequency, Global, IR-Based Convective Cloud Tops for Studies of the TTL

NASA Technical Reports Server (NTRS)

Pfister, Leonhard; Ueyama, Rei; Jensen, Eric; Schoeberl, Mark

2017-01-01

Models of varying complexity that simulate water vapor and clouds in the Tropical Tropopause Layer (TTL) show that including convection directly is essential to properly simulating the water vapor and cloud distribution. In boreal winter, for example, simulations without convection yield a water vapor distribution that is too uniform with longitude, as well as minimal cloud distributions. Two things are important for convective simulations. First, it is important to get the convective cloud top potential temperature correctly, since unrealistically high values (reaching above the cold point tropopause too frequently) will cause excessive hydration of the stratosphere. Second, one must capture the time variation as well, since hydration by convection depends on the local relative humidity (temperature), which has substantial variation on synoptic time scales in the TTL. This paper describes a method for obtaining high frequency (3-hourly) global convective cloud top distributions which can be used in trajectory models. The method uses rainfall thresholds, standard IR brightness temperatures, meteorological temperature analyses, and physically realistic and documented corrections IR brightness temperature corrections to derive cloud top altitudes and potential temperatures. The cloud top altitudes compare well with combined CLOUDSAT and CALIPSO data, both in time-averaged overall vertical and horizontal distributions and in individual cases (correlations of .65-.7). An important finding is that there is significant uncertainty (nearly .5 km) in evaluating the statistical distribution of convective cloud tops even using lidar. Deep convection whose tops are in regions of high relative humidity (such as much of the TTL), will cause clouds to form above the actual convection. It is often difficult to distinguish these clouds from the actual convective cloud due to the uncertainties of evaluating ice water content from lidar measurements. Comparison with models show that calculated cloud top altitudes are generally higher than those calculated by global analyses (e.g., MERRA). Interannual variability in the distribution of convective cloud top altitudes is also investigated.

Radiative Transfer Model for Operational Retrieval of Cloud Parameters from DSCOVR-EPIC Measurements

NASA Astrophysics Data System (ADS)

Yang, Y.; Molina Garcia, V.; Doicu, A.; Loyola, D. G.

2016-12-01

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) measures the radiance in the backscattering region. To make sure that all details in the backward glory are covered, a large number of streams is required by a standard radiative transfer model based on the discrete ordinates method. Even the use of the delta-M scaling and the TMS correction do not substantially reduce the number of streams. The aim of this work is to analyze the capability of a fast radiative transfer model to retrieve operationally cloud parameters from EPIC measurements. The radiative transfer model combines the discrete ordinates method with matrix exponential for the computation of radiances and the matrix operator method for the calculation of the reflection and transmission matrices. Standard acceleration techniques as, for instance, the use of the normalized right and left eigenvectors, telescoping technique, Pade approximation and successive-order-of-scattering approximation are implemented. In addition, the model may compute the reflection matrix of the cloud by means of the asymptotic theory, and may use the equivalent Lambertian cloud model. The various approximations are analyzed from the point of view of efficiency and accuracy.

Investigation of unsteadiness in Shock-particle cloud interaction: Fully resolved two-dimensional simulation and one-dimensional modeling

NASA Astrophysics Data System (ADS)

Hosseinzadeh-Nik, Zahra; Regele, Jonathan D.

2015-11-01

Dense compressible particle-laden flow, which has a complex nature, exists in various engineering applications. Shock waves impacting a particle cloud is a canonical problem to investigate this type of flow. It has been demonstrated that large flow unsteadiness is generated inside the particle cloud from the flow induced by the shock passage. It is desirable to develop models for the Reynolds stress to capture the energy contained in vortical structures so that volume-averaged models with point particles can be simulated accurately. However, the previous work used Euler equations, which makes the prediction of vorticity generation and propagation innacurate. In this work, a fully resolved two dimensional (2D) simulation using the compressible Navier-Stokes equations with a volume penalization method to model the particles has been performed with the parallel adaptive wavelet-collocation method. The results still show large unsteadiness inside and downstream of the particle cloud. A 1D model is created for the unclosed terms based upon these 2D results. The 1D model uses a two-phase simple low dissipation AUSM scheme (TSLAU) developed by coupled with the compressible two phase kinetic energy equation.

Effects of Stratospheric Lapse Rate on Thunderstorm Cloud-Top Structure in a Three-Dimensional Numerical Simulation. Part I: Some Basic Results of Comparative Experiments.

NASA Astrophysics Data System (ADS)

Schlesinger, Robert E.

1988-05-01

An anelastic three-dimensional model is used to investigate the effects of stratospheric temperature lapse rate on cloud top height/temperature structure for strongly sheared mature isolated midlatitude thunderstorms. Three comparative experiments are performed, differing only with respect to the stratospheric stability. The assumed stratospheric lapse rate is 0 K km1 (isothermal) in the first experiment, 3 K km1 in the second, and 3 K km1 (inversion) in the third.Kinematic storm structure is very similar in all three cases, especially in the troposphere. A strong quasi-steady updraft evolves splitting into a dominant cyclonic overshooting right-mover and a weaker anticyclonic left-mover that does not reach the tropopause. Strongest downdrafts occur at low to middle levels between the updrafts, and in the lower stratosphere a few kilometers upshear and downshear of the tapering updraft summit.Each storm shows a cloud-top thermal couplet, relatively cold near and upshear of the summit, and with a `close-in' warm region downshear. Both cold and warm regions become warmer, with significant morphological changes and a lowering of the cloud summit, as stratospheric stability is increased, though the temperature spread is not greatly affected.The coldest and highest cloud-top points are nearly colocated in the absence of a stratospheric inversion, but the coldest point is offset well upshear of the summit when an inversion is present. The cold region as a whole in each case shows at least a transient `V' shape, with the arms pointing downshear, although this shape is persistent only with the inversion.In the experiment with a 3 K km1 stratospheric lapse rate (weakest stability), the warm region is small and separates into two spots with secondary cold spots downshear of them. The warm region becomes larger, and remains single, as stratospheric stability increase. In each run, the warm regions are not accompanied by corresponding cloud-top height minima except very briefly.The cold cloud-top points are near or slightly downwind of relative vertical velocity maxima, usually positive, while the warm points are imbedded in subsidence downwind of the principal cloud-top downdraft core. The storm-relative cloud-top horizontal wind fields are consistent with the `V' shape of the cold region, showing strong diffluent flow directed downshear along the flanks from an upshear stagnation zone.

Automatic Recognition of Indoor Navigation Elements from Kinect Point Clouds

NASA Astrophysics Data System (ADS)

Zeng, L.; Kang, Z.

2017-09-01

This paper realizes automatically the navigating elements defined by indoorGML data standard - door, stairway and wall. The data used is indoor 3D point cloud collected by Kinect v2 launched in 2011 through the means of ORB-SLAM. By contrast, it is cheaper and more convenient than lidar, but the point clouds also have the problem of noise, registration error and large data volume. Hence, we adopt a shape descriptor - histogram of distances between two randomly chosen points, proposed by Osada and merges with other descriptor - in conjunction with random forest classifier to recognize the navigation elements (door, stairway and wall) from Kinect point clouds. This research acquires navigation elements and their 3-d location information from each single data frame through segmentation of point clouds, boundary extraction, feature calculation and classification. Finally, this paper utilizes the acquired navigation elements and their information to generate the state data of the indoor navigation module automatically. The experimental results demonstrate a high recognition accuracy of the proposed method.

3D change detection at street level using mobile laser scanning point clouds and terrestrial images

NASA Astrophysics Data System (ADS)

Qin, Rongjun; Gruen, Armin

2014-04-01

Automatic change detection and geo-database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images, but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-databases include 3D street-level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection, but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference, and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost-efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi-automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and re-projected between each other to check the geometrical consistency between point clouds and stereo images. Finally, an over-segmentation based graph cut optimization is carried out, taking into account the color, depth and class information to compute the changed area in the image space. The proposed method is invariant to light changes, robust to small co-registration errors between images and point clouds, and can be applied straightforwardly to 3D polyhedral models. This method can be used for 3D street data updating, city infrastructure management and damage monitoring in complex urban scenes.

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Collision Visualization of a Laser-Scanned Point Cloud of Streets and a Festival Float Model Used for the Revival of a Traditional Procession Route

NASA Astrophysics Data System (ADS)

Li, W.; Shigeta, K.; Hasegawa, K.; Li, L.; Yano, K.; Tanaka, S.

2017-09-01

Recently, laser-scanning technology, especially mobile mapping systems (MMSs), has been applied to measure 3D urban scenes. Thus, it has become possible to simulate a traditional cultural event in a virtual space constructed using measured point clouds. In this paper, we take the festival float procession in the Gion Festival that has a long history in Kyoto City, Japan. The city government plans to revive the original procession route that is narrow and not used at present. For the revival, it is important to know whether a festival float collides with houses, billboards, electric wires or other objects along the original route. Therefore, in this paper, we propose a method for visualizing the collisions of point cloud objects. The advantageous features of our method are (1) a see-through visualization with a correct depth feel that is helpful to robustly determine the collision areas, (2) the ability to visualize areas of high collision risk as well as real collision areas, and (3) the ability to highlight target visualized areas by increasing the point densities there.

Scientific Overview of Temporal Experiment for Storms and Tropical Systems (TEMPEST) Program

NASA Astrophysics Data System (ADS)

Chandra, C. V.; Reising, S. C.; Kummerow, C. D.; van den Heever, S. C.; Todd, G.; Padmanabhan, S.; Brown, S. T.; Lim, B.; Haddad, Z. S.; Koch, T.; Berg, G.; L'Ecuyer, T.; Munchak, S. J.; Luo, Z. J.; Boukabara, S. A.; Ruf, C. S.

2014-12-01

Over the past decade and a half, we have gained a better understanding of the role of clouds and precipitation on Earth's water cycle, energy budget and climate, from focused Earth science observational satellite missions. However, these missions provide only a snapshot at one point in time of the cloud's development. Processes that govern cloud system development occur primarily on time scales of the order of 5-30 minutes that are generally not observable from low Earth orbiting satellites. Geostationary satellites, in contrast, have higher temporal resolution but at present are limited to visible and infrared wavelengths that observe only the tops of clouds. This observing gap was noted by the National Research Council's Earth Science Decadal Survey in 2007. Uncertainties in global climate models are significantly affected by processes that govern the formation and dissipation of clouds that largely control the global water and energy budgets. Current uncertainties in cloud parameterization within climate models lead to drastically different climate outcomes. With all evidence suggesting that the precipitation onset may be governed by factors such atmospheric stability, it becomes critical to have at least first-order observations globally in diverse climate regimes. Similar arguments are valid for ice processes where more efficient ice formation and precipitation have a tendency to leave fewer ice clouds behind that have different but equally important impacts on the Earth's energy budget and resulting temperature trends. TEMPEST is a unique program that will provide a small constellation of inexpensive CubeSats with millimeter-wave radiometers to address key science needs related to cloud and precipitation processes. Because these processes are most critical in the development of climate models that will soon run at scales that explicitly resolve clouds, the TEMPEST program will directly focus on examining, validating and improving the parameterizations currently used in cloud scale models. The time evolution of cloud and precipitation microphysics is dependent upon parameterized process rates. The outcome of TEMPEST will provide a first-order understanding of how individual assumptions in current cloud model parameterizations behave in diverse climate regimes.

3D Surface Reconstruction and Volume Calculation of Rills

NASA Astrophysics Data System (ADS)

Brings, Christine; Gronz, Oliver; Becker, Kerstin; Wirtz, Stefan; Seeger, Manuel; Ries, Johannes B.

2015-04-01

We use the low-cost, user-friendly photogrammetric Structure from Motion (SfM) technique, which is implemented in the Software VisualSfM, for 3D surface reconstruction and volume calculation of an 18 meter long rill in Luxembourg. The images were taken with a Canon HD video camera 1) before a natural rainfall event, 2) after a natural rainfall event and before a rill experiment and 3) after a rill experiment. Recording with a video camera results compared to a photo camera not only a huge time advantage, the method also guarantees more than adequately overlapping sharp images. For each model, approximately 8 minutes of video were taken. As SfM needs single images, we automatically selected the sharpest image from 15 frame intervals. The sharpness was estimated using a derivative-based metric. Then, VisualSfM detects feature points in each image, searches matching feature points in all image pairs, recovers the camera positions and finally by triangulation of camera positions and feature points the software reconstructs a point cloud of the rill surface. From the point cloud, 3D surface models (meshes) are created and via difference calculations of the pre and post models a visualization of the changes (erosion and accumulation areas) and quantification of erosion volumes are possible. The calculated volumes are presented in spatial units of the models and so real values must be converted via references. The outputs are three models at three different points in time. The results show that especially using images taken from suboptimal videos (bad lighting conditions, low contrast of the surface, too much in-motion unsharpness), the sharpness algorithm leads to much more matching features. Hence the point densities of the 3D models are increased and thereby clarify the calculations.

Fast Semantic Segmentation of 3d Point Clouds with Strongly Varying Density

NASA Astrophysics Data System (ADS)

Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

2016-06-01

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point's (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

Cross Validation on the Equality of Uav-Based and Contour-Based Dems

NASA Astrophysics Data System (ADS)

Ma, R.; Xu, Z.; Wu, L.; Liu, S.

2018-04-01

Unmanned Aerial Vehicles (UAV) have been widely used for Digital Elevation Model (DEM) generation in geographic applications. This paper proposes a novel framework of generating DEM from UAV images. It starts with the generation of the point clouds by image matching, where the flight control data are used as reference for searching for the corresponding images, leading to a significant time saving. Besides, a set of ground control points (GCP) obtained from field surveying are used to transform the point clouds to the user's coordinate system. Following that, we use a multi-feature based supervised classification method for discriminating non-ground points from ground ones. In the end, we generate DEM by constructing triangular irregular networks and rasterization. The experiments are conducted in the east of Jilin province in China, which has been suffered from soil erosion for several years. The quality of UAV based DEM (UAV-DEM) is compared with that generated from contour interpolation (Contour-DEM). The comparison shows a higher resolution, as well as higher accuracy of UAV-DEMs, which contains more geographic information. In addition, the RMSE errors of the UAV-DEMs generated from point clouds with and without GCPs are ±0.5 m and ±20 m, respectively.

Classification by Using Multispectral Point Cloud Data

NASA Astrophysics Data System (ADS)

Liao, C. T.; Huang, H. H.

2012-07-01

Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

Laser Scanning in Engineering Surveying: Methods of Measurement and Modeling of Structures

NASA Astrophysics Data System (ADS)

Lenda, Grzegorz; Uznański, Andrzej; Strach, Michał; Lewińska, Paulina

2016-06-01

The study is devoted to the uses of laser scanning in the field of engineering surveying. It is currently one of the main trends of research which is developed at the Department of Engineering Surveying and Civil Engineering at the Faculty of Mining Surveying and Environmental Engineering of AGH University of Science and Technology in Krakow. They mainly relate to the issues associated with tower and shell structures, infrastructure of rail routes, or development of digital elevation models for a wide range of applications. These issues often require the use of a variety of scanning techniques (stationary, mobile), but the differences also regard the planning of measurement stations and methods of merging point clouds. Significant differences appear during the analysis of point clouds, especially when modeling objects. Analysis of the selected parameters is already possible basing on ad hoc measurements carried out on a point cloud. However, only the construction of three-dimensional models provides complete information about the shape of structures, allows to perform the analysis in any place and reduces the amount of the stored data. Some structures can be modeled in the form of simple axes, sections, or solids, for others it becomes necessary to create sophisticated models of surfaces, depicting local deformations. The examples selected for the study allow to assess the scope of measurement and office work for a variety of uses related to the issue set forth in the title of this study. Additionally, the latest, forward-looking technology was presented - laser scanning performed from Unmanned Aerial Vehicles (drones). Currently, it is basically in the prototype phase, but it might be expected to make a significant progress in numerous applications in the field of engineering surveying.

Line segment extraction for large scale unorganized point clouds

NASA Astrophysics Data System (ADS)

Lin, Yangbin; Wang, Cheng; Cheng, Jun; Chen, Bili; Jia, Fukai; Chen, Zhonggui; Li, Jonathan

2015-04-01

Line segment detection in images is already a well-investigated topic, although it has received considerably less attention in 3D point clouds. Benefiting from current LiDAR devices, large-scale point clouds are becoming increasingly common. Most human-made objects have flat surfaces. Line segments that occur where pairs of planes intersect give important information regarding the geometric content of point clouds, which is especially useful for automatic building reconstruction and segmentation. This paper proposes a novel method that is capable of accurately extracting plane intersection line segments from large-scale raw scan points. The 3D line-support region, namely, a point set near a straight linear structure, is extracted simultaneously. The 3D line-support region is fitted by our Line-Segment-Half-Planes (LSHP) structure, which provides a geometric constraint for a line segment, making the line segment more reliable and accurate. We demonstrate our method on the point clouds of large-scale, complex, real-world scenes acquired by LiDAR devices. We also demonstrate the application of 3D line-support regions and their LSHP structures on urban scene abstraction.

Characterizing Sorghum Panicles using 3D Point Clouds

NASA Astrophysics Data System (ADS)

Lonesome, M.; Popescu, S. C.; Horne, D. W.; Pugh, N. A.; Rooney, W.

2017-12-01

To address demands of population growth and impacts of global climate change, plant breeders must increase crop yield through genetic improvement. However, plant phenotyping, the characterization of a plant's physical attributes, remains a primary bottleneck in modern crop improvement programs. 3D point clouds generated from terrestrial laser scanning (TLS) and unmanned aerial systems (UAS) based structure from motion (SfM) are a promising data source to increase the efficiency of screening plant material in breeding programs. This study develops and evaluates methods for characterizing sorghum (Sorghum bicolor) panicles (heads) in field plots from both TLS and UAS-based SfM point clouds. The TLS point cloud over experimental sorghum field at Texas A&M farm in Burleston County TX were collected using a FARO Focus X330 3D laser scanner. SfM point cloud was generated from UAS imagery captured using a Phantom 3 Professional UAS at 10m altitude and 85% image overlap. The panicle detection method applies point cloud reflectance, height and point density attributes characteristic of sorghum panicles to detect them and estimate their dimensions (panicle length and width) through image classification and clustering procedures. We compare the derived panicle counts and panicle sizes with field-based and manually digitized measurements in selected plots and study the strengths and limitations of each data source for sorghum panicle characterization.

Gas release and conductivity modification studies

NASA Technical Reports Server (NTRS)

Linson, L. M.; Baxter, D. C.

1979-01-01

The behavior of gas clouds produced by releases from orbital velocity in either a point release or venting mode is described by the modification of snowplow equations valid in an intermediate altitude regime. Quantitative estimates are produced for the time dependence of the radius of the cloud, the average internal energy, the translational velocity, and the distance traveled. The dependence of these quantities on the assumed density profile, the internal energy of the gas, and the ratio of specific heats is examined. The new feature is the inclusion of the effect of the large orbital velocity. The resulting gas cloud models are used to calculate the characteristics of the field line integrated Pedersen conductivity enhancements that would be produced by the release of barium thermite at orbital velocity in either the point release or venting modes as a function of release altitude and chemical payload weight.

Dependence of marine stratocumulus reflectivities on liquid water paths

NASA Technical Reports Server (NTRS)

Coakley, James A., Jr.; Snider, Jack B.

1990-01-01

Simple parameterizations that relate cloud liquid water content to cloud reflectivity are often used in general circulation climate models to calculate the effect of clouds in the earth's energy budget. Such parameterizations have been developed by Stephens (1978) and by Slingo and Schrecker (1982) and others. Here researchers seek to verify the parametric relationship through the use of simultaneous observations of cloud liquid water content and cloud reflectivity. The column amount of cloud liquid was measured using a microwave radiometer on San Nicolas Island following techniques described by Hogg et al., (1983). Cloud reflectivity was obtained through spatial coherence analysis of Advanced Very High Resolution Radiometer (AVHRR) imagery data (Coakley and Beckner, 1988). They present the dependence of the observed reflectivity on the observed liquid water path. They also compare this empirical relationship with that proposed by Stephens (1978). Researchers found that by taking clouds to be isotropic reflectors, the observed reflectivities and observed column amounts of cloud liquid water are related in a manner that is consistent with simple parameterizations often used in general circulation climate models to determine the effect of clouds on the earth's radiation budget. Attempts to use the results of radiative transfer calculations to correct for the anisotropy of the AVHRR derived reflectivities resulted in a greater scatter of the points about the relationship expected between liquid water path and reflectivity. The anisotropy of the observed reflectivities proved to be small, much smaller than indicated by theory. To critically assess parameterizations, more simultaneous observations of cloud liquid water and cloud reflectivities and better calibration of the AVHRR sensors are needed.

Surface Deformation Associated with the 1983 Borah Peak Earthquake Measured from Digital Surface Model Differencing

NASA Astrophysics Data System (ADS)

Reitman, N. G.; Briggs, R.; Gold, R. D.; DuRoss, C. B.

2015-12-01

Post-earthquake, field-based assessments of surface displacement commonly underestimate offsets observed with remote sensing techniques (e.g., InSAR, image cross-correlation) because they fail to capture the total deformation field. Modern earthquakes are readily characterized by comparing pre- and post-event remote sensing data, but historical earthquakes often lack pre-event data. To overcome this challenge, we use historical aerial photographs to derive pre-event digital surface models (DSMs), which we compare to modern, post-event DSMs. Our case study focuses on resolving on- and off-fault deformation along the Lost River fault that accompanied the 1983 M6.9 Borah Peak, Idaho, normal-faulting earthquake. We use 343 aerial images from 1952-1966 and vertical control points selected from National Geodetic Survey benchmarks measured prior to 1983 to construct a pre-event point cloud (average ~ 0.25 pts/m2) and corresponding DSM. The post-event point cloud (average ~ 1 pt/m2) and corresponding DSM are derived from WorldView 1 and 2 scenes processed with NASA's Ames Stereo Pipeline. The point clouds and DSMs are coregistered using vertical control points, an iterative closest point algorithm, and a DSM coregistration algorithm. Preliminary results of differencing the coregistered DSMs reveal a signal spanning the surface rupture that is consistent with tectonic displacement. Ongoing work is focused on quantifying the significance of this signal and error analysis. We expect this technique to yield a more complete understanding of on- and off-fault deformation patterns associated with the Borah Peak earthquake along the Lost River fault and to help improve assessments of surface deformation for other historical ruptures.

A continuous surface reconstruction method on point cloud captured from a 3D surface photogrammetry system.

PubMed

Liu, Wenyang; Cheung, Yam; Sabouri, Pouya; Arai, Tatsuya J; Sawant, Amit; Ruan, Dan

2015-11-01

To accurately and efficiently reconstruct a continuous surface from noisy point clouds captured by a surface photogrammetry system (VisionRT). The authors have developed a level-set based surface reconstruction method on point clouds captured by a surface photogrammetry system (VisionRT). The proposed method reconstructs an implicit and continuous representation of the underlying patient surface by optimizing a regularized fitting energy, offering extra robustness to noise and missing measurements. By contrast to explicit/discrete meshing-type schemes, their continuous representation is particularly advantageous for subsequent surface registration and motion tracking by eliminating the need for maintaining explicit point correspondences as in discrete models. The authors solve the proposed method with an efficient narrowband evolving scheme. The authors evaluated the proposed method on both phantom and human subject data with two sets of complementary experiments. In the first set of experiment, the authors generated a series of surfaces each with different black patches placed on one chest phantom. The resulting VisionRT measurements from the patched area had different degree of noise and missing levels, since VisionRT has difficulties in detecting dark surfaces. The authors applied the proposed method to point clouds acquired under these different configurations, and quantitatively evaluated reconstructed surfaces by comparing against a high-quality reference surface with respect to root mean squared error (RMSE). In the second set of experiment, the authors applied their method to 100 clinical point clouds acquired from one human subject. In the absence of ground-truth, the authors qualitatively validated reconstructed surfaces by comparing the local geometry, specifically mean curvature distributions, against that of the surface extracted from a high-quality CT obtained from the same patient. On phantom point clouds, their method achieved submillimeter reconstruction RMSE under different configurations, demonstrating quantitatively the faith of the proposed method in preserving local structural properties of the underlying surface in the presence of noise and missing measurements, and its robustness toward variations of such characteristics. On point clouds from the human subject, the proposed method successfully reconstructed all patient surfaces, filling regions where raw point coordinate readings were missing. Within two comparable regions of interest in the chest area, similar mean curvature distributions were acquired from both their reconstructed surface and CT surface, with mean and standard deviation of (μrecon=-2.7×10(-3) mm(-1), σrecon=7.0×10(-3) mm(-1)) and (μCT=-2.5×10(-3) mm(-1), σCT=5.3×10(-3) mm(-1)), respectively. The agreement of local geometry properties between the reconstructed surfaces and the CT surface demonstrated the ability of the proposed method in faithfully representing the underlying patient surface. The authors have integrated and developed an accurate level-set based continuous surface reconstruction method on point clouds acquired by a 3D surface photogrammetry system. The proposed method has generated a continuous representation of the underlying phantom and patient surfaces with good robustness against noise and missing measurements. It serves as an important first step for further development of motion tracking methods during radiotherapy.

The evaluation of GCMs and a new cloud parameterisation using satellite and in-situ data as part of a Climate Process Team

NASA Astrophysics Data System (ADS)

Grosvenor, D. P.; Wood, R.

2012-12-01

As part of one of the Climate Process Teams (CPTs) we have been testing the implementation of a new cloud parameterization into the CAM5 and AM3 GCMs. The CLUBB parameterization replaces all but the deep convection cloud scheme and uses an innovative PDF based approach to diagnose cloud water content and turbulence. We have evaluated the base models and the CLUBB parameterization in the SE Pacific stratocumulus region using a suite of satellite observation metrics including: Liquid Water Path (LWP) measurements from AMSRE; cloud fractions from CloudSat/CALIPSO; droplet concentrations (Nd) and Cloud Top Temperatures from MODIS; CloudSat precipitation; and relationships between Estimated Inversion Strength (calculated from AMSRE SSTs, Cloud Top Temperatures from MODIS and ECMWF re-analysis fields) and cloud fraction. This region has the advantage of an abundance of in-situ aircraft observations taken during the VOCALS campaign, which is facilitating the diagnosis of the model problems highlighted by the model evaluation. This data has also been recently used to demonstrate the reliability of MODIS Nd estimates. The satellite data needs to be filtered to ensure accurate retrievals and we have been careful to apply the same screenings to the model fields. For example, scenes with high cloud fractions and with output times near to the satellite overpass times can be extracted from the model for a fair comparison with MODIS Nd estimates. To facilitate this we have been supplied with instantaneous model output since screening would not be possible based on time averaged data. We also have COSP satellite simulator output, which allows a fairer comparison between satellite and model. For example, COSP cloud fraction is based upon the detection threshold of the satellite instrument in question. These COSP fields are also used for the model output filtering just described. The results have revealed problems with both the base models and the versions with the CLUBB parameterization. The CAM5 model produces realistic near-coast cloud cover, but too little further west in the stratocumulus to cumulus regions. The implementation of CLUBB has vastly improved this situation with cloud cover that is very similar to that observed. CLUBB also improves the Nd field in CAM5 by producing realistic near-coast increases and by removing high Nd values associated with the detrainment of droplets by cumulus clouds. AM3 has a lack of stratocumulus cloud near the South American coast and has much lower droplet concentrations than observed. VOCALS measurements showed that sulfate mass loadings were generally too high in both base models, whereas CCN concentrations were too low. This suggests a problem with the mass distribution partitioning of sulfate that is being investigated. Diurnal and seasonal comparisons have been very illuminating. CLUBB produces very little diurnal variation in LWP, but large variations in precipitation rates. This is likely to point to problems that are now being addressed by the modeling part of the CPT team, creating an iterative workflow process between the model developers and the model testers, which should facilitate efficient parameterization improvement. We will report on the latest developments of this process.

3D point cloud analysis of structured light registration in computer-assisted navigation in spinal surgeries

NASA Astrophysics Data System (ADS)

Gupta, Shaurya; Guha, Daipayan; Jakubovic, Raphael; Yang, Victor X. D.

2017-02-01

Computer-assisted navigation is used by surgeons in spine procedures to guide pedicle screws to improve placement accuracy and in some cases, to better visualize patient's underlying anatomy. Intraoperative registration is performed to establish a correlation between patient's anatomy and the pre/intra-operative image. Current algorithms rely on seeding points obtained directly from the exposed spinal surface to achieve clinically acceptable registration accuracy. Registration of these three dimensional surface point-clouds are prone to various systematic errors. The goal of this study was to evaluate the robustness of surgical navigation systems by looking at the relationship between the optical density of an acquired 3D point-cloud and the corresponding surgical navigation error. A retrospective review of a total of 48 registrations performed using an experimental structured light navigation system developed within our lab was conducted. For each registration, the number of points in the acquired point cloud was evaluated relative to whether the registration was acceptable, the corresponding system reported error and target registration error. It was demonstrated that the number of points in the point cloud neither correlates with the acceptance/rejection of a registration or the system reported error. However, a negative correlation was observed between the number of the points in the point-cloud and the corresponding sagittal angular error. Thus, system reported total registration points and accuracy are insufficient to gauge the accuracy of a navigation system and the operating surgeon must verify and validate registration based on anatomical landmarks prior to commencing surgery.

3D Reconstruction of Irregular Buildings and Buddha Statues

NASA Astrophysics Data System (ADS)

Zhang, K.; Li, M.-j.

2014-04-01

Three-dimensional laser scanning could acquire object's surface data quickly and accurately. However, the post-processing of point cloud is not perfect and could be improved. Based on the study of 3D laser scanning technology, this paper describes the details of solutions to modelling irregular ancient buildings and Buddha statues in Jinshan Temple, which aiming at data acquisition, modelling and texture mapping, etc. In order to modelling irregular ancient buildings effectively, the structure of each building is extracted manually by point cloud and the textures are mapped by the software of 3ds Max. The methods clearly combine 3D laser scanning technology with traditional modelling methods, and greatly improves the efficiency and accuracy of the ancient buildings restored. On the other hand, the main idea of modelling statues is regarded as modelling objects in reverse engineering. The digital model of statues obtained is not just vivid, but also accurate in the field of surveying and mapping. On this basis, a 3D scene of Jinshan Temple is reconstructed, which proves the validity of the solutions.

Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping.

PubMed

Cui, Tingting; Ji, Shunping; Shan, Jie; Gong, Jianya; Liu, Kejian

2016-12-31

For multi-sensor integrated systems, such as the mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable.

Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping

PubMed Central

Cui, Tingting; Ji, Shunping; Shan, Jie; Gong, Jianya; Liu, Kejian

2016-01-01

For multi-sensor integrated systems, such as the mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable. PMID:28042855

Object-Based Coregistration of Terrestrial Photogrammetric and ALS Point Clouds in Forested Areas

NASA Astrophysics Data System (ADS)

Polewski, P.; Erickson, A.; Yao, W.; Coops, N.; Krzystek, P.; Stilla, U.

2016-06-01

Airborne Laser Scanning (ALS) and terrestrial photogrammetry are methods applicable for mapping forested environments. While ground-based techniques provide valuable information about the forest understory, the measured point clouds are normally expressed in a local coordinate system, whose transformation into a georeferenced system requires additional effort. In contrast, ALS point clouds are usually georeferenced, yet the point density near the ground may be poor under dense overstory conditions. In this work, we propose to combine the strengths of the two data sources by co-registering the respective point clouds, thus enriching the georeferenced ALS point cloud with detailed understory information in a fully automatic manner. Due to markedly different sensor characteristics, coregistration methods which expect a high geometric similarity between keypoints are not suitable in this setting. Instead, our method focuses on the object (tree stem) level. We first calculate approximate stem positions in the terrestrial and ALS point clouds and construct, for each stem, a descriptor which quantifies the 2D and vertical distances to other stem centers (at ground height). Then, the similarities between all descriptor pairs from the two point clouds are calculated, and standard graph maximum matching techniques are employed to compute corresponding stem pairs (tiepoints). Finally, the tiepoint subset yielding the optimal rigid transformation between the terrestrial and ALS coordinate systems is determined. We test our method on simulated tree positions and a plot situated in the northern interior of the Coast Range in western Oregon, USA, using ALS data (76 x 121 m2) and a photogrammetric point cloud (33 x 35 m2) derived from terrestrial photographs taken with a handheld camera. Results on both simulated and real data show that the proposed stem descriptors are discriminative enough to derive good correspondences. Specifically, for the real plot data, 24 corresponding stems were coregistered with an average 2D position deviation of 66 cm.

Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry

NASA Astrophysics Data System (ADS)

Javernick, L.; Brasington, J.; Caruso, B.

2014-05-01

Recent advances in computer vision and image analysis have led to the development of a novel, fully automated photogrammetric method to generate dense 3d point cloud data. This approach, termed Structure-from-Motion or SfM, requires only limited ground-control and is ideally suited to imagery obtained from low-cost, non-metric cameras acquired either at close-range or using aerial platforms. Terrain models generated using SfM have begun to emerge recently and with a growing spectrum of software now available, there is an urgent need to provide a robust quality assessment of the data products generated using standard field and computational workflows. To address this demand, we present a detailed error analysis of sub-meter resolution terrain models of two contiguous reaches (1.6 and 1.7 km long) of the braided Ahuriri River, New Zealand, generated using SfM. A six stage methodology is described, involving: i) hand-held image acquisition from an aerial platform, ii) 3d point cloud extraction modeling using Agisoft PhotoScan, iii) georeferencing on a redundant network of GPS-surveyed ground-control points, iv) point cloud filtering to reduce computational demand as well as reduce vegetation noise, v) optical bathymetric modeling of inundated areas; and vi) data fusion and surface modeling to generate sub-meter raster terrain models. Bootstrapped geo-registration as well as extensive distributed GPS and sonar-based bathymetric check-data were used to quantify the quality of the models generated after each processing step. The results obtained provide the first quantified analysis of SfM applied to model the complex terrain of a braided river. Results indicate that geo-registration errors of 0.04 m (planar) and 0.10 m (elevation) and vertical surface errors of 0.10 m in non-vegetation areas can be achieved from a dataset of photographs taken at 600 m and 800 m above the ground level. These encouraging results suggest that this low-cost, logistically simple method can deliver high quality terrain datasets competitive with those obtained with significantly more expensive laser scanning, and suitable for geomorphic change detection and hydrodynamic modeling.

Robotic Online Path Planning on Point Cloud.

PubMed

Liu, Ming

2016-05-01

This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.

A method of 3D object recognition and localization in a cloud of points

NASA Astrophysics Data System (ADS)

Bielicki, Jerzy; Sitnik, Robert

2013-12-01

The proposed method given in this article is prepared for analysis of data in the form of cloud of points directly from 3D measurements. It is designed for use in the end-user applications that can directly be integrated with 3D scanning software. The method utilizes locally calculated feature vectors (FVs) in point cloud data. Recognition is based on comparison of the analyzed scene with reference object library. A global descriptor in the form of a set of spatially distributed FVs is created for each reference model. During the detection process, correlation of subsets of reference FVs with FVs calculated in the scene is computed. Features utilized in the algorithm are based on parameters, which qualitatively estimate mean and Gaussian curvatures. Replacement of differentiation with averaging in the curvatures estimation makes the algorithm more resistant to discontinuities and poor quality of the input data. Utilization of the FV subsets allows to detect partially occluded and cluttered objects in the scene, while additional spatial information maintains false positive rate at a reasonably low level.

Study on the high-frequency laser measurement of slot surface difference

NASA Astrophysics Data System (ADS)

Bing, Jia; Lv, Qiongying; Cao, Guohua

2017-10-01

In view of the measurement of the slot surface difference in the large-scale mechanical assembly process, Based on high frequency laser scanning technology and laser detection imaging principle, This paragraph designs a double galvanometer pulse laser scanning system. Laser probe scanning system architecture consists of three parts: laser ranging part, mechanical scanning part, data acquisition and processing part. The part of laser range uses high-frequency laser range finder to measure the distance information of the target shape and get a lot of point cloud data. Mechanical scanning part includes high-speed rotary table, high-speed transit and related structure design, in order to realize the whole system should be carried out in accordance with the design of scanning path on the target three-dimensional laser scanning. Data processing part mainly by FPGA hardware with LAbVIEW software to design a core, to process the point cloud data collected by the laser range finder at the high-speed and fitting calculation of point cloud data, to establish a three-dimensional model of the target, so laser scanning imaging is realized.

How Models Simulate the Radiative Effect in the Transition Zone of the Aerosol-Cloud Continuum

NASA Astrophysics Data System (ADS)

Calbo Angrill, J.; González, J. A.; Long, C. N.; McComiskey, A. C.

2017-12-01

Several studies have pointed towards dealing with clouds and aerosols as two manifestations of what is essentially the same physical phenomenon: a suspension of tiny particles in the air. Although the two extreme cases (i.e., pure aerosol and well-defined cloud) are easily distinguished, and obviously produce different radiative effects, there are many situations in the transition (or "twilight") zone. In a recent paper [Calbó et al., Atmos. Res. 2017, j.atmosres.2017.06.010], the authors of the current communication estimated that about 10% of time there might be a suspension of particles in the air that is difficult to distinguish as either cloud or aerosol. Radiative transfer models, however, simulate the effect of clouds and aerosols with different modules, routines, or parameterizations. In this study, we apply a sensitivity analysis approach to assess the ability of two radiative transfer models (SBDART and RRTM) in simulating the radiative effect of a suspension of particles with characteristics in the boundary between cloud and aerosol. We simulate this kind of suspension either in "cloud mode" or in "aerosol mode" and setting different values of optical depth, droplet size, water path, aerosol type, cloud height, etc. Irradiances both for solar and infrared bands are studied, both at ground level and at the top of the atmosphere, and all analyses are repeated for different solar zenith angles. We obtain that (a) water clouds and ice clouds have similar radiative effects if they have the same optical depth; (b) the spread of effects regarding different aerosol type/aerosol characteristics is remarkable; (c) radiative effects of an aerosol layer and of a cloud layer are different, even if they have similar optical depth; (d) for a given effect on the diffuse component, the effect on the direct component is usually greater (more extinction of direct beam) by aerosols than by clouds; (e) radiative transfer models are somewhat limited when simulating the effects of a suspension of particles in the transition zone, as the approach to this zone as an aerosol or as a cloud produces different results.

Sharing Planetary-Scale Data in the Cloud

NASA Astrophysics Data System (ADS)

Sundwall, J.; Flasher, J.

2016-12-01

On 19 March 2015, Amazon Web Services (AWS) announced Landsat on AWS, an initiative to make data from the U.S. Geological Survey's Landsat satellite program freely available in the cloud. Because of Landsat's global coverage and long history, it has become a reference point for all Earth observation work and is considered the gold standard of natural resource satellite imagery. Within the first year of Landsat on AWS, the service served over a billion requests for Landsat imagery and metadata, globally. Availability of the data in the cloud has led to new product development by companies and startups including Mapbox, Esri, CartoDB, MathWorks, Development Seed, Trimble, Astro Digital, Blue Raster and Timbr.io. The model of staging data for analysis in the cloud established by Landsat on AWS has since been applied to high resolution radar data, European Space Agency satellite imagery, global elevation data and EPA air quality models. This session will provide an overview of lessons learned throughout these projects. It will demonstrate how cloud-based object storage is democratizing access to massive publicly-funded data sets that have previously only been available to people with access to large amounts of storage, bandwidth, and computing power. Technical discussion points will include: The differences between staging data for analysis using object storage versus file storage Using object stores to design simple RESTful APIs through thoughtful file naming conventions, header fields, and HTTP Range Requests Managing costs through data architecture and Amazon S3's "requester pays" feature Building tools that allow users to take their algorithm to the data in the cloud Using serverless technologies to display dynamic frontends for massive data sets

3D Reconstruction of Static Human Body with a Digital Camera

NASA Astrophysics Data System (ADS)

Remondino, Fabio

2003-01-01

Nowadays the interest in 3D reconstruction and modeling of real humans is one of the most challenging problems and a topic of great interest. The human models are used for movies, video games or ergonomics applications and they are usually created with 3D scanner devices. In this paper a new method to reconstruct the shape of a static human is presented. Our approach is based on photogrammetric techniques and uses a sequence of images acquired around a standing person with a digital still video camera or with a camcorder. First the images are calibrated and orientated using a bundle adjustment. After the establishment of a stable adjusted image block, an image matching process is performed between consecutive triplets of images. Finally the 3D coordinates of the matched points are computed with a mean accuracy of ca 2 mm by forward ray intersection. The obtained point cloud can then be triangulated to generate a surface model of the body or a virtual human model can be fitted to the recovered 3D data. Results of the 3D human point cloud with pixel color information are presented.

3DVEM Software Modules for Efficient Management of Point Clouds and Photorealistic 3d Models

NASA Astrophysics Data System (ADS)

Fabado, S.; Seguí, A. E.; Cabrelles, M.; Navarro, S.; García-De-San-Miguel, D.; Lerma, J. L.

2013-07-01

Cultural heritage managers in general and information users in particular are not usually used to deal with high-technological hardware and software. On the contrary, information providers of metric surveys are most of the times applying latest developments for real-life conservation and restoration projects. This paper addresses the software issue of handling and managing either 3D point clouds or (photorealistic) 3D models to bridge the gap between information users and information providers as regards the management of information which users and providers share as a tool for decision-making, analysis, visualization and management. There are not many viewers specifically designed to handle, manage and create easily animations of architectural and/or archaeological 3D objects, monuments and sites, among others. 3DVEM - 3D Viewer, Editor & Meter software will be introduced to the scientific community, as well as 3DVEM - Live and 3DVEM - Register. The advantages of managing projects with both sets of data, 3D point cloud and photorealistic 3D models, will be introduced. Different visualizations of true documentation projects in the fields of architecture, archaeology and industry will be presented. Emphasis will be driven to highlight the features of new userfriendly software to manage virtual projects. Furthermore, the easiness of creating controlled interactive animations (both walkthrough and fly-through) by the user either on-the-fly or as a traditional movie file will be demonstrated through 3DVEM - Live.

Particle backscatter and relative humidity measured across cirrus clouds and comparison with microphysical cirrus modelling

NASA Astrophysics Data System (ADS)

Brabec, M.; Wienhold, F. G.; Luo, B. P.; Vömel, H.; Immler, F.; Steiner, P.; Hausammann, E.; Weers, U.; Peter, T.

2012-10-01

Advanced measurement and modelling techniques are employed to estimate the partitioning of atmospheric water between the gas phase and the condensed phase in and around cirrus clouds, and thus to identify in-cloud and out-of-cloud supersaturations with respect to ice. In November 2008 the newly developed balloon-borne backscatter sonde COBALD (Compact Optical Backscatter and AerosoL Detector) was flown 14 times together with a CFH (Cryogenic Frost point Hygrometer) from Lindenberg, Germany (52° N, 14° E). The case discussed here in detail shows two cirrus layers with in-cloud relative humidities with respect to ice between 50% and 130%. Global operational analysis data of ECMWF (roughly 1° × 1° horizontal and 1 km vertical resolution, 6-hourly stored fields) fail to represent ice water contents and relative humidities. Conversely, regional COSMO-7 forecasts (6.6 km × 6.6 km, 5-min stored fields) capture the measured humidities and cloud positions remarkably well. The main difference between ECMWF and COSMO data is the resolution of small-scale vertical features responsible for cirrus formation. Nevertheless, ice water contents in COSMO-7 are still off by factors 2-10, likely reflecting limitations in COSMO's ice phase bulk scheme. Significant improvements can be achieved by comprehensive size-resolved microphysical and optical modelling along backward trajectories based on COSMO-7 wind and temperature fields, which allow accurate computation of humidities, homogeneous ice nucleation, resulting ice particle size distributions and backscatter ratios at the COBALD wavelengths. However, only by superimposing small-scale temperature fluctuations, which remain unresolved by the numerical weather prediction models, can we obtain a satisfying agreement with the observations and reconcile the measured in-cloud non-equilibrium humidities with conventional ice cloud microphysics. Conversely, the model-data comparison provides no evidence that additional changes to ice-cloud microphysics - such as heterogeneous nucleation or changing the water vapour accommodation coefficient on ice - are required.

Structure-from-motion approach for characterization of bioerosion patterns using UAV imagery.

PubMed

Genchi, Sibila A; Vitale, Alejandro J; Perillo, Gerardo M E; Delrieux, Claudio A

2015-02-04

The aim of this work is to evaluate the applicability of the 3D model obtained through Structure-from-Motion (SFM) from unmanned aerial vehicle (UAV) imagery, in order to characterize bioerosion patterns (i.e., cavities for roosting and nesting) caused by burrowing parrots on a cliff in Bahía Blanca, Argentina. The combined use of SFM-UAV technology was successfully applied for the 3D point cloud model reconstruction. The local point density, obtained by means of a sphere of radius equal to 0.5 m, reached a mean value of 9749, allowing to build a high-resolution model (0.013 m) for resolving fine spatial details in topography. To test the model, we compared it with another point cloud dataset which was created using a low cost do-it-yourself terrestrial laser scanner; the results showed that our georeferenced model had a good accuracy. In addition, an innovative method for the detection of the bioerosion features was implemented, through the processing of data provided by SFM like color and spatial coordinates (particularly the y coordinate). From the 3D model, we also derived topographic calculations such as slope angle and surface roughness, to get associations between the surface topography and bioerosion features.

Structure-from-Motion Approach for Characterization of Bioerosion Patterns Using UAV Imagery

PubMed Central

Genchi, Sibila A.; Vitale, Alejandro J.; Perillo, Gerardo M. E.; Delrieux, Claudio A.

2015-01-01

The aim of this work is to evaluate the applicability of the 3D model obtained through Structure-from-Motion (SFM) from unmanned aerial vehicle (UAV) imagery, in order to characterize bioerosion patterns (i.e., cavities for roosting and nesting) caused by burrowing parrots on a cliff in Bahía Blanca, Argentina. The combined use of SFM-UAV technology was successfully applied for the 3D point cloud model reconstruction. The local point density, obtained by means of a sphere of radius equal to 0.5 m, reached a mean value of 9749, allowing to build a high-resolution model (0.013 m) for resolving fine spatial details in topography. To test the model, we compared it with another point cloud dataset which was created using a low cost do-it-yourself terrestrial laser scanner; the results showed that our georeferenced model had a good accuracy. In addition, an innovative method for the detection of the bioerosion features was implemented, through the processing of data provided by SFM like color and spatial coordinates (particularly the y coordinate). From the 3D model, we also derived topographic calculations such as slope angle and surface roughness, to get associations between the surface topography and bioerosion features. PMID:25658392

Erosion and Channel Incision Analysis with High-Resolution Lidar

NASA Astrophysics Data System (ADS)

Potapenko, J.; Bookhagen, B.

2013-12-01

High-resolution LiDAR (LIght Detection And Ranging) provides a new generation of sub-meter topographic data that is still to be fully exploited by the Earth science communities. We make use of multi-temporal airborne and terrestrial lidar scans in the south-central California and Santa Barbara area. Specifically, we have investigated the Mission Canyon and Channel Islands regions from 2009-2011 to study changes in erosion and channel incision on the landscape. In addition to gridding the lidar data into digital elevation models (DEMs), we also make use of raw lidar point clouds and triangulated irregular networks (TINs) for detailed analysis of heterogeneously spaced topographic data. Using recent advancements in lidar point cloud processing from information technology disciplines, we have employed novel lidar point cloud processing and feature detection algorithms to automate the detection of deeply incised channels and gullies, vegetation, and other derived metrics (e.g. estimates of eroded volume). Our analysis compares topographically-derived erosion volumes to field-derived cosmogenic radionuclide age and in-situ sediment-flux measurements. First results indicate that gully erosion accounts for up to 60% of the sediment volume removed from the Mission Canyon region. Furthermore, we observe that gully erosion and upstream arroyo propagation accelerated after fires, especially in regions where vegetation was heavily burned. The use of high-resolution lidar point cloud data for topographic analysis is still a novel method that needs more precedent and we hope to provide a cogent example of this approach with our research.

Particle backscatter and relative humidity measured across cirrus clouds and comparison with state-of-the-art cirrus modelling

NASA Astrophysics Data System (ADS)

Brabec, M.; Wienhold, F. G.; Luo, B.; Vömel, H.; Immler, F.; Steiner, P.; Peter, T.

2012-04-01

Advanced measurement and modelling techniques are employed to determine the partitioning of atmospheric water between the gas phase and the condensed phase in and around cirrus clouds, and thus to identify in-cloud and out-of-cloud supersaturations with respect to ice. In November 2008 the newly developed balloon-borne backscatter sonde COBALD (Compact Optical Backscatter and AerosoL Detector) was flown 14 times together with a CFH (Cryogenic Frost point Hygrometer) from Lindenberg, Germany (52° N, 14° E). The case discussed here in detail shows two cirrus layers with in-cloud relative humidities with respect to ice between 50% and 130%. Global operational analysis data of ECMWF (roughly 1° × 1° horizontal and 1 km vertical resolution, 6-hourly stored fields) fail to represent ice water contents and relative humidities. Conversely, regional COSMO-7 forecasts (6.6 km × 6.6 km, 5-min stored fields) capture the measured humidities and cloud positions remarkably well. The main difference between ECMWF and COSMO data is the resolution of small-scale vertical features responsible for cirrus formation. Nevertheless, ice water contents in COSMO-7 are still off by factors 2-10, likely reflecting limitations in COSMO's ice phase bulk scheme. Significant improvements can be achieved by comprehensive size-resolved microphysical and optical modelling along backward trajectories based on COSMO-7 wind and temperature fields, which allow accurate computation of humidities, ice particle size distributions and backscatter ratios at the COBALD wavelengths. However, only by superimposing small-scale temperature fluctuations, which remain unresolved by the NWP models, can we obtain a satisfying agreement with the observations and reconcile the measured in-cloud non-equilibrium humidities with conventional ice cloud microphysics.

Extracting cross sections and water levels of vegetated ditches from LiDAR point clouds

NASA Astrophysics Data System (ADS)

Roelens, Jennifer; Dondeyne, Stefaan; Van Orshoven, Jos; Diels, Jan

2016-12-01

The hydrologic response of a catchment is sensitive to the morphology of the drainage network. Dimensions of bigger channels are usually well known, however, geometrical data for man-made ditches is often missing as there are many and small. Aerial LiDAR data offers the possibility to extract these small geometrical features. Analysing the three-dimensional point clouds directly will maintain the highest degree of information. A longitudinal and cross-sectional buffer were used to extract the cross-sectional profile points from the LiDAR point cloud. The profile was represented by spline functions fitted through the minimum envelop of the extracted points. The cross-sectional ditch profiles were classified for the presence of water and vegetation based on the normalized difference water index and the spatial characteristics of the points along the profile. The normalized difference water index was created using the RGB and intensity data coupled to the LiDAR points. The mean vertical deviation of 0.14 m found between the extracted and reference cross sections could mainly be attributed to the occurrence of water and partly to vegetation on the banks. In contrast to the cross-sectional area, the extracted width was not influenced by the environment (coefficient of determination R2 = 0.87). Water and vegetation influenced the extracted ditch characteristics, but the proposed method is still robust and therefore facilitates input data acquisition and improves accuracy of spatially explicit hydrological models.

Science in the cloud (SIC): A use case in MRI connectomics.

PubMed

Kiar, Gregory; Gorgolewski, Krzysztof J; Kleissas, Dean; Roncal, William Gray; Litt, Brian; Wandell, Brian; Poldrack, Russel A; Wiener, Martin; Vogelstein, R Jacob; Burns, Randal; Vogelstein, Joshua T

2017-05-01

Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called 'science in the cloud' (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended. © The Author 2017. Published by Oxford University Press.

Automatic registration of terrestrial point clouds based on panoramic reflectance images and efficient BaySAC

NASA Astrophysics Data System (ADS)

Kang, Zhizhong

2013-10-01

This paper presents a new approach to automatic registration of terrestrial laser scanning (TLS) point clouds utilizing a novel robust estimation method by an efficient BaySAC (BAYes SAmpling Consensus). The proposed method directly generates reflectance images from 3D point clouds, and then using SIFT algorithm extracts keypoints to identify corresponding image points. The 3D corresponding points, from which transformation parameters between point clouds are computed, are acquired by mapping the 2D ones onto the point cloud. To remove false accepted correspondences, we implement a conditional sampling method to select the n data points with the highest inlier probabilities as a hypothesis set and update the inlier probabilities of each data point using simplified Bayes' rule for the purpose of improving the computation efficiency. The prior probability is estimated by the verification of the distance invariance between correspondences. The proposed approach is tested on four data sets acquired by three different scanners. The results show that, comparing with the performance of RANSAC, BaySAC leads to less iterations and cheaper computation cost when the hypothesis set is contaminated with more outliers. The registration results also indicate that, the proposed algorithm can achieve high registration accuracy on all experimental datasets.

Analytical optical scattering in clouds

NASA Technical Reports Server (NTRS)

Phanord, Dieudonne D.

1989-01-01

An analytical optical model for scattering of light due to lightning by clouds of different geometry is being developed. The self-consistent approach and the equivalent medium concept of Twersky was used to treat the case corresponding to outside illumination. Thus, the resulting multiple scattering problem is transformed with the knowledge of the bulk parameters, into scattering by a single obstacle in isolation. Based on the size parameter of a typical water droplet as compared to the incident wave length, the problem for the single scatterer equivalent to the distribution of cloud particles can be solved either by Mie or Rayleigh scattering theory. The super computing code of Wiscombe can be used immediately to produce results that can be compared to the Monte Carlo computer simulation for outside incidence. A fairly reasonable inverse approach using the solution of the outside illumination case was proposed to model analytically the situation for point sources located inside the thick optical cloud. Its mathematical details are still being investigated. When finished, it will provide scientists an enhanced capability to study more realistic clouds. For testing purposes, the direct approach to the inside illumination of clouds by lightning is under consideration. Presently, an analytical solution for the cubic cloud will soon be obtained. For cylindrical or spherical clouds, preliminary results are needed for scattering by bounded obstacles above or below a penetrable surface interface.

Raster Vs. Point Cloud LiDAR Data Classification

NASA Astrophysics Data System (ADS)

El-Ashmawy, N.; Shaker, A.

2014-09-01

Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the classification results can be achieved by using the proposed approach.

Continuum Limit of Total Variation on Point Clouds

NASA Astrophysics Data System (ADS)

García Trillos, Nicolás; Slepčev, Dejan

2016-04-01

We consider point clouds obtained as random samples of a measure on a Euclidean domain. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. Our goal is to develop mathematical tools needed to study the consistency, as the number of available data points increases, of graph-based machine learning algorithms for tasks such as clustering. In particular, we study when the cut capacity, and more generally total variation, on these graphs is a good approximation of the perimeter (total variation) in the continuum setting. We address this question in the setting of Γ-convergence. We obtain almost optimal conditions on the scaling, as the number of points increases, of the size of the neighborhood over which the points are connected by an edge for the Γ-convergence to hold. Taking of the limit is enabled by a transportation based metric which allows us to suitably compare functionals defined on different point clouds.

Grammar-Supported 3d Indoor Reconstruction from Point Clouds for As-Built Bim

NASA Astrophysics Data System (ADS)

Becker, S.; Peter, M.; Fritsch, D.

2015-03-01

The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data - here 3D point clouds - collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

On the performance of metrics to predict quality in point cloud representations

NASA Astrophysics Data System (ADS)

Alexiou, Evangelos; Ebrahimi, Touradj

2017-09-01

Point clouds are a promising alternative for immersive representation of visual contents. Recently, an increased interest has been observed in the acquisition, processing and rendering of this modality. Although subjective and objective evaluations are critical in order to assess the visual quality of media content, they still remain open problems for point cloud representation. In this paper we focus our efforts on subjective quality assessment of point cloud geometry, subject to typical types of impairments such as noise corruption and compression-like distortions. In particular, we propose a subjective methodology that is closer to real-life scenarios of point cloud visualization. The performance of the state-of-the-art objective metrics is assessed by considering the subjective scores as the ground truth. Moreover, we investigate the impact of adopting different test methodologies by comparing them. Advantages and drawbacks of every approach are reported, based on statistical analysis. The results and conclusions of this work provide useful insights that could be considered in future experimentation.

Feature-based three-dimensional registration for repetitive geometry in machine vision

PubMed Central

Gong, Yuanzheng; Seibel, Eric J.

2016-01-01

As an important step in three-dimensional (3D) machine vision, 3D registration is a process of aligning two or multiple 3D point clouds that are collected from different perspectives together into a complete one. The most popular approach to register point clouds is to minimize the difference between these point clouds iteratively by Iterative Closest Point (ICP) algorithm. However, ICP does not work well for repetitive geometries. To solve this problem, a feature-based 3D registration algorithm is proposed to align the point clouds that are generated by vision-based 3D reconstruction. By utilizing texture information of the object and the robustness of image features, 3D correspondences can be retrieved so that the 3D registration of two point clouds is to solve a rigid transformation. The comparison of our method and different ICP algorithms demonstrates that our proposed algorithm is more accurate, efficient and robust for repetitive geometry registration. Moreover, this method can also be used to solve high depth uncertainty problem caused by little camera baseline in vision-based 3D reconstruction. PMID:28286703

Mapping with Small UAS: A Point Cloud Accuracy Assessment

NASA Astrophysics Data System (ADS)

Toth, Charles; Jozkow, Grzegorz; Grejner-Brzezinska, Dorota

2015-12-01

Interest in using inexpensive Unmanned Aerial System (UAS) technology for topographic mapping has recently significantly increased. Small UAS platforms equipped with consumer grade cameras can easily acquire high-resolution aerial imagery allowing for dense point cloud generation, followed by surface model creation and orthophoto production. In contrast to conventional airborne mapping systems, UAS has limited ground coverage due to low flying height and limited flying time, yet it offers an attractive alternative to high performance airborne systems, as the cost of the sensors and platform, and the flight logistics, is relatively low. In addition, UAS is better suited for small area data acquisitions and to acquire data in difficult to access areas, such as urban canyons or densely built-up environments. The main question with respect to the use of UAS is whether the inexpensive consumer sensors installed in UAS platforms can provide the geospatial data quality comparable to that provided by conventional systems. This study aims at the performance evaluation of the current practice of UAS-based topographic mapping by reviewing the practical aspects of sensor configuration, georeferencing and point cloud generation, including comparisons between sensor types and processing tools. The main objective is to provide accuracy characterization and practical information for selecting and using UAS solutions in general mapping applications. The analysis is based on statistical evaluation as well as visual examination of experimental data acquired by a Bergen octocopter with three different image sensor configurations, including a GoPro HERO3+ Black Edition, a Nikon D800 DSLR and a Velodyne HDL-32. In addition, georeferencing data of varying quality were acquired and evaluated. The optical imagery was processed by using three commercial point cloud generation tools. Comparing point clouds created by active and passive sensors by using different quality sensors, and finally, by different commercial software tools, provides essential information for the performance validation of UAS technology.

An Automatic Procedure for Combining Digital Images and Laser Scanner Data

NASA Astrophysics Data System (ADS)

Moussa, W.; Abdel-Wahab, M.; Fritsch, D.

2012-07-01

Besides improving both the geometry and the visual quality of the model, the integration of close-range photogrammetry and terrestrial laser scanning techniques directs at filling gaps in laser scanner point clouds to avoid modeling errors, reconstructing more details in higher resolution and recovering simple structures with less geometric details. Thus, within this paper a flexible approach for the automatic combination of digital images and laser scanner data is presented. Our approach comprises two methods for data fusion. The first method starts by a marker-free registration of digital images based on a point-based environment model (PEM) of a scene which stores the 3D laser scanner point clouds associated with intensity and RGB values. The PEM allows the extraction of accurate control information for the direct computation of absolute camera orientations with redundant information by means of accurate space resection methods. In order to use the computed relations between the digital images and the laser scanner data, an extended Helmert (seven-parameter) transformation is introduced and its parameters are estimated. Precedent to that, in the second method, the local relative orientation parameters of the camera images are calculated by means of an optimized Structure and Motion (SaM) reconstruction method. Then, using the determined transformation parameters results in having absolute oriented images in relation to the laser scanner data. With the resulting absolute orientations we have employed robust dense image reconstruction algorithms to create oriented dense image point clouds, which are automatically combined with the laser scanner data to form a complete detailed representation of a scene. Examples of different data sets are shown and experimental results demonstrate the effectiveness of the presented procedures.

A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments

Treesearch

Jeffrey S. Evans; Andrew T. Hudak

2007-01-01

One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground...

Evaluation of Rock Surface Characterization by Means of Temperature Distribution

NASA Astrophysics Data System (ADS)

Seker, D. Z.; Incekara, A. H.; Acar, A.; Kaya, S.; Bayram, B.; Sivri, N.

2017-12-01

Rocks have many different types which are formed over many years. Close range photogrammetry is a techniques widely used and preferred rather than other conventional methods. In this method, the photographs overlapping each other are the basic data source of the point cloud data which is the main data source for 3D model that provides analysts automation possibility. Due to irregular and complex structures of rocks, representation of their surfaces with a large number points is more effective. Color differences caused by weathering on the rock surfaces or naturally occurring make it possible to produce enough number of point clouds from the photographs. Objects such as small trees, shrubs and weeds on and around the surface also contribute to this. These differences and properties are important for efficient operation of pixel matching algorithms to generate adequate point cloud from photographs. In this study, possibilities of using temperature distribution for interpretation of roughness of rock surface which is one of the parameters representing the surface, was investigated. For the study, a small rock which is in size of 3 m x 1 m, located at ITU Ayazaga Campus was selected as study object. Two different methods were used. The first one is production of producing choropleth map by interpolation using temperature values of control points marked on object which were also used in 3D model. 3D object model was created with the help of terrestrial photographs and 12 control points marked on the object and coordinated. Temperature value of control points were measured by using infrared thermometer and used as basic data source in order to create choropleth map with interpolation. Temperature values range from 32 to 37.2 degrees. In the second method, 3D object model was produced by means of terrestrial thermal photographs. Fort this purpose, several terrestrial photographs were taken by thermal camera and 3D object model showing temperature distribution was created. The temperature distributions in both applications are almost identical in position. The areas on the rock surface that roughness values are higher than the surroundings can be clearly identified. When the temperature distributions produced by both methods are evaluated, it is observed that as the roughness on the surface increases, the temperature increases.

The Comparison of Point Data Models for the Output of WRF Hydro Model in the IDV

NASA Astrophysics Data System (ADS)

Ho, Y.; Weber, J.

2017-12-01

WRF Hydro netCDF output files contain streamflow, flow depth, longitude, latitude, altitude and stream order values for each forecast point. However, the data are not CF compliant. The total number of forecast points for the US CONUS is approximately 2.7 million and it is a big challenge for any visualization and analysis tool. The IDV point cloud display shows point data as a set of points colored by parameter. This display is very efficient compared to a standard point type display for rendering a large number of points. The one problem we have is that the data I/O can be a bottleneck issue when dealing with a large collection of point input files. In this presentation, we will experiment with different point data models and their APIs to access the same WRF Hydro model output. The results will help us construct a CF compliant netCDF point data format for the community.

a Point Cloud Classification Approach Based on Vertical Structures of Ground Objects

NASA Astrophysics Data System (ADS)

Zhao, Y.; Hu, Q.; Hu, W.

2018-04-01

This paper proposes a novel method for point cloud classification using vertical structural characteristics of ground objects. Since urbanization develops rapidly nowadays, urban ground objects also change frequently. Conventional photogrammetric methods cannot satisfy the requirements of updating the ground objects' information efficiently, so LiDAR (Light Detection and Ranging) technology is employed to accomplish this task. LiDAR data, namely point cloud data, can obtain detailed three-dimensional coordinates of ground objects, but this kind of data is discrete and unorganized. To accomplish ground objects classification with point cloud, we first construct horizontal grids and vertical layers to organize point cloud data, and then calculate vertical characteristics, including density and measures of dispersion, and form characteristic curves for each grids. With the help of PCA processing and K-means algorithm, we analyze the similarities and differences of characteristic curves. Curves that have similar features will be classified into the same class and point cloud correspond to these curves will be classified as well. The whole process is simple but effective, and this approach does not need assistance of other data sources. In this study, point cloud data are classified into three classes, which are vegetation, buildings, and roads. When horizontal grid spacing and vertical layer spacing are 3 m and 1 m respectively, vertical characteristic is set as density, and the number of dimensions after PCA processing is 11, the overall precision of classification result is about 86.31 %. The result can help us quickly understand the distribution of various ground objects.

Evaluation of Methods for Coregistration and Fusion of Rpas-Based 3d Point Clouds and Thermal Infrared Images

NASA Astrophysics Data System (ADS)

Hoegner, L.; Tuttas, S.; Xu, Y.; Eder, K.; Stilla, U.

2016-06-01

This paper discusses the automatic coregistration and fusion of 3d point clouds generated from aerial image sequences and corresponding thermal infrared (TIR) images. Both RGB and TIR images have been taken from a RPAS platform with a predefined flight path where every RGB image has a corresponding TIR image taken from the same position and with the same orientation with respect to the accuracy of the RPAS system and the inertial measurement unit. To remove remaining differences in the exterior orientation, different strategies for coregistering RGB and TIR images are discussed: (i) coregistration based on 2D line segments for every single TIR image and the corresponding RGB image. This method implies a mainly planar scene to avoid mismatches; (ii) coregistration of both the dense 3D point clouds from RGB images and from TIR images by coregistering 2D image projections of both point clouds; (iii) coregistration based on 2D line segments in every single TIR image and 3D line segments extracted from intersections of planes fitted in the segmented dense 3D point cloud; (iv) coregistration of both the dense 3D point clouds from RGB images and from TIR images using both ICP and an adapted version based on corresponding segmented planes; (v) coregistration of both image sets based on point features. The quality is measured by comparing the differences of the back projection of homologous points in both corrected RGB and TIR images.

Marine Cloud Brightening

DOE Office of Scientific and Technical Information (OSTI.GOV)

Latham, John; Bower, Keith; Choularton, Tom

2012-09-07

The idea behind the marine cloud-brightening (MCB) geoengineering technique is that seeding marine stratocumulus clouds with copious quantities of roughly monodisperse sub-micrometre sea water particles might significantly enhance the cloud droplet number concentration, and thereby the cloud albedo and possibly longevity. This would produce a cooling, which general circulation model (GCM) computations suggest could - subject to satisfactory resolution of technical and scientific problems identified herein - have the capacity to balance global warming up to the carbon dioxide-doubling point. We describe herein an account of our recent research on a number of critical issues associated with MCB. This involvesmore » (i) GCM studies, which are our primary tools for evaluating globally the effectiveness of MCB, and assessing its climate impacts on rainfall amounts and distribution, and also polar sea-ice cover and thickness; (ii) high-resolution modelling of the effects of seeding on marine stratocumulus, which are required to understand the complex array of interacting processes involved in cloud brightening; (iii) microphysical modelling sensitivity studies, examining the influence of seeding amount, seedparticle salt-mass, air-mass characteristics, updraught speed and other parameters on cloud-albedo change; (iv) sea water spray-production techniques; (v) computational fluid dynamics studies of possible large-scale periodicities in Flettner rotors; and (vi) the planning of a three-stage limited-area field research experiment, with the primary objectives of technology testing and determining to what extent, if any, cloud albedo might be enhanced by seeding marine stratocumulus clouds on a spatial scale of around 100 km. We stress that there would be no justification for deployment of MCB unless it was clearly established that no significant adverse consequences would result. There would also need to be an international agreement firmly in favour of such action.« less

Marine cloud brightening.

PubMed

Latham, John; Bower, Keith; Choularton, Tom; Coe, Hugh; Connolly, Paul; Cooper, Gary; Craft, Tim; Foster, Jack; Gadian, Alan; Galbraith, Lee; Iacovides, Hector; Johnston, David; Launder, Brian; Leslie, Brian; Meyer, John; Neukermans, Armand; Ormond, Bob; Parkes, Ben; Rasch, Phillip; Rush, John; Salter, Stephen; Stevenson, Tom; Wang, Hailong; Wang, Qin; Wood, Rob

2012-09-13

The idea behind the marine cloud-brightening (MCB) geoengineering technique is that seeding marine stratocumulus clouds with copious quantities of roughly monodisperse sub-micrometre sea water particles might significantly enhance the cloud droplet number concentration, and thereby the cloud albedo and possibly longevity. This would produce a cooling, which general circulation model (GCM) computations suggest could-subject to satisfactory resolution of technical and scientific problems identified herein-have the capacity to balance global warming up to the carbon dioxide-doubling point. We describe herein an account of our recent research on a number of critical issues associated with MCB. This involves (i) GCM studies, which are our primary tools for evaluating globally the effectiveness of MCB, and assessing its climate impacts on rainfall amounts and distribution, and also polar sea-ice cover and thickness; (ii) high-resolution modelling of the effects of seeding on marine stratocumulus, which are required to understand the complex array of interacting processes involved in cloud brightening; (iii) microphysical modelling sensitivity studies, examining the influence of seeding amount, seed-particle salt-mass, air-mass characteristics, updraught speed and other parameters on cloud-albedo change; (iv) sea water spray-production techniques; (v) computational fluid dynamics studies of possible large-scale periodicities in Flettner rotors; and (vi) the planning of a three-stage limited-area field research experiment, with the primary objectives of technology testing and determining to what extent, if any, cloud albedo might be enhanced by seeding marine stratocumulus clouds on a spatial scale of around 100×100 km. We stress that there would be no justification for deployment of MCB unless it was clearly established that no significant adverse consequences would result. There would also need to be an international agreement firmly in favour of such action.

Monte Carlo Radiative Transfer Modeling of Lightning Observed in Galileo Images of Jupiter

NASA Technical Reports Server (NTRS)

Dyudine, U. A.; Ingersoll, Andrew P.

2002-01-01

We study lightning on Jupiter and the clouds illuminated by the lightning using images taken by the Galileo orbiter. The Galileo images have a resolution of 25 km/pixel and axe able to resolve the shape of the single lightning spots in the images, which have full widths at half the maximum intensity in the range of 90-160 km. We compare the measured lightning flash images with simulated images produced by our ED Monte Carlo light-scattering model. The model calculates Monte Carlo scattering of photons in a ED opacity distribution. During each scattering event, light is partially absorbed. The new direction of the photon after scattering is chosen according to a Henyey-Greenstein phase function. An image from each direction is produced by accumulating photons emerging from the cloud in a small range (bins) of emission angles. Lightning bolts are modeled either as points or vertical lines. Our results suggest that some of the observed scattering patterns axe produced in a 3-D cloud rather than in a plane-parallel cloud layer. Lightning is estimated to occur at least as deep as the bottom of the expected water cloud. For the six cases studied, we find that the clouds above the lightning are optically thick (tau > 5). Jovian flashes are more regular and circular than the largest terrestrial flashes observed from space. On Jupiter there is nothing equivalent to the 30-40-km horizontal flashes which axe seen on Earth.

Mapping Urban Tree Canopy Cover Using Fused Airborne LIDAR and Satellite Imagery Data

NASA Astrophysics Data System (ADS)

Parmehr, Ebadat G.; Amati, Marco; Fraser, Clive S.

2016-06-01

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.

Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations.

PubMed

Yao, Lianbi; Wu, Hangbin; Li, Yayun; Meng, Bin; Qian, Jinfei; Liu, Chun; Fan, Hongchao

2017-04-11

A mobile mapping system (MMS) is usually utilized to collect environmental data on and around urban roads. Laser scanners and panoramic cameras are the main sensors of an MMS. This paper presents a new method for the registration of the point clouds and panoramic images based on sensor constellation. After the sensor constellation was analyzed, a feature point, the intersection of the connecting line between the global positioning system (GPS) antenna and the panoramic camera with a horizontal plane, was utilized to separate the point clouds into blocks. The blocks for the central and sideward laser scanners were extracted with the segmentation feature points. Then, the point clouds located in the blocks were separated from the original point clouds. Each point in the blocks was used to find the accurate corresponding pixel in the relative panoramic images via a collinear function, and the position and orientation relationship amongst different sensors. A search strategy is proposed for the correspondence of laser scanners and lenses of panoramic cameras to reduce calculation complexity and improve efficiency. Four cases of different urban road types were selected to verify the efficiency and accuracy of the proposed method. Results indicate that most of the point clouds (with an average of 99.7%) were successfully registered with the panoramic images with great efficiency. Geometric evaluation results indicate that horizontal accuracy was approximately 0.10-0.20 m, and vertical accuracy was approximately 0.01-0.02 m for all cases. Finally, the main factors that affect registration accuracy, including time synchronization amongst different sensors, system positioning and vehicle speed, are discussed.

Topographic Structure from Motion

NASA Astrophysics Data System (ADS)

Fonstad, M. A.; Dietrich, J. T.; Courville, B. C.; Jensen, J.; Carbonneau, P.

2011-12-01

The production of high-resolution topographic datasets is of increasing concern and application throughout the geomorphic sciences, and river science is no exception. Consequently, a wide range of topographic measurement methods have evolved. Despite the range of available methods, the production of high resolution, high quality digital elevation models (DEMs) generally requires a significant investment in personnel time, hardware and/or software. However, image-based methods such as digital photogrammetry have steadily been decreasing in costs. Initially developed for the purpose of rapid, inexpensive and easy three dimensional surveys of buildings or small objects, the "structure from motion" photogrammetric approach (SfM) is a purely image based method which could deliver a step-change if transferred to river remote sensing, and requires very little training and is extremely inexpensive. Using the online SfM program Microsoft Photosynth, we have created high-resolution digital elevation models (DEM) of rivers from ordinary photographs produced from a multi-step workflow that takes advantage of free and open source software. This process reconstructs real world scenes from SfM algorithms based on the derived positions of the photographs in three-dimensional space. One of the products of the SfM process is a three-dimensional point cloud of features present in the input photographs. This point cloud can be georeferenced from a small number of ground control points collected via GPS in the field. The georeferenced point cloud can then be used to create a variety of digital elevation model products. Among several study sites, we examine the applicability of SfM in the Pedernales River in Texas (USA), where several hundred images taken from a hand-held helikite are used to produce DEMs of the fluvial topographic environment. This test shows that SfM and low-altitude platforms can produce point clouds with point densities considerably better than airborne LiDAR, with horizontal and vertical precision in the centimeter range, and with very low capital and labor costs and low expertise levels. Advanced structure from motion software (such as Bundler and OpenSynther) are currently under development and should increase the density of topographic points rivaling those of terrestrial laser scanning when using images shot from low altitude platforms such as helikites, poles, remote-controlled aircraft and rotocraft, and low-flying manned aircraft. Clearly, the development of this set of inexpensive and low-required-expertise tools has the potential to fundamentally shift the production of digital fluvial topography from a capital-intensive enterprise of a low number of researchers to a low-cost exercise of many river researchers.

The monitoring and managing application of cloud computing based on Internet of Things.

PubMed

Luo, Shiliang; Ren, Bin

2016-07-01

Cloud computing and the Internet of Things are the two hot points in the Internet application field. The application of the two new technologies is in hot discussion and research, but quite less on the field of medical monitoring and managing application. Thus, in this paper, we study and analyze the application of cloud computing and the Internet of Things on the medical field. And we manage to make a combination of the two techniques in the medical monitoring and managing field. The model architecture for remote monitoring cloud platform of healthcare information (RMCPHI) was established firstly. Then the RMCPHI architecture was analyzed. Finally an efficient PSOSAA algorithm was proposed for the medical monitoring and managing application of cloud computing. Simulation results showed that our proposed scheme can improve the efficiency about 50%. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Remote Sensing of Cloud Top Heights Using the Research Scanning Polarimeter

NASA Technical Reports Server (NTRS)

Sinclair, Kenneth; van Diedenhoven, Bastiaan; Cairns, Brian; Yorks, John; Wasilewski, Andrzej

2015-01-01

Clouds cover roughly two thirds of the globe and act as an important regulator of Earth's radiation budget. Of these, multilayered clouds occur about half of the time and are predominantly two-layered. Changes in cloud top height (CTH) have been predicted by models to have a globally averaged positive feedback, however observational changes in CTH have shown uncertain results. Additional CTH observations are necessary to better and quantify the effect. Improved CTH observations will also allow for improved sub-grid parameterizations in large-scale models and accurate CTH information is important when studying variations in freezing point and cloud microphysics. NASA's airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. RSP scans along the aircraft track and obtains measurements at 152 viewing angles at any aircraft location. The approach presented here aggregates measurements from multiple scans to a single location at cloud altitude using a correlation function designed to identify the location-distinct features in each scan. During NASAs SEAC4RS air campaign, the RSP was mounted on the ER-2 aircraft along with the Cloud Physics Lidar (CPL), which made simultaneous measurements of CTH. The RSPs unique method of determining CTH is presented. The capabilities of using single and combinations of channels within the approach are investigated. A detailed comparison of RSP retrieved CTHs with those of CPL reveal the accuracy of the approach. Results indicate a strong ability for the RSP to accurately identify cloud heights. Interestingly, the analysis reveals an ability for the approach to identify multiple cloud layers in a single scene and estimate the CTH of each layer. Capabilities and limitations of identifying single and multiple cloud layers heights are explored. Special focus is given to sources of error in the method including optically thin clouds, physically thick clouds, multi-layered clouds as well as cloud phase. When determining multi-layered CTHs, limits on the upper clouds opacity are assessed.

Using deep recurrent neural network for direct beam solar irradiance cloud screening

NASA Astrophysics Data System (ADS)

Chen, Maosi; Davis, John M.; Liu, Chaoshun; Sun, Zhibin; Zempila, Melina Maria; Gao, Wei

2017-09-01

Cloud screening is an essential procedure for in-situ calibration and atmospheric properties retrieval on (UV-)MultiFilter Rotating Shadowband Radiometer [(UV-)MFRSR]. Previous study has explored a cloud screening algorithm for direct-beam (UV-)MFRSR voltage measurements based on the stability assumption on a long time period (typically a half day or a whole day). To design such an algorithm requires in-depth understanding of radiative transfer and delicate data manipulation. Recent rapid developments on deep neural network and computation hardware have opened a window for modeling complicated End-to-End systems with a standardized strategy. In this study, a multi-layer dynamic bidirectional recurrent neural network is built for determining the cloudiness on each time point with a 17-year training dataset and tested with another 1-year dataset. The dataset is the daily 3-minute cosine corrected voltages, airmasses, and the corresponding cloud/clear-sky labels at two stations of the USDA UV-B Monitoring and Research Program. The results show that the optimized neural network model (3-layer, 250 hidden units, and 80 epochs of training) has an overall test accuracy of 97.87% (97.56% for the Oklahoma site and 98.16% for the Hawaii site). Generally, the neural network model grasps the key concept of the original model to use data in the entire day rather than short nearby measurements to perform cloud screening. A scrutiny of the logits layer suggests that the neural network model automatically learns a way to calculate a quantity similar to total optical depth and finds an appropriate threshold for cloud screening.

Derivation of Cloud Heating Rate Profiles using observations of Mixed-Phase Arctic Clouds: Impacts of Solar Zenith Angle

NASA Astrophysics Data System (ADS)

Zhang, G.; McFarquhar, G.; Poellot, M.; Verlinde, J.; Heymsfield, A.; Kok, G.

2005-12-01

Arctic stratus clouds play an important role in the energy balance of the Arctic region. Previous studies have suggested that Arctic stratus persist due to a balance among cloud top radiation cooling, latent heating, ice crystal fall out and large scale forcing. In this study, radiative heating profiles through Arctic stratus are computed using cloud, surface and thermodynamic observations obtained during the Mixed-Phase Arctic Cloud Experiment (M-PACE) as input to the radiative transfer model STREAMER. In particular, microphysical and macrophycial cloud properties such as phase, water content, effective particle size, particle shape, cloud height and cloud thickness were derived using data collected by in-situ sensors on the University of North Dakota (UND) Citation and ground-based remote sensors at Barrow and Oliktok Point. Temperature profiles were derived from radiosonde launches and a fresh snow surface was assumed. One series of sensitivity studies explored the dependence of the heating profile on the solar zenith angle. For smaller solar zenith angles, more incoming solar radiation is received at cloud top acting to counterbalance infrared cooling. As solar zenith angle in the Arctic is large compared to low latitudes, a large solar zenith angle may contribute to the longevity of these clouds.

Simple Models of the Spatial Distribution of Cloud Radiative Properties for Remote Sensing Studies

NASA Technical Reports Server (NTRS)

2004-01-01

This project aimed to assess the degree to which estimates of three-dimensional cloud structure can be inferred from a time series of profiles obtained at a point. The work was motivated by the desire to understand the extent to which high-frequency profiles of the atmosphere (e.g. ARM data streams) can be used to assess the magnitude of non-plane parallel transfer of radiation in thc atmosphere. We accomplished this by performing an observing system simulation using a large-eddy simulation and a Monte Carlo radiative transfer model. We define the 3D effect as the part of the radiative transfer that isn't captured by one-dimensional radiative transfer calculations. We assess the magnitude of the 3D effect in small cumulus clouds by using a fine-scale cloud model to simulate many hours of cloudiness over a continental site. We then use a Monte Carlo radiative transfer model to compute the broadband shortwave fluxes at the surface twice, once using the complete three-dimensional radiative transfer F(sup 3D), and once using the ICA F (sup ICA); the difference between them is the 3D effect given.

Cotton growth modeling and assessment using UAS visual-band imagery

USDA-ARS?s Scientific Manuscript database

This paper explores the potential of using unmanned aircraft system (UAS)-based visible-band images to assess cotton growth. By applying the structure-from-motion algorithm, cotton plant height (ph) and canopy cover (cc) were retrieved from the point cloud-based digital surface models (DSMs) and ort...

Massive superclusters as a probe of the nature and amplitude of primordial density fluctuations

NASA Technical Reports Server (NTRS)

Kaiser, N.; Davis, M.

1985-01-01

It is pointed out that correlation studies of galaxy positions have been widely used in the search for information about the large-scale matter distribution. The study of rare condensations on large scales provides an approach to extend the existing knowledge of large-scale structure into the weakly clustered regime. Shane (1975) provides a description of several apparent massive condensations within the Shane-Wirtanen catalog, taking into account the Serpens-Virgo cloud and the Corona cloud. In the present study, a description is given of a model for estimating the frequency of condensations which evolve from initially Gaussian fluctuations. This model is applied to the Corona cloud to estimate its 'rareness' and thereby estimate the rms density contrast on this mass scale. An attempt is made to find a conflict between the density fluctuations derived from the Corona cloud and independent constraints. A comparison is conducted of the estimate and the density fluctuations predicted to arise in a universe dominated by cold dark matter.

Impact of Albedo Contrast Between Cirrus and Boundary-Layer Clouds on Climate Sensitivity

NASA Technical Reports Server (NTRS)

Chou, Ming-Dah; Lindzen, R. S.; Hou, A. Y.; Lau, William K. M. (Technical Monitor)

2001-01-01

In assessing the iris effect suggested by Lindzen et al. (2001), Fu et al. (2001) found that the response of high-level clouds to the sea surface temperature had an effect of reducing the climate sensitivity to external radiative forcing, but the effect was not as strong as LCH found. This weaker reduction in climate sensitivity was due to the smaller contrasts in albedos and effective emitting temperatures between cirrus clouds and the neighboring regions. FBH specified the albedos and the outgoing longwave radiation (OLR) in the LCH 3.5-box radiative-convective model by requiring that the model radiation budgets at the top of the atmosphere be consistent with that inferred from the Earth Radiation Budget Experiment (ERBE). In point of fact, the constraint by radiation budgets alone is not sufficient for deriving the correct contrast in radiation properties between cirrus clouds and the neighboring regions, and the approach of FBH to specifying those properties is, we feel inappropriate for assessing the iris effect.

Advanced Visualization and Interactive Display Rapid Innovation and Discovery Evaluation Research (VISRIDER) Program Task 6: Point Cloud Visualization Techniques for Desktop and Web Platforms

DTIC Science & Technology

2017-04-01

ADVANCED VISUALIZATION AND INTERACTIVE DISPLAY RAPID INNOVATION AND DISCOVERY EVALUATION RESEARCH (VISRIDER) PROGRAM TASK 6: POINT CLOUD...To) OCT 2013 – SEP 2014 4. TITLE AND SUBTITLE ADVANCED VISUALIZATION AND INTERACTIVE DISPLAY RAPID INNOVATION AND DISCOVERY EVALUATION RESEARCH...various point cloud visualization techniques for viewing large scale LiDAR datasets. Evaluate their potential use for thick client desktop platforms

Inventory of File WAFS_blended_2014102006f06.grib2

Science.gov Websites

) [%] 004 700 mb CTP 6 hour fcst In-Cloud Turbulence [%] spatial ave,code table 4.15=3,#points=1 005 700 mb CTP 6 hour fcst In-Cloud Turbulence [%] spatial max,code table 4.15=3,#points=1 006 600 mb CTP 6 hour fcst In-Cloud Turbulence [%] spatial ave,code table 4.15=3,#points=1 007 600 mb CTP 6 hour fcst In

An analysis of the relationship between cloud anomalies and sea surface temperature anomalies in a global circulation model

NASA Technical Reports Server (NTRS)

Peterson, Thomas C.; Barnett, Tim P.; Roeckner, Erich; Vonder Haar, Thomas H.

1992-01-01

The relationship between the sea surface temperature anomalies (SSTAs) and the anomalies of the monthly mean cloud cover (including the high-level, low-level, and total cloud cover), the outgoing longwave radiation, and the reflected solar radiation was analyzed using a least absolute deviations regression at each grid point over the open ocean for a 6-yr period. The results indicate that cloud change in association with a local 1-C increase in SSTAs cannot be used to predict clouds in a potential future world where all the oceans are 1-C warmer than at present, because much of the observed cloud changes are due to circulation changes, which in turn are related not only to changes in SSTAs but to changes in SSTA gradients. However, because SSTAs are associated with changes in the local ocean-atmosphere moisture and heat fluxes as well as significant changes in circulation (such as ENSO), SSTAs can serve as a surrogate for many aspects of global climate change.

A study of an expanding interplanatary magnetic cloud and its interaction with the earth's magnetosphere - The interplanetary aspect

NASA Technical Reports Server (NTRS)

Farrugia, C. J.; Burlaga, L. F.; Osherovich, V. A.; Richardson, I. G.; Freeman, M. P.; Lepping, R. P.; Lazarus, A. J.

1993-01-01

High time resolution interplanetary magnetic field and plasma measurements of an interplanetary magnetic cloud and its interaction with the earth's magnetosphere on January 14/15, 1988 are interpreted and discussed. It is argued that the data are consistent with the theoretical model of magnetic clouds as flux ropes of local straight cylindrical geometry. The data also suggest that this cloud is aligned with its axis in the ecliptic plane and pointing in the east-west direction. Evidence consisting of the intensity and directional distribution of energetic particle in the magnetic cloud argues in favor of the connectedness of the magnetic field lines to the sun's surface. The intensities of about 0.5 MeV ions is rapidly enhanced and the particles stream in a collimated beam along the magnetic field preferentially from the west of the sun. The particles travel form a flare site along the cloud magnetic field lines, which are thus presumably still attached to the sun.

3-D Object Recognition from Point Cloud Data

NASA Astrophysics Data System (ADS)

Smith, W.; Walker, A. S.; Zhang, B.

2011-09-01

The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs). Massively parallel processes such as graphics processing unit (GPU) computing make real-time 3-D object recognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D object recognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM) and digital elevation model (DEM), so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex roofs. Several case studies have been conducted using a variety of point densities, terrain types and building densities. The results have been encouraging. More work is required for better processing of, for example, forested areas, buildings with sides that are not at right angles or are not straight, and single trees that impinge on buildings. Further work may also be required to ensure that the buildings extracted are of fully cartographic quality. A first version will be included in production software later in 2011. In addition to the standard geospatial applications and the UAV navigation, the results have a further advantage: since LiDAR data tends to be accurately georeferenced, the building models extracted can be used to refine image metadata whenever the same buildings appear in imagery for which the GPS/IMU values are poorer than those for the LiDAR.

Observations of the boundary layer, cloud, and aerosol variability in the southeast Pacific near-coastal marine stratocumulus during VOCALS-REx

NASA Astrophysics Data System (ADS)

Zheng, X.; Albrecht, B.; Jonsson, H. H.; Khelif, D.; Feingold, G.; Minnis, P.; Ayers, K.; Chuang, P.; Donaher, S.; Rossiter, D.; Ghate, V.; Ruiz-Plancarte, J.; Sun-Mack, S.

2011-09-01

Aircraft observations made off the coast of northern Chile in the Southeastern Pacific (20° S, 72° W; named Point Alpha) from 16 October to 13 November 2008 during the VAMOS Ocean-Cloud- Atmosphere-Land Study-Regional Experiment (VOCALS-REx), combined with meteorological reanalysis, satellite measurements, and radiosonde data, are used to investigate the boundary layer (BL) and aerosol-cloud-drizzle variations in this region. On days without predominately synoptic and meso-scale influences, the BL at Point Alpha was typical of a non-drizzling stratocumulus-topped BL. Entrainment rates calculated from the near cloud-top fluxes and turbulence in the BL at Point Alpha appeared to be weaker than those in the BL over the open ocean west of Point Alpha and the BL near the coast of the northeast Pacific. The cloud liquid water path (LWP) varied between 15 g m-2 and 160 g m-2. The BL had a depth of 1140 ± 120 m, was generally well-mixed and capped by a sharp inversion without predominately synoptic and meso-scale influences. The wind direction generally switched from southerly within the BL to northerly above the inversion. On days when a synoptic system and related mesoscale costal circulations affected conditions at Point Alpha (29 October-4 November), a moist layer above the inversion moved over Point Alpha, and the total-water mixing ratio above the inversion was larger than that within the BL. The accumulation mode aerosol varied from 250 to 700 cm-3 within the BL, and CCN at 0.2 % supersaturation within the BL ranged between 150 and 550 cm-3. The main aerosol source at Point Alpha was horizontal advection within the BL from south. The average cloud droplet number concentration ranged between 80 and 400 cm-3. While the mean LWP retrieved from GOES was in good agreement with the in situ measurements, the GOES-derived cloud droplet effective radius tended to be larger than that from the aircraft in situ observations near cloud top. The aerosol and cloud LWP relationship reveals that during the typical well-mixed BL days the cloud LWP increased with the CCN concentrations. On the other hand, meteorological factors and the decoupling processes have large influences on the cloud LWP variation as well.

Imaging Systems for Size Measurements of Debrisat Fragments

NASA Technical Reports Server (NTRS)

Shiotani, B.; Scruggs, T.; Toledo, R.; Fitz-Coy, N.; Liou, J. C.; Sorge, M.; Huynh, T.; Opiela, J.; Krisko, P.; Cowardin, H.

2017-01-01

The overall objective of the DebriSat project is to provide data to update existing standard spacecraft breakup models. One of the key sets of parameters used in these models is the physical dimensions of the fragments (i.e., length, average-cross sectional area, and volume). For the DebriSat project, only fragments with at least one dimension greater than 2 mm are collected and processed. Additionally, a significant portion of the fragments recovered from the impact test are needle-like and/or flat plate-like fragments where their heights are almost negligible in comparison to their other dimensions. As a result, two fragment size categories were defined: 2D objects and 3D objects. While measurement systems are commercially available, factors such as measurement rates, system adaptability, size characterization limitations and equipment costs presented significant challenges to the project and a decision was made to develop our own size characterization systems. The size characterization systems consist of two automated image systems, one referred to as the 3D imaging system and the other as the 2D imaging system. Which imaging system to use depends on the classification of the fragment being measured. Both imaging systems utilize point-and-shoot cameras for object image acquisition and create representative point clouds of the fragments. The 3D imaging system utilizes a space-carving algorithm to generate a 3D point cloud, while the 2D imaging system utilizes an edge detection algorithm to generate a 2D point cloud. From the point clouds, the three largest orthogonal dimensions are determined using a convex hull algorithm. For 3D objects, in addition to the three largest orthogonal dimensions, the volume is computed via an alpha-shape algorithm applied to the point clouds. The average cross-sectional area is also computed for 3D objects. Both imaging systems have automated size measurements (image acquisition and image processing) driven by the need to quickly and accurately measure tens of thousands of debris fragments. Moreover, the automated size measurement reduces potential fragment damage/mishandling and ability for accuracy and repeatability. As the fragment characterization progressed, it became evident that the imaging systems had to be revised. For example, an additional view was added to the 2D imaging system to capture the height of the 2D object. This paper presents the DebriSat project's imaging systems and calculation techniques in detail; from design and development to maturation. The experiences and challenges are also shared.

Topobathymetric LiDAR point cloud processing and landform classification in a tidal environment

NASA Astrophysics Data System (ADS)

Skovgaard Andersen, Mikkel; Al-Hamdani, Zyad; Steinbacher, Frank; Rolighed Larsen, Laurids; Brandbyge Ernstsen, Verner

2017-04-01

Historically it has been difficult to create high resolution Digital Elevation Models (DEMs) in land-water transition zones due to shallow water depth and often challenging environmental conditions. This gap of information has been reflected as a "white ribbon" with no data in the land-water transition zone. In recent years, the technology of airborne topobathymetric Light Detection and Ranging (LiDAR) has proven capable of filling out the gap by simultaneously capturing topographic and bathymetric elevation information, using only a single green laser. We collected green LiDAR point cloud data in the Knudedyb tidal inlet system in the Danish Wadden Sea in spring 2014. Creating a DEM from a point cloud requires the general processing steps of data filtering, water surface detection and refraction correction. However, there is no transparent and reproducible method for processing green LiDAR data into a DEM, specifically regarding the procedure of water surface detection and modelling. We developed a step-by-step procedure for creating a DEM from raw green LiDAR point cloud data, including a procedure for making a Digital Water Surface Model (DWSM) (see Andersen et al., 2017). Two different classification analyses were applied to the high resolution DEM: A geomorphometric and a morphological classification, respectively. The classification methods were originally developed for a small test area; but in this work, we have used the classification methods to classify the complete Knudedyb tidal inlet system. References Andersen MS, Gergely Á, Al-Hamdani Z, Steinbacher F, Larsen LR, Ernstsen VB (2017). Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment. Hydrol. Earth Syst. Sci., 21: 43-63, doi:10.5194/hess-21-43-2017. Acknowledgements This work was funded by the Danish Council for Independent Research | Natural Sciences through the project "Process-based understanding and prediction of morphodynamics in a natural coastal system in response to climate change" (Steno Grant no. 10-081102) and by the Geocenter Denmark through the project "Closing the gap! - Coherent land-water environmental mapping (LAWA)" (Grant no. 4-2015).

Accurate documentation in cultural heritage by merging TLS and high-resolution photogrammetric data

NASA Astrophysics Data System (ADS)

Grussenmeyer, Pierre; Alby, Emmanuel; Assali, Pierre; Poitevin, Valentin; Hullo, Jean-François; Smigiel, Eddie

2011-07-01

Several recording techniques are used together in Cultural Heritage Documentation projects. The main purpose of the documentation and conservation works is usually to generate geometric and photorealistic 3D models for both accurate reconstruction and visualization purposes. The recording approach discussed in this paper is based on the combination of photogrammetric dense matching and Terrestrial Laser Scanning (TLS) techniques. Both techniques have pros and cons, and criteria as geometry, texture, accuracy, resolution, recording and processing time are often compared. TLS techniques (time of flight or phase shift systems) are often used for the recording of large and complex objects or sites. Point cloud generation from images by dense stereo or multi-image matching can be used as an alternative or a complementary method to TLS. Compared to TLS, the photogrammetric solution is a low cost one as the acquisition system is limited to a digital camera and a few accessories only. Indeed, the stereo matching process offers a cheap, flexible and accurate solution to get 3D point clouds and textured models. The calibration of the camera allows the processing of distortion free images, accurate orientation of the images, and matching at the subpixel level. The main advantage of this photogrammetric methodology is to get at the same time a point cloud (the resolution depends on the size of the pixel on the object), and therefore an accurate meshed object with its texture. After the matching and processing steps, we can use the resulting data in much the same way as a TLS point cloud, but with really better raster information for textures. The paper will address the automation of recording and processing steps, the assessment of the results, and the deliverables (e.g. PDF-3D files). Visualization aspects of the final 3D models are presented. Two case studies with merged photogrammetric and TLS data are finally presented: - The Gallo-roman Theatre of Mandeure, France); - The Medieval Fortress of Châtel-sur-Moselle, France), where a network of underground galleries and vaults has been recorded.

First Steps to Automated Interior Reconstruction from Semantically Enriched Point Clouds and Imagery

NASA Astrophysics Data System (ADS)

Obrock, L. S.; Gülch, E.

2018-05-01

The automated generation of a BIM-Model from sensor data is a huge challenge for the modeling of existing buildings. Currently the measurements and analyses are time consuming, allow little automation and require expensive equipment. We do lack an automated acquisition of semantical information of objects in a building. We are presenting first results of our approach based on imagery and derived products aiming at a more automated modeling of interior for a BIM building model. We examine the building parts and objects visible in the collected images using Deep Learning Methods based on Convolutional Neural Networks. For localization and classification of building parts we apply the FCN8s-Model for pixel-wise Semantic Segmentation. We, so far, reach a Pixel Accuracy of 77.2 % and a mean Intersection over Union of 44.2 %. We finally use the network for further reasoning on the images of the interior room. We combine the segmented images with the original images and use photogrammetric methods to produce a three-dimensional point cloud. We code the extracted object types as colours of the 3D-points. We thus are able to uniquely classify the points in three-dimensional space. We preliminary investigate a simple extraction method for colour and material of building parts. It is shown, that the combined images are very well suited to further extract more semantic information for the BIM-Model. With the presented methods we see a sound basis for further automation of acquisition and modeling of semantic and geometric information of interior rooms for a BIM-Model.

A continuous surface reconstruction method on point cloud captured from a 3D surface photogrammetry system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Wenyang; Cheung, Yam; Sabouri, Pouya

2015-11-15

Purpose: To accurately and efficiently reconstruct a continuous surface from noisy point clouds captured by a surface photogrammetry system (VisionRT). Methods: The authors have developed a level-set based surface reconstruction method on point clouds captured by a surface photogrammetry system (VisionRT). The proposed method reconstructs an implicit and continuous representation of the underlying patient surface by optimizing a regularized fitting energy, offering extra robustness to noise and missing measurements. By contrast to explicit/discrete meshing-type schemes, their continuous representation is particularly advantageous for subsequent surface registration and motion tracking by eliminating the need for maintaining explicit point correspondences as in discretemore » models. The authors solve the proposed method with an efficient narrowband evolving scheme. The authors evaluated the proposed method on both phantom and human subject data with two sets of complementary experiments. In the first set of experiment, the authors generated a series of surfaces each with different black patches placed on one chest phantom. The resulting VisionRT measurements from the patched area had different degree of noise and missing levels, since VisionRT has difficulties in detecting dark surfaces. The authors applied the proposed method to point clouds acquired under these different configurations, and quantitatively evaluated reconstructed surfaces by comparing against a high-quality reference surface with respect to root mean squared error (RMSE). In the second set of experiment, the authors applied their method to 100 clinical point clouds acquired from one human subject. In the absence of ground-truth, the authors qualitatively validated reconstructed surfaces by comparing the local geometry, specifically mean curvature distributions, against that of the surface extracted from a high-quality CT obtained from the same patient. Results: On phantom point clouds, their method achieved submillimeter reconstruction RMSE under different configurations, demonstrating quantitatively the faith of the proposed method in preserving local structural properties of the underlying surface in the presence of noise and missing measurements, and its robustness toward variations of such characteristics. On point clouds from the human subject, the proposed method successfully reconstructed all patient surfaces, filling regions where raw point coordinate readings were missing. Within two comparable regions of interest in the chest area, similar mean curvature distributions were acquired from both their reconstructed surface and CT surface, with mean and standard deviation of (μ{sub recon} = − 2.7 × 10{sup −3} mm{sup −1}, σ{sub recon} = 7.0 × 10{sup −3} mm{sup −1}) and (μ{sub CT} = − 2.5 × 10{sup −3} mm{sup −1}, σ{sub CT} = 5.3 × 10{sup −3} mm{sup −1}), respectively. The agreement of local geometry properties between the reconstructed surfaces and the CT surface demonstrated the ability of the proposed method in faithfully representing the underlying patient surface. Conclusions: The authors have integrated and developed an accurate level-set based continuous surface reconstruction method on point clouds acquired by a 3D surface photogrammetry system. The proposed method has generated a continuous representation of the underlying phantom and patient surfaces with good robustness against noise and missing measurements. It serves as an important first step for further development of motion tracking methods during radiotherapy.« less

A continuous surface reconstruction method on point cloud captured from a 3D surface photogrammetry system

PubMed Central

Liu, Wenyang; Cheung, Yam; Sabouri, Pouya; Arai, Tatsuya J.; Sawant, Amit; Ruan, Dan

2015-01-01

Purpose: To accurately and efficiently reconstruct a continuous surface from noisy point clouds captured by a surface photogrammetry system (VisionRT). Methods: The authors have developed a level-set based surface reconstruction method on point clouds captured by a surface photogrammetry system (VisionRT). The proposed method reconstructs an implicit and continuous representation of the underlying patient surface by optimizing a regularized fitting energy, offering extra robustness to noise and missing measurements. By contrast to explicit/discrete meshing-type schemes, their continuous representation is particularly advantageous for subsequent surface registration and motion tracking by eliminating the need for maintaining explicit point correspondences as in discrete models. The authors solve the proposed method with an efficient narrowband evolving scheme. The authors evaluated the proposed method on both phantom and human subject data with two sets of complementary experiments. In the first set of experiment, the authors generated a series of surfaces each with different black patches placed on one chest phantom. The resulting VisionRT measurements from the patched area had different degree of noise and missing levels, since VisionRT has difficulties in detecting dark surfaces. The authors applied the proposed method to point clouds acquired under these different configurations, and quantitatively evaluated reconstructed surfaces by comparing against a high-quality reference surface with respect to root mean squared error (RMSE). In the second set of experiment, the authors applied their method to 100 clinical point clouds acquired from one human subject. In the absence of ground-truth, the authors qualitatively validated reconstructed surfaces by comparing the local geometry, specifically mean curvature distributions, against that of the surface extracted from a high-quality CT obtained from the same patient. Results: On phantom point clouds, their method achieved submillimeter reconstruction RMSE under different configurations, demonstrating quantitatively the faith of the proposed method in preserving local structural properties of the underlying surface in the presence of noise and missing measurements, and its robustness toward variations of such characteristics. On point clouds from the human subject, the proposed method successfully reconstructed all patient surfaces, filling regions where raw point coordinate readings were missing. Within two comparable regions of interest in the chest area, similar mean curvature distributions were acquired from both their reconstructed surface and CT surface, with mean and standard deviation of (μrecon = − 2.7 × 10−3 mm−1, σrecon = 7.0 × 10−3 mm−1) and (μCT = − 2.5 × 10−3 mm−1, σCT = 5.3 × 10−3 mm−1), respectively. The agreement of local geometry properties between the reconstructed surfaces and the CT surface demonstrated the ability of the proposed method in faithfully representing the underlying patient surface. Conclusions: The authors have integrated and developed an accurate level-set based continuous surface reconstruction method on point clouds acquired by a 3D surface photogrammetry system. The proposed method has generated a continuous representation of the underlying phantom and patient surfaces with good robustness against noise and missing measurements. It serves as an important first step for further development of motion tracking methods during radiotherapy. PMID:26520747

Using Lunar Observations to Validate In-Flight Calibrations of Clouds and Earth Radiant Energy System Instruments

NASA Technical Reports Server (NTRS)

Daniels, Janet L.; Smith, G. Louis; Priestley, Kory J.; Thomas, Susan

2014-01-01

The validation of in-orbit instrument performance requires stability in both instrument and calibration source. This paper describes a method of validation using lunar observations scanning near full moon by the Clouds and Earth Radiant Energy System (CERES) instruments. Unlike internal calibrations, the Moon offers an external source whose signal variance is predictable and non-degrading. From 2006 to present, in-orbit observations have become standardized and compiled for the Flight Models-1 and -2 aboard the Terra satellite, for Flight Models-3 and -4 aboard the Aqua satellite, and beginning 2012, for Flight Model-5 aboard Suomi-NPP. Instrument performance parameters which can be gleaned are detector gain, pointing accuracy and static detector point response function validation. Lunar observations are used to examine the stability of all three detectors on each of these instruments from 2006 to present. This validation method has yielded results showing trends per CERES data channel of 1.2% per decade or less.

Comparison of effects of different screw materials in the triangle fixation of femoral neck fractures.

PubMed

Gok, Kadir; Inal, Sermet; Gok, Arif; Gulbandilar, Eyyup

2017-05-01

In this study, biomechanical behaviors of three different screw materials (stainless steel, titanium and cobalt-chromium) have analyzed to fix with triangle fixation under axial loading in femoral neck fracture and which material is best has been investigated. Point cloud obtained after scanning the human femoral model with the three dimensional (3D) scanner and this point cloud has been converted to 3D femoral model by Geomagic Studio software. Femoral neck fracture was modeled by SolidWorks software for only triangle configuration and computer-aided numerical analyses of three different materials have been carried out by AnsysWorkbench finite element analysis (FEA) software. The loading, boundary conditions and material properties have prepared for FEA and Von-Misses stress values on upper and lower proximity of the femur and screws have been calculated. At the end of numerical analyses, the best advantageous screw material has calculated as titanium because it creates minimum stress at the upper and lower proximity of the fracture line.

A new mosaic method for three-dimensional surface

NASA Astrophysics Data System (ADS)

Yuan, Yun; Zhu, Zhaokun; Ding, Yongjun

2011-08-01

Three-dimensional (3-D) data mosaic is a indispensable link in surface measurement and digital terrain map generation. With respect to the mosaic problem of the local unorganized cloud points with rude registration and mass mismatched points, a new mosaic method for 3-D surface based on RANSAC is proposed. Every circular of this method is processed sequentially by random sample with additional shape constraint, data normalization of cloud points, absolute orientation, data denormalization of cloud points, inlier number statistic, etc. After N random sample trials the largest consensus set is selected, and at last the model is re-estimated using all the points in the selected subset. The minimal subset is composed of three non-colinear points which form a triangle. The shape of triangle is considered in random sample selection in order to make the sample selection reasonable. A new coordinate system transformation algorithm presented in this paper is used to avoid the singularity. The whole rotation transformation between the two coordinate systems can be solved by twice rotations expressed by Euler angle vector, each rotation has explicit physical means. Both simulation and real data are used to prove the correctness and validity of this mosaic method. This method has better noise immunity due to its robust estimation property, and has high accuracy as the shape constraint is added to random sample and the data normalization added to the absolute orientation. This method is applicable for high precision measurement of three-dimensional surface and also for the 3-D terrain mosaic.

TLS for generating multi-LOD of 3D building model

NASA Astrophysics Data System (ADS)

Akmalia, R.; Setan, H.; Majid, Z.; Suwardhi, D.; Chong, A.

2014-02-01

The popularity of Terrestrial Laser Scanners (TLS) to capture three dimensional (3D) objects has been used widely for various applications. Development in 3D models has also led people to visualize the environment in 3D. Visualization of objects in a city environment in 3D can be useful for many applications. However, different applications require different kind of 3D models. Since a building is an important object, CityGML has defined a standard for 3D building models at four different levels of detail (LOD). In this research, the advantages of TLS for capturing buildings and the modelling process of the point cloud can be explored. TLS will be used to capture all the building details to generate multi-LOD. This task, in previous works, involves usually the integration of several sensors. However, in this research, point cloud from TLS will be processed to generate the LOD3 model. LOD2 and LOD1 will then be generalized from the resulting LOD3 model. Result from this research is a guiding process to generate the multi-LOD of 3D building starting from LOD3 using TLS. Lastly, the visualization for multi-LOD model will also be shown.

Study into Point Cloud Geometric Rigidity and Accuracy of TLS-Based Identification of Geometric Bodies

NASA Astrophysics Data System (ADS)

Klapa, Przemyslaw; Mitka, Bartosz; Zygmunt, Mariusz

2017-12-01

Capability of obtaining a multimillion point cloud in a very short time has made the Terrestrial Laser Scanning (TLS) a widely used tool in many fields of science and technology. The TLS accuracy matches traditional devices used in land surveying (tacheometry, GNSS - RTK), but like any measurement it is burdened with error which affects the precise identification of objects based on their image in the form of a point cloud. The point’s coordinates are determined indirectly by means of measuring the angles and calculating the time of travel of the electromagnetic wave. Each such component has a measurement error which is translated into the final result. The XYZ coordinates of a measuring point are determined with some uncertainty and the very accuracy of determining these coordinates is reduced as the distance to the instrument increases. The paper presents the results of examination of geometrical stability of a point cloud obtained by means terrestrial laser scanner and accuracy evaluation of solids determined using the cloud. Leica P40 scanner and two different settings of measuring points were used in the tests. The first concept involved placing a few balls in the field and then scanning them from various sides at similar distances. The second part of measurement involved placing balls and scanning them a few times from one side but at varying distances from the instrument to the object. Each measurement encompassed a scan of the object with automatic determination of its position and geometry. The desk studies involved a semiautomatic fitting of solids and measurement of their geometrical elements, and comparison of parameters that determine their geometry and location in space. The differences of measures of geometrical elements of balls and translations vectors of the solids centres indicate the geometrical changes of the point cloud depending on the scanning distance and parameters. The results indicate the changes in the geometry of scanned objects depending on the point cloud quality and distance from the measuring instrument. Varying geometrical dimensions of the same element suggest also that the point cloud does not keep a stable geometry of measured objects.

Methane rain on Titan

NASA Technical Reports Server (NTRS)

Toon, Owen B.; Mckay, Christopher P.; Courtin, Regis; Ackerman, Thomas P.

1988-01-01

The atmosphere of Titan is characterized by means of model computations based on Voyager IRIS IR spectra and published data from laboratory determinations of absorption coefficients and cloud refractive indices. The results are presented in tables and graphs, and it is pointed out that the presence of Ar is not required in the model. Particular attention is given to the role of CH4, which is found to form patchy clouds (with particle radii of 50 microns or greater and visible/IR optical depths of 2-5) at altitudes up to about 30 km. The mechanisms by which such rain-sized particles could form are discussed, and it is suggested that the observed 500-600/cm spectrum is affected much less by the CH4 clouds than by H2 or variations in the temperature of the high-altitude haze.

Joint Simultaneous Reconstruction of Regularized Building Superstructures from Low-Density LIDAR Data Using Icp

NASA Astrophysics Data System (ADS)

Wichmann, Andreas; Kada, Martin

2016-06-01

There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.

Structure-From for Calibration of a Vehicle Camera System with Non-Overlapping Fields-Of in AN Urban Environment

NASA Astrophysics Data System (ADS)

Hanel, A.; Stilla, U.

2017-05-01

Vehicle environment cameras observing traffic participants in the area around a car and interior cameras observing the car driver are important data sources for driver intention recognition algorithms. To combine information from both camera groups, a camera system calibration can be performed. Typically, there is no overlapping field-of-view between environment and interior cameras. Often no marked reference points are available in environments, which are a large enough to cover a car for the system calibration. In this contribution, a calibration method for a vehicle camera system with non-overlapping camera groups in an urban environment is described. A-priori images of an urban calibration environment taken with an external camera are processed with the structure-frommotion method to obtain an environment point cloud. Images of the vehicle interior, taken also with an external camera, are processed to obtain an interior point cloud. Both point clouds are tied to each other with images of both image sets showing the same real-world objects. The point clouds are transformed into a self-defined vehicle coordinate system describing the vehicle movement. On demand, videos can be recorded with the vehicle cameras in a calibration drive. Poses of vehicle environment cameras and interior cameras are estimated separately using ground control points from the respective point cloud. All poses of a vehicle camera estimated for different video frames are optimized in a bundle adjustment. In an experiment, a point cloud is created from images of an underground car park, as well as a point cloud of the interior of a Volkswagen test car is created. Videos of two environment and one interior cameras are recorded. Results show, that the vehicle camera poses are estimated successfully especially when the car is not moving. Position standard deviations in the centimeter range can be achieved for all vehicle cameras. Relative distances between the vehicle cameras deviate between one and ten centimeters from tachymeter reference measurements.

Initial Simulations of RF Waves in Hot Plasmas Using the FullWave Code

NASA Astrophysics Data System (ADS)

Zhao, Liangji; Svidzinski, Vladimir; Spencer, Andrew; Kim, Jin-Soo

2017-10-01

FullWave is a simulation tool that models RF fields in hot inhomogeneous magnetized plasmas. The wave equations with linearized hot plasma dielectric response are solved in configuration space on adaptive cloud of computational points. The nonlocal hot plasma dielectric response is formulated by calculating the plasma conductivity kernel based on the solution of the linearized Vlasov equation in inhomogeneous magnetic field. In an rf field, the hot plasma dielectric response is limited to the distance of a few particles' Larmor radii, near the magnetic field line passing through the test point. The localization of the hot plasma dielectric response results in a sparse matrix of the problem thus significantly reduces the size of the problem and makes the simulations faster. We will present the initial results of modeling of rf waves using the Fullwave code, including calculation of nonlocal conductivity kernel in 2D Tokamak geometry; the interpolation of conductivity kernel from test points to adaptive cloud of computational points; and the results of self-consistent simulations of 2D rf fields using calculated hot plasma conductivity kernel in a tokamak plasma with reduced parameters. Work supported by the US DOE ``SBIR program.

Comparison of 3D point clouds obtained by photogrammetric UAVs and TLS to determine the attitude of dolerite outcrops discontinuities.

NASA Astrophysics Data System (ADS)

Duarte, João; Gonçalves, Gil; Duarte, Diogo; Figueiredo, Fernando; Mira, Maria

2015-04-01

Photogrammetric Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanners (TLS) are two emerging technologies that allows the production of dense 3D point clouds of the sensed topographic surfaces. Although image-based stereo-photogrammetric point clouds could not, in general, compete on geometric quality over TLS point clouds, fully automated mapping solutions based on ultra-light UAVs (or drones) have recently become commercially available at very reasonable accuracy and cost for engineering and geological applications. The purpose of this paper is to compare the two point clouds generated by these two technologies, in order to automatize the manual process tasks commonly used to detect and represent the attitude of discontinuities (Stereographic projection: Schmidt net - Equal area). To avoid the difficulties of access and guarantee the data survey security conditions, this fundamental step in all geological/geotechnical studies, applied to the extractive industry and engineering works, has to be replaced by a more expeditious and reliable methodology. This methodology will allow, in a more actuated clear way, give answers to the needs of evaluation of rock masses, by mapping the structures present, which will reduce considerably the associated risks (investment, structures dimensioning, security, etc.). A case study of a dolerite outcrop locate in the center of Portugal (the dolerite outcrop is situated in the volcanic complex of Serra de Todo-o-Mundo, Casais Gaiola, intruded in Jurassic sandstones) will be used to assess this methodology. The results obtained show that the 3D point cloud produced by the Photogrammetric UAV platform has the appropriate geometric quality for extracting the parameters that define the discontinuities of the dolerite outcrops. Although, they are comparable to the manual extracted parameters, their quality is inferior to parameters extracted from the TLS point cloud.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Key, J.

1989-01-01

The objectives are to develop a suitable validation data set for evaluating the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) algorithm for cloud retrieval in polar regions, to identify limitations of current procedures and to explore potential means to remedy them using textural classifiers, and to compare synoptic cloud data from model runs with observations. Toward the first goal, a polar data set consisting of visible, thermal, and passive microwave data was developed. The AVHRR and SMMR data were digitally merged to a polar stereographic projection with an effective pixel size of 5 sq km. With this data set, two unconventional methods of classifying the imagery for the analysis of polar clouds and surfaces were examined: one based on fuzzy sets theory and another based on a trained neural network. An algorithm for cloud detection was developed from an early test version of the ISCCP algorithm. This algorithm includes the identification of surface types with passive microwave, then temporal tests at each pixel location in the cloud detection phase. Cloud maps and clear sky radiance composites for 5 day periods are produced. Algorithm testing and validation was done with both actural AVHRR/SMMR data, and simulated imagery. From this point in the algorithm, groups of cloud pixels are examined for their spectral and textural characteristics, and a procedure is developed for the analysis of cloud patterns utilizing albedo, IR temperature, and texture. In a completion of earlier work, empirical analyses of arctic cloud cover were explored through manual interpretations of DMSP imagery and compared to U.S. Air Force 3D-nephanalysis. Comparisons of observed cloudiness from existing climatologies to patterns computed by the GISS climate model were also made.

Full Waveform Modelling for Subsurface Characterization with Converted-Wave Seismic Reflection

NASA Astrophysics Data System (ADS)

Triyoso, Wahyu; Oktariena, Madaniya; Sinaga, Edycakra; Syaifuddin, Firman

2017-04-01

While a large number of reservoirs have been explored using P-waves seismic data, P-wave seismic survey ceases to provide adequate result in seismically and geologically challenging areas, like gas cloud, shallow drilling hazards, strong multiples, highly fractured, anisotropy. Most of these reservoir problems can be addressed using P and PS seismic data combination. Multicomponent seismic survey records both P-wave and S-wave unlike conventional survey that only records compressional P-wave. Under certain conditions, conventional energy source can be used to record P and PS data using the fact that compressional wave energy partly converts into shear waves at the reflector. Shear component can be recorded using down going P-wave and upcoming S-wave by placing a horizontal component geophone on the ocean floor. A synthetic model is created based on real data to analyze the effect of gas cloud existence to PP and PS wave reflections which has a similar characteristic to Sub-Volcanic imaging. The challenge within the multicomponent seismic is the different travel time between P-wave and S-wave, therefore the converted-wave seismic data should be processed with different approach. This research will provide a method to determine an optimum converted point known as Common Conversion Point (CCP) that can solve the Asymmetrical Conversion Point of PS data. The value of γ (Vp/Vs) is essential to estimate the right CCP that will be used in converted-wave seismic processing. This research will also continue to the advanced processing method of converted-wave seismic by applying Joint Inversion to PP&PS seismic. Joint Inversion is a simultaneous model-based inversion that estimates the P&S-wave impedance which are consistent with the PP&PS amplitude data. The result reveals a more complex structure mirrored in PS data below the gas cloud area. Through estimated γ section resulted from Joint Inversion, we receive a better imaging improvement below gas cloud area tribute to the converted-wave seismic as additional constrain.

Cloud-point detection using a portable thickness shear mode crystal resonator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mansure, A.J.; Spates, J.J.; Germer, J.W.

1997-08-01

The Thickness Shear Mode (TSM) crystal resonator monitors the crude oil by propagating a shear wave into the oil. The coupling of the shear wave and the crystal vibrations is a function of the viscosity of the oil. By driving the crystal with circuitry that incorporates feedback, it is possible to determine the change from Newtonian to non-Newtonian viscosity at the cloud point. A portable prototype TSM Cloud Point Detector (CPD) has performed flawlessly during field and lab tests proving the technique is less subjective or operator dependent than the ASTM standard. The TSM CPD, in contrast to standard viscositymore » techniques, makes the measurement in a closed container capable of maintaining up to 100 psi. The closed container minimizes losses of low molecular weight volatiles, allowing samples (25 ml) to be retested with the addition of chemicals. By cycling/thermal soaking the sample, the effects of thermal history can be investigated and eliminated as a source of confusion. The CPD is portable, suitable for shipping the field offices for use by personnel without special training or experience in cloud point measurements. As such, it can make cloud point data available without the delays and inconvenience of sending samples to special labs. The crystal resonator technology can be adapted to in-line monitoring of cloud point and deposition detection.« less

Geomorphological activity at a rock glacier front detected with a 3D density-based clustering algorithm

NASA Astrophysics Data System (ADS)

Micheletti, Natan; Tonini, Marj; Lane, Stuart N.

2017-02-01

Acquisition of high density point clouds using terrestrial laser scanners (TLSs) has become commonplace in geomorphic science. The derived point clouds are often interpolated onto regular grids and the grids compared to detect change (i.e. erosion and deposition/advancement movements). This procedure is necessary for some applications (e.g. digital terrain analysis), but it inevitably leads to a certain loss of potentially valuable information contained within the point clouds. In the present study, an alternative methodology for geomorphological analysis and feature detection from point clouds is proposed. It rests on the use of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), applied to TLS data for a rock glacier front slope in the Swiss Alps. The proposed methods allowed the detection and isolation of movements directly from point clouds which yield to accuracies in the following computation of volumes that depend only on the actual registered distance between points. We demonstrated that these values are more conservative than volumes computed with the traditional DEM comparison. The results are illustrated for the summer of 2015, a season of enhanced geomorphic activity associated with exceptionally high temperatures.

Impact of bacterial ice nucleating particles on weather predicted by a numerical weather prediction model

NASA Astrophysics Data System (ADS)

Sahyoun, Maher; Korsholm, Ulrik S.; Sørensen, Jens H.; Šantl-Temkiv, Tina; Finster, Kai; Gosewinkel, Ulrich; Nielsen, Niels W.

2017-12-01

Bacterial ice-nucleating particles (INP) have the ability to facilitate ice nucleation from super-cooled cloud droplets at temperatures just below the melting point. Bacterial INP have been detected in cloud water, precipitation, and dry air, hence they may have an impact on weather and climate. In modeling studies, the potential impact of bacteria on ice nucleation and precipitation formation on global scale is still uncertain due to their small concentration compared to other types of INP, i.e. dust. Those earlier studies did not account for the yet undetected high concentration of nanoscale fragments of bacterial INP, which may be found free or attached to soil dust in the atmosphere. In this study, we investigate the sensitivity of modeled cloud ice, precipitation and global solar radiation in different weather scenarios to changes in the fraction of cloud droplets containing bacterial INP, regardless of their size. For this purpose, a module that calculates the probability of ice nucleation as a function of ice nucleation rate and bacterial INP fraction was developed and implemented in a numerical weather prediction model. The threshold value for the fraction of cloud droplets containing bacterial INP needed to produce a 1% increase in cloud ice was determined at 10-5 to 10-4. We also found that increasing this fraction causes a perturbation in the forecast, leading to significant differences in cloud ice and smaller differences in convective and total precipitation and in net solar radiation reaching the surface. These effects were most pronounced in local convective events. Our results show that bacterial INP can be considered as a trigger factor for precipitation, but not an enhancement factor.

Carbon Sequestration Estimation of Street Trees Based on Point Cloud from Vehicle-Borne Laser Scanning System

NASA Astrophysics Data System (ADS)

Zhao, Y.; Hu, Q.

2017-09-01

Continuous development of urban road traffic system requests higher standards of road ecological environment. Ecological benefits of street trees are getting more attention. Carbon sequestration of street trees refers to the carbon stocks of street trees, which can be a measurement for ecological benefits of street trees. Estimating carbon sequestration in a traditional way is costly and inefficient. In order to solve above problems, a carbon sequestration estimation approach for street trees based on 3D point cloud from vehicle-borne laser scanning system is proposed in this paper. The method can measure the geometric parameters of a street tree, including tree height, crown width, diameter at breast height (DBH), by processing and analyzing point cloud data of an individual tree. Four Chinese scholartree trees and four camphor trees are selected for experiment. The root mean square error (RMSE) of tree height is 0.11m for Chinese scholartree and 0.02m for camphor. Crown widths in X direction and Y direction, as well as the average crown width are calculated. And the RMSE of average crown width is 0.22m for Chinese scholartree and 0.10m for camphor. The last calculated parameter is DBH, the RMSE of DBH is 0.5cm for both Chinese scholartree and camphor. Combining the measured geometric parameters and an appropriate carbon sequestration calculation model, the individual tree's carbon sequestration will be estimated. The proposed method can help enlarge application range of vehicle-borne laser point cloud data, improve the efficiency of estimating carbon sequestration, construct urban ecological environment and manage landscape.

GPU-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites.

PubMed

Leinweber, Matthias; Fober, Thomas; Freisleben, Bernd

2018-01-01

In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.

The Feasibility of 3d Point Cloud Generation from Smartphones

NASA Astrophysics Data System (ADS)

Alsubaie, N.; El-Sheimy, N.

2016-06-01

This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones. The smartphone's motion sensors are used to directly acquire the Exterior Orientation Parameters (EOPs) of the captured images. These EOPs, along with the Interior Orientation Parameters (IOPs) of the camera/ phone, are used to reconstruct the image-based 3D point cloud. However, because smartphone motion sensors suffer from poor GPS accuracy, accumulated drift and high signal noise, inaccurate 3D mapping solutions often result. Therefore, horizontal and vertical linear features, visible in each image, are extracted and used as constraints in the bundle adjustment procedure. These constraints correct the relative position and orientation of the 3D mapping solution. Once the enhanced EOPs are estimated, the semi-global matching algorithm (SGM) is used to generate the image-based dense 3D point cloud. Statistical analysis and assessment are implemented herein, in order to demonstrate the feasibility of 3D point cloud generation from the consumer-grade sensors in smartphones.

Clouds and the Earth's Radiant Energy System (CERES) Algorithm Theoretical Basis Document. Volume 3; Cloud Analyses and Determination of Improved Top of Atmosphere Fluxes (Subsystem 4)

NASA Technical Reports Server (NTRS)

1995-01-01

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 3 details the advanced CERES methods for performing scene identification and inverting each CERES scanner radiance to a top-of-the-atmosphere (TOA) flux. CERES determines cloud fraction, height, phase, effective particle size, layering, and thickness from high-resolution, multispectral imager data. CERES derives cloud properties for each pixel of the Tropical Rainfall Measuring Mission (TRMM) visible and infrared scanner and the Earth Observing System (EOS) moderate-resolution imaging spectroradiometer. Cloud properties for each imager pixel are convolved with the CERES footprint point spread function to produce average cloud properties for each CERES scanner radiance. The mean cloud properties are used to determine an angular distribution model (ADM) to convert each CERES radiance to a TOA flux. The TOA fluxes are used in simple parameterization to derive surface radiative fluxes. This state-of-the-art cloud-radiation product will be used to substantially improve our understanding of the complex relationship between clouds and the radiation budget of the Earth-atmosphere system.

Challenges and opportunities of cloud computing for atmospheric sciences

NASA Astrophysics Data System (ADS)

Pérez Montes, Diego A.; Añel, Juan A.; Pena, Tomás F.; Wallom, David C. H.

2016-04-01

Cloud computing is an emerging technological solution widely used in many fields. Initially developed as a flexible way of managing peak demand it has began to make its way in scientific research. One of the greatest advantages of cloud computing for scientific research is independence of having access to a large cyberinfrastructure to fund or perform a research project. Cloud computing can avoid maintenance expenses for large supercomputers and has the potential to 'democratize' the access to high-performance computing, giving flexibility to funding bodies for allocating budgets for the computational costs associated with a project. Two of the most challenging problems in atmospheric sciences are computational cost and uncertainty in meteorological forecasting and climate projections. Both problems are closely related. Usually uncertainty can be reduced with the availability of computational resources to better reproduce a phenomenon or to perform a larger number of experiments. Here we expose results of the application of cloud computing resources for climate modeling using cloud computing infrastructures of three major vendors and two climate models. We show how the cloud infrastructure compares in performance to traditional supercomputers and how it provides the capability to complete experiments in shorter periods of time. The monetary cost associated is also analyzed. Finally we discuss the future potential of this technology for meteorological and climatological applications, both from the point of view of operational use and research.

Study on the application of mobile internet cloud computing platform

NASA Astrophysics Data System (ADS)

Gong, Songchun; Fu, Songyin; Chen, Zheng

2012-04-01

The innovative development of computer technology promotes the application of the cloud computing platform, which actually is the substitution and exchange of a sort of resource service models and meets the needs of users on the utilization of different resources after changes and adjustments of multiple aspects. "Cloud computing" owns advantages in many aspects which not merely reduce the difficulties to apply the operating system and also make it easy for users to search, acquire and process the resources. In accordance with this point, the author takes the management of digital libraries as the research focus in this paper, and analyzes the key technologies of the mobile internet cloud computing platform in the operation process. The popularization and promotion of computer technology drive people to create the digital library models, and its core idea is to strengthen the optimal management of the library resource information through computers and construct an inquiry and search platform with high performance, allowing the users to access to the necessary information resources at any time. However, the cloud computing is able to promote the computations within the computers to distribute in a large number of distributed computers, and hence implement the connection service of multiple computers. The digital libraries, as a typical representative of the applications of the cloud computing, can be used to carry out an analysis on the key technologies of the cloud computing.

Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations

PubMed Central

Yao, Lianbi; Wu, Hangbin; Li, Yayun; Meng, Bin; Qian, Jinfei; Liu, Chun; Fan, Hongchao

2017-01-01

A mobile mapping system (MMS) is usually utilized to collect environmental data on and around urban roads. Laser scanners and panoramic cameras are the main sensors of an MMS. This paper presents a new method for the registration of the point clouds and panoramic images based on sensor constellation. After the sensor constellation was analyzed, a feature point, the intersection of the connecting line between the global positioning system (GPS) antenna and the panoramic camera with a horizontal plane, was utilized to separate the point clouds into blocks. The blocks for the central and sideward laser scanners were extracted with the segmentation feature points. Then, the point clouds located in the blocks were separated from the original point clouds. Each point in the blocks was used to find the accurate corresponding pixel in the relative panoramic images via a collinear function, and the position and orientation relationship amongst different sensors. A search strategy is proposed for the correspondence of laser scanners and lenses of panoramic cameras to reduce calculation complexity and improve efficiency. Four cases of different urban road types were selected to verify the efficiency and accuracy of the proposed method. Results indicate that most of the point clouds (with an average of 99.7%) were successfully registered with the panoramic images with great efficiency. Geometric evaluation results indicate that horizontal accuracy was approximately 0.10–0.20 m, and vertical accuracy was approximately 0.01–0.02 m for all cases. Finally, the main factors that affect registration accuracy, including time synchronization amongst different sensors, system positioning and vehicle speed, are discussed. PMID:28398256

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Shawn

This code consists of Matlab routines which enable the user to perform non-manifold surface reconstruction via triangulation from high dimensional point cloud data. The code was based on an algorithm originally developed in [Freedman (2007), An Incremental Algorithm for Reconstruction of Surfaces of Arbitrary Codimension Computational Geometry: Theory and Applications, 36(2):106-116]. This algorithm has been modified to accommodate non-manifold surface according to the work described in [S. Martin and J.-P. Watson (2009), Non-Manifold Surface Reconstruction from High Dimensional Point Cloud DataSAND #5272610].The motivation for developing the code was a point cloud describing the molecular conformation space of cyclooctane (C8H16). Cyclooctanemore » conformation space was represented using points in 72 dimensions (3 coordinates for each molecule). The code was used to triangulate the point cloud and thereby study the geometry and topology of cyclooctane. Futures applications are envisioned for peptides and proteins.« less

Classification of Mobile Laser Scanning Point Clouds from Height Features

NASA Astrophysics Data System (ADS)

Zheng, M.; Lemmens, M.; van Oosterom, P.

2017-09-01

The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features - two height components and one reflectance value, and achieved an overall accuracy of 73 %, which is really encouraging for further refining our approach.

Outdoor Illegal Construction Identification Algorithm Based on 3D Point Cloud Segmentation

NASA Astrophysics Data System (ADS)

An, Lu; Guo, Baolong

2018-03-01

Recently, various illegal constructions occur significantly in our surroundings, which seriously restrict the orderly development of urban modernization. The 3D point cloud data technology is used to identify the illegal buildings, which could address the problem above effectively. This paper proposes an outdoor illegal construction identification algorithm based on 3D point cloud segmentation. Initially, in order to save memory space and reduce processing time, a lossless point cloud compression method based on minimum spanning tree is proposed. Then, a ground point removing method based on the multi-scale filtering is introduced to increase accuracy. Finally, building clusters on the ground can be obtained using a region growing method, as a result, the illegal construction can be marked. The effectiveness of the proposed algorithm is verified using a publicly data set collected from the International Society for Photogrammetry and Remote Sensing (ISPRS).

Adjustment of Sonar and Laser Acquisition Data for Building the 3D Reference Model of a Canal Tunnel.

PubMed

Moisan, Emmanuel; Charbonnier, Pierre; Foucher, Philippe; Grussenmeyer, Pierre; Guillemin, Samuel; Koehl, Mathieu

2015-12-11

In this paper, we focus on the construction of a full 3D model of a canal tunnel by combining terrestrial laser (for its above-water part) and sonar (for its underwater part) scans collected from static acquisitions. The modeling of such a structure is challenging because the sonar device is used in a narrow environment that induces many artifacts. Moreover, the location and the orientation of the sonar device are unknown. In our approach, sonar data are first simultaneously denoised and meshed. Then, above- and under-water point clouds are co-registered to generate directly the full 3D model of the canal tunnel. Faced with the lack of overlap between both models, we introduce a robust algorithm that relies on geometrical entities and partially-immersed targets, which are visible in both the laser and sonar point clouds. A full 3D model, visually promising, of the entrance of a canal tunnel is obtained. The analysis of the method raises several improvement directions that will help with obtaining more accurate models, in a more automated way, in the limits of the involved technology.