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Sample records for 3-d point clouds

  1. Can 3D Point Clouds Replace GCPs?

    NASA Astrophysics Data System (ADS)

    Stavropoulou, G.; Tzovla, G.; Georgopoulos, A.

    2014-05-01

    Over the past decade, large-scale photogrammetric products have been extensively used for the geometric documentation of cultural heritage monuments, as they combine metric information with the qualities of an image document. Additionally, the rising technology of terrestrial laser scanning has enabled the easier and faster production of accurate digital surface models (DSM), which have in turn contributed to the documentation of heavily textured monuments. However, due to the required accuracy of control points, the photogrammetric methods are always applied in combination with surveying measurements and hence are dependent on them. Along this line of thought, this paper explores the possibility of limiting the surveying measurements and the field work necessary for the production of large-scale photogrammetric products and proposes an alternative method on the basis of which the necessary control points instead of being measured with surveying procedures are chosen from a dense and accurate point cloud. Using this point cloud also as a surface model, the only field work necessary is the scanning of the object and image acquisition, which need not be subject to strict planning. To evaluate the proposed method an algorithm and the complementary interface were produced that allow the parallel manipulation of 3D point clouds and images and through which single image procedures take place. The paper concludes by presenting the results of a case study in the ancient temple of Hephaestus in Athens and by providing a set of guidelines for implementing effectively the method.

  2. Alignment of continuous video onto 3D point clouds.

    PubMed

    Zhao, Wenyi; Nister, David; Hsu, Steve

    2005-08-01

    We propose a general framework for aligning continuous (oblique) video onto 3D sensor data. We align a point cloud computed from the video onto the point cloud directly obtained from a 3D sensor. This is in contrast to existing techniques where the 2D images are aligned to a 3D model derived from the 3D sensor data. Using point clouds enables the alignment for scenes full of objects that are difficult to model; for example, trees. To compute 3D point clouds from video, motion stereo is used along with a state-of-the-art algorithm for camera pose estimation. Our experiments with real data demonstrate the advantages of the proposed registration algorithm for texturing models in large-scale semiurban environments. The capability to align video before a 3D model is built from the 3D sensor data offers new practical opportunities for 3D modeling. We introduce a novel modeling-through-registration approach that fuses 3D information from both the 3D sensor and the video. Initial experiments with real data illustrate the potential of the proposed approach.

  3. Automatic 3-D Point Cloud Classification of Urban Environments

    DTIC Science & Technology

    2008-12-01

    paper, we address the problem of automated interpretation of 3-D point clouds from scenes of urban and natural environments; our analysis is...over 10 km of traverse. We implemented three geometric features com- monly used in spectral analysis of point clouds . We de- fine λ2 ≥ λ1 ≥ λ0 to be

  4. Point Cloud Visualization in AN Open Source 3d Globe

    NASA Astrophysics Data System (ADS)

    De La Calle, M.; Gómez-Deck, D.; Koehler, O.; Pulido, F.

    2011-09-01

    During the last years the usage of 3D applications in GIS is becoming more popular. Since the appearance of Google Earth, users are familiarized with 3D environments. On the other hand, nowadays computers with 3D acceleration are common, broadband access is widespread and the public information that can be used in GIS clients that are able to use data from the Internet is constantly increasing. There are currently several libraries suitable for this kind of applications. Based on these facts, and using libraries that are already developed and connected to our own developments, we are working on the implementation of a real 3D GIS with analysis capabilities. Since a 3D GIS such as this can be very interesting for tasks like LiDAR or Laser Scanner point clouds rendering and analysis, special attention is given to get an optimal handling of very large data sets. Glob3 will be a multidimensional GIS in which 3D point clouds could be explored and analysed, even if they are consist of several million points.The latest addition to our visualization libraries is the development of a points cloud server that works regardless of the cloud's size. The server receives and processes petitions from a 3d client (for example glob3, but could be any other, such as one based on WebGL) and delivers the data in the form of pre-processed tiles, depending on the required level of detail.

  5. 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.

  6. Mirror Identification and Correction of 3d Point Clouds

    NASA Astrophysics Data System (ADS)

    Käshammer, P.-F.; Nüchter, A.

    2015-02-01

    In terrestrial laser scanning (TLS), the surface geometry of objects is scanned by laser beams and recorded digitally. This produces a discrete set of scan points, commonly referred to as a point cloud. The coordinates of the scan points are determined by measuring the angles and the time-of-flight relative to the origin (scanner position). However, if it comes to mirror surfaces laser beams are fully reflected, due to the high reflectivity. Mirrors do not appear in the point cloud at all. Instead, for every reflected beam, a incorrect scan point is created behind the actual mirror plane. Consequently, problems arise in multiple derived application fields such as 3D virtual reconstruction of complex architectures. The paper presents a new approach to automatically detect framed rectangular mirrors with known dimensions and to correct the 3D point cloud, using the calculated mirror plane.

  7. 3D Building Reconstruction Using Dense Photogrammetric Point Cloud

    NASA Astrophysics Data System (ADS)

    Malihi, S.; Valadan Zoej, M. J.; Hahn, M.; Mokhtarzade, M.; Arefi, H.

    2016-06-01

    Three dimensional models of urban areas play an important role in city planning, disaster management, city navigation and other applications. Reconstruction of 3D building models is still a challenging issue in 3D city modelling. Point clouds generated from multi view images of UAV is a novel source of spatial data, which is used in this research for building reconstruction. The process starts with the segmentation of point clouds of roofs and walls into planar groups. By generating related surfaces and using geometrical constraints plus considering symmetry, a 3d model of building is reconstructed. In a refinement step, dormers are extracted, and their models are reconstructed. The details of the 3d reconstructed model are in LoD3 level, with respect to modelling eaves, fractions of roof and dormers.

  8. a Fast Method for Measuring the Similarity Between 3d Model and 3d Point Cloud

    NASA Astrophysics Data System (ADS)

    Zhang, Zongliang; Li, Jonathan; Li, Xin; Lin, Yangbin; Zhang, Shanxin; Wang, Cheng

    2016-06-01

    This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.

  9. 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.

  10. 3D scene reconstruction based on 3D laser point cloud combining UAV images

    NASA Astrophysics Data System (ADS)

    Liu, Huiyun; Yan, Yangyang; Zhang, Xitong; Wu, Zhenzhen

    2016-03-01

    It is a big challenge capturing and modeling 3D information of the built environment. A number of techniques and technologies are now in use. These include GPS, and photogrammetric application and also remote sensing applications. The experiment uses multi-source data fusion technology for 3D scene reconstruction based on the principle of 3D laser scanning technology, which uses the laser point cloud data as the basis and Digital Ortho-photo Map as an auxiliary, uses 3DsMAX software as a basic tool for building three-dimensional scene reconstruction. The article includes data acquisition, data preprocessing, 3D scene construction. The results show that the 3D scene has better truthfulness, and the accuracy of the scene meet the need of 3D scene construction.

  11. 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.

  12. The medial scaffold of 3D unorganized point clouds.

    PubMed

    Leymarie, Frederic F; Kimia, Benjamin B

    2007-02-01

    We introduce the notion of the medial scaffold, a hierarchical organization of the medial axis of a 3D shape in the form of a graph constructed from special medial curves connecting special medial points. A key advantage of the scaffold is that it captures the qualitative aspects of shape in a hierarchical and tightly condensed representation. We propose an efficient and exact method for computing the medial scaffold based on a notion of propagation along the scaffold itself, starting from initial sources of the flow and constructing the scaffold during the propagation. We examine this method specifically in the context of an unorganized cloud of points in 3D, e.g., as obtained from laser range finders, which typically involve hundreds of thousands of points, but the ideas are generalizable to data arising from geometrically described surface patches. The computational bottleneck in the propagation-based scheme is in finding the initial sources of the flow. We thus present several ideas to avoid the unnecessary consideration of pairs of points which cannot possibly form a medial point source, such as the "visibility" of a point from another given a third point and the interaction of clusters of points. An application of using the medial scaffold for the representation of point samplings of real-life objects is also illustrated.

  13. 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

  14. Comparison of 3D interest point detectors and descriptors for point cloud fusion

    NASA Astrophysics Data System (ADS)

    Hänsch, R.; Weber, T.; Hellwich, O.

    2014-08-01

    The extraction and description of keypoints as salient image parts has a long tradition within processing and analysis of 2D images. Nowadays, 3D data gains more and more importance. This paper discusses the benefits and limitations of keypoints for the task of fusing multiple 3D point clouds. For this goal, several combinations of 3D keypoint detectors and descriptors are tested. The experiments are based on 3D scenes with varying properties, including 3D scanner data as well as Kinect point clouds. The obtained results indicate that the specific method to extract and describe keypoints in 3D data has to be carefully chosen. In many cases the accuracy suffers from a too strong reduction of the available points to keypoints.

  15. Filtering method for 3D laser scanning point cloud

    NASA Astrophysics Data System (ADS)

    Liu, Da; Wang, Li; Hao, Yuncai; Zhang, Jun

    2015-10-01

    In recent years, with the rapid development of the hardware and software of the three-dimensional model acquisition, three-dimensional laser scanning technology is utilized in various aspects, especially in space exploration. The point cloud filter is very important before using the data. In the paper, considering both the processing quality and computing speed, an improved mean-shift point cloud filter method is proposed. Firstly, by analyze the relevance of the normal vector between the upcoming processing point and the near points, the iterative neighborhood of the mean-shift is selected dynamically, then the high frequency noise is constrained. Secondly, considering the normal vector of the processing point, the normal vector is updated. Finally, updated position is calculated for each point, then each point is moved in the normal vector according to the updated position. The experimental results show that the large features are retained, at the same time, the small sharp features are also existed for different size and shape of objects, so the target feature information is protected precisely. The computational complexity of the proposed method is not high, it can bring high precision results with fast speed, so it is very suitable for space application. It can also be utilized in civil, such as large object measurement, industrial measurement, car navigation etc. In the future, filter with the help of point strength will be further exploited.

  16. Automated Mosaicking of Multiple 3d Point Clouds Generated from a Depth Camera

    NASA Astrophysics Data System (ADS)

    Kim, H.; Yoon, W.; Kim, T.

    2016-06-01

    In this paper, we propose a method for automated mosaicking of multiple 3D point clouds generated from a depth camera. A depth camera generates depth data by using ToF (Time of Flight) method and intensity data by using intensity of returned signal. The depth camera used in this paper was a SR4000 from MESA Imaging. This camera generates a depth map and intensity map of 176 x 44 pixels. Generated depth map saves physical depth data with mm of precision. Generated intensity map contains texture data with many noises. We used texture maps for extracting tiepoints and depth maps for assigning z coordinates to tiepoints and point cloud mosaicking. There are four steps in the proposed mosaicking method. In the first step, we acquired multiple 3D point clouds by rotating depth camera and capturing data per rotation. In the second step, we estimated 3D-3D transformation relationships between subsequent point clouds. For this, 2D tiepoints were extracted automatically from the corresponding two intensity maps. They were converted into 3D tiepoints using depth maps. We used a 3D similarity transformation model for estimating the 3D-3D transformation relationships. In the third step, we converted local 3D-3D transformations into a global transformation for all point clouds with respect to a reference one. In the last step, the extent of single depth map mosaic was calculated and depth values per mosaic pixel were determined by a ray tracing method. For experiments, 8 depth maps and intensity maps were used. After the four steps, an output mosaicked depth map of 454x144 was generated. It is expected that the proposed method would be useful for developing an effective 3D indoor mapping method in future.

  17. 3D point cloud registration based on the assistant camera and Harris-SIFT

    NASA Astrophysics Data System (ADS)

    Zhang, Yue; Yu, HongYang

    2016-07-01

    3D(Three-Dimensional) point cloud registration technology is the hot topic in the field of 3D reconstruction, but most of the registration method is not real-time and ineffective. This paper proposes a point cloud registration method of 3D reconstruction based on Harris-SIFT and assistant camera. The assistant camera is used to pinpoint mobile 3D reconstruction device, The feature points of images are detected by using Harris operator, the main orientation for each feature point is calculated, and lastly, the feature point descriptors are generated after rotating the coordinates of the descriptors relative to the feature points' main orientations. Experimental results of demonstrate the effectiveness of the proposed method.

  18. Unlocking the scientific potential of complex 3D point cloud dataset : new classification and 3D comparison methods

    NASA Astrophysics Data System (ADS)

    Lague, D.; Brodu, N.; Leroux, J.

    2012-12-01

    Ground based lidar and photogrammetric techniques are increasingly used to track the evolution of natural surfaces in 3D at an unprecedented resolution and precision. The range of applications encompass many type of natural surfaces with different geometries and roughness characteristics (landslides, cliff erosion, river beds, bank erosion,....). Unravelling surface change in these contexts requires to compare large point clouds in 2D or 3D. The most commonly used method in geomorphology is based on a 2D difference of the gridded point clouds. Yet this is hardly adapted to many 3D natural environments such as rivers (with horizontal beds and vertical banks), while gridding complex rough surfaces is a complex task. On the other hand, tools allowing to perform 3D comparison are scarce and may require to mesh the point clouds which is difficult on rough natural surfaces. Moreover, existing 3D comparison tools do not provide an explicit calculation of confidence intervals that would factor in registration errors, roughness effects and instrument related position uncertainties. To unlock this problem, we developed the first algorithm combining a 3D measurement of surface change directly on point clouds with an estimate of spatially variable confidence intervals (called M3C2). The method has two steps : (1) surface normal estimation and orientation in 3D at a scale consistent with the local roughness ; (2) measurement of mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing 3D methods based on a closest-point calculation demonstrates the higher precision of the M3C2 method when mm changes needs to be detected. The M3C2 method is also simple to use as it does not require surface meshing or gridding, and is not sensitive to missing data or change in point density. We also present a 3D classification tool (CANUPO) for vegetation removal based on a new geometrical measure: the multi

  19. Fast Probabilistic Fusion of 3d Point Clouds via Occupancy Grids for Scene Classification

    NASA Astrophysics Data System (ADS)

    Kuhn, Andreas; Huang, Hai; Drauschke, Martin; Mayer, Helmut

    2016-06-01

    High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

  20. 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

  1. Extension of RCC Topological Relations for 3d Complex Objects Components Extracted from 3d LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Xing, Xu-Feng; Abolfazl Mostafavia, Mir; Wang, Chen

    2016-06-01

    Topological relations are fundamental for qualitative description, querying and analysis of a 3D scene. Although topological relations for 2D objects have been extensively studied and implemented in GIS applications, their direct extension to 3D is very challenging and they cannot be directly applied to represent relations between components of complex 3D objects represented by 3D B-Rep models in R3. Herein we present an extended Region Connection Calculus (RCC) model to express and formalize topological relations between planar regions for creating 3D model represented by Boundary Representation model in R3. We proposed a new dimension extended 9-Intersection model to represent the basic relations among components of a complex object, including disjoint, meet and intersect. The last element in 3*3 matrix records the details of connection through the common parts of two regions and the intersecting line of two planes. Additionally, this model can deal with the case of planar regions with holes. Finally, the geometric information is transformed into a list of strings consisting of topological relations between two planar regions and detailed connection information. The experiments show that the proposed approach helps to identify topological relations of planar segments of point cloud automatically.

  2. From Tls Point Clouds to 3d Models of Trees: a Comparison of Existing Algorithms for 3d Tree Reconstruction

    NASA Astrophysics Data System (ADS)

    Bournez, E.; Landes, T.; Saudreau, M.; Kastendeuch, P.; Najjar, G.

    2017-02-01

    3D models of tree geometry are important for numerous studies, such as for urban planning or agricultural studies. In climatology, tree models can be necessary for simulating the cooling effect of trees by estimating their evapotranspiration. The literature shows that the more accurate the 3D structure of a tree is, the more accurate microclimate models are. This is the reason why, since 2013, we have been developing an algorithm for the reconstruction of trees from terrestrial laser scanner (TLS) data, which we call TreeArchitecture. Meanwhile, new promising algorithms dedicated to tree reconstruction have emerged in the literature. In this paper, we assess the capacity of our algorithm and of two others -PlantScan3D and SimpleTree- to reconstruct the 3D structure of trees. The aim of this reconstruction is to be able to characterize the geometric complexity of trees, with different heights, sizes and shapes of branches. Based on a specific surveying workflow with a TLS, we have acquired dense point clouds of six different urban trees, with specific architectures, before reconstructing them with each algorithm. Finally, qualitative and quantitative assessments of the models are performed using reference tree reconstructions and field measurements. Based on this assessment, the advantages and the limits of every reconstruction algorithm are highlighted. Anyway, very satisfying results can be reached for 3D reconstructions of tree topology as well as of tree volume.

  3. 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.

  4. Image-Based Airborne LiDAR Point Cloud Encoding for 3d Building Model Retrieval

    NASA Astrophysics Data System (ADS)

    Chen, Yi-Chen; Lin, Chao-Hung

    2016-06-01

    With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority

  5. Facets : a Cloudcompare Plugin to Extract Geological Planes from Unstructured 3d Point Clouds

    NASA Astrophysics Data System (ADS)

    Dewez, T. J. B.; Girardeau-Montaut, D.; Allanic, C.; Rohmer, J.

    2016-06-01

    Geological planar facets (stratification, fault, joint…) are key features to unravel the tectonic history of rock outcrop or appreciate the stability of a hazardous rock cliff. Measuring their spatial attitude (dip and strike) is generally performed by hand with a compass/clinometer, which is time consuming, requires some degree of censoring (i.e. refusing to measure some features judged unimportant at the time), is not always possible for fractures higher up on the outcrop and is somewhat hazardous. 3D virtual geological outcrop hold the potential to alleviate these issues. Efficiently segmenting massive 3D point clouds into individual planar facets, inside a convenient software environment was lacking. FACETS is a dedicated plugin within CloudCompare v2.6.2 (http://cloudcompare.org/ ) implemented to perform planar facet extraction, calculate their dip and dip direction (i.e. azimuth of steepest decent) and report the extracted data in interactive stereograms. Two algorithms perform the segmentation: Kd-Tree and Fast Marching. Both divide the point cloud into sub-cells, then compute elementary planar objects and aggregate them progressively according to a planeity threshold into polygons. The boundaries of the polygons are adjusted around segmented points with a tension parameter, and the facet polygons can be exported as 3D polygon shapefiles towards third party GIS software or simply as ASCII comma separated files. One of the great features of FACETS is the capability to explore planar objects but also 3D points with normals with the stereogram tool. Poles can be readily displayed, queried and manually segmented interactively. The plugin blends seamlessly into CloudCompare to leverage all its other 3D point cloud manipulation features. A demonstration of the tool is presented to illustrate these different features. While designed for geological applications, FACETS could be more widely applied to any planar

  6. 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.

  7. The Engelbourg's ruins: from 3D TLS point cloud acquisition to 3D virtual and historic models

    NASA Astrophysics Data System (ADS)

    Koehl, Mathieu; Berger, Solveig; Nobile, Sylvain

    2014-05-01

    The Castle of Engelbourg was built at the beginning of the 13th century, at the top of the Schlossberg. It is situated on the territory of the municipality of Thann (France), at the crossroads of Alsace and Lorraine, and dominates the outlet of the valley of Thur. Its strategic position was one of the causes of its systematic destructions during the 17th century, and Louis XIV finished his fate by ordering his demolition in 1673. Today only few vestiges remain, of which a section of the main tower from about 7m of diameter and 4m of wide laying on its slice, unique characteristic in the regional castral landscape. It is visible since the valley, was named "the Eye of the witch", and became a key attraction of the region. The site, which extends over approximately one hectare, is for several years the object of numerous archaeological studies and is at the heart of a project of valuation of the vestiges today. It was indeed a key objective, among the numerous planned works, to realize a 3D model of the site in its current state, in other words, a virtual model "such as seized", exploitable as well from a cultural and tourist point of view as by scientists and in archaeological researches. The team of the ICube/INSA lab had in responsibility the realization of this model, the acquisition of the data until the delivery of the virtual model, thanks to 3D TLS and topographic surveying methods. It was also planned to integrate into this 3D model, data of 2D archives, stemming from series of former excavations. The objectives of this project were the following ones: • Acquisition of 3D digital data of the site and 3D modelling • Digitization of the 2D archaeological data and integration in the 3D model • Implementation of a database connected to the 3D model • Virtual Visit of the site The obtained results allowed us to visualize every 3D object individually, under several forms (point clouds, 3D meshed objects and models, etc.) and at several levels of detail

  8. Automated detection of planes in 3-D point clouds using fast Hough transforms

    NASA Astrophysics Data System (ADS)

    Ogundana, Olatokunbo O.; Coggrave, C. Russell; Burguete, Richard L.; Huntley, Jonathan M.

    2011-05-01

    Calibration of 3-D optical sensors often involves the use of calibration artifacts consisting of geometric features, such as 2 or more planes or spheres of known separation. In order to reduce data processing time and minimize user input during calibration, the respective features of the calibration artifact need to be automatically detected and labeled from the measured point clouds. The Hough transform (HT), which is a well-known method for line detection based on foot-of-normal parameterization, has been extended to plane detection in 3-D space. However, the typically sparse intermediate 3-D Hough accumulator space leads to excessive memory storage requirements. A 3-D HT method based on voting in an optimized sparse 3-D matrix model and efficient peak detection in Hough space is described. An alternative 1-D HT is also investigated for rapid detection of nominally parallel planes. Examples of the performance of these methods using simulated and experimental shape data are presented.

  9. Feature relevance assessment for the semantic interpretation of 3D point cloud data

    NASA Astrophysics Data System (ADS)

    Weinmann, M.; Jutzi, B.; Mallet, C.

    2013-10-01

    The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

  10. Comparison Between Two Generic 3d Building Reconstruction Approaches - Point Cloud Based VS. Image Processing Based

    NASA Astrophysics Data System (ADS)

    Dahlke, D.; Linkiewicz, M.

    2016-06-01

    This paper compares two generic approaches for the reconstruction of buildings. Synthesized and real oblique and vertical aerial imagery is transformed on the one hand into a dense photogrammetric 3D point cloud and on the other hand into photogrammetric 2.5D surface models depicting a scene from different cardinal directions. One approach evaluates the 3D point cloud statistically in order to extract the hull of structures, while the other approach makes use of salient line segments in 2.5D surface models, so that the hull of 3D structures can be recovered. With orders of magnitudes more analyzed 3D points, the point cloud based approach is an order of magnitude more accurate for the synthetic dataset compared to the lower dimensioned, but therefor orders of magnitude faster, image processing based approach. For real world data the difference in accuracy between both approaches is not significant anymore. In both cases the reconstructed polyhedra supply information about their inherent semantic and can be used for subsequent and more differentiated semantic annotations through exploitation of texture information.

  11. 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.

  12. 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.

  13. Non-rigid registration of 3D point clouds under isometric deformation

    NASA Astrophysics Data System (ADS)

    Ge, Xuming

    2016-11-01

    An algorithm for pairwise non-rigid registration of 3D point clouds is presented in the specific context of isometric deformations. The critical step is registration of point clouds at different epochs captured from an isometric deformation surface within overlapping regions. Based on characteristics invariant under isometric deformation, a variant of the four-point congruent sets algorithm is applied to generate correspondences between two deformed point clouds, and subsequently a RANSAC framework is used to complete cluster extraction in preparation for global optimal alignment. Examples are presented and the results compared with existing approaches to demonstrate the two main contributions of the technique: a success rate for generating true correspondences of 90% and a root mean square error after final registration of 2-3 mm.

  14. Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

    PubMed Central

    Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Sim, Sungdae

    2014-01-01

    A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame. PMID:25093204

  15. 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.

  16. 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.

  17. Quality of 3d Point Clouds from Highly Overlapping Uav Imagery

    NASA Astrophysics Data System (ADS)

    Haala, N.; Cramer, M.; Rothermel, M.

    2013-08-01

    UAVs are becoming standard platforms for photogrammetric data capture especially while aiming at large scale aerial mapping for areas of limited extent. Such applications especially benefit from the very reasonable price of a small light UAS including control system and standard consumer grade digital camera, which is some orders of magnitude lower compared to digital photogrammetric systems. Within the paper the capability of UAV-based data collection will be evaluated for two different consumer camera systems and compared to an aerial survey with a state-of-the-art digital airborne camera system. During this evaluation, the quality of 3D point clouds generated by dense multiple image matching will be used as a benchmark. Also due to recent software developments such point clouds can be generated at a resolution similar to the ground sampling distance of the available imagery and are used for an increasing number of applications. Usually, image matching benefits from the good images quality as provided from digital airborne camera systems, which is frequently not available from the low-cost sensor components used for UAV image collection. Within the paper an investigation on UAV-based 3D data capture will be presented. For this purpose dense 3D point clouds are generated for a test area from three different platforms: first a UAV with a light weight compact camera, second a system using a system camera and finally a medium-format airborne digital camera system. Despite the considerable differences in system costs, suitable results can be derived from all data, especially if large redundancy is available such highly overlapping image blocks are not only beneficial during georeferencing, but are especially advantageous while aiming at a dense and accurate image based 3D surface reconstruction.

  18. 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

  19. Automatic extraction of discontinuity orientation from rock mass surface 3D point cloud

    NASA Astrophysics Data System (ADS)

    Chen, Jianqin; Zhu, Hehua; Li, Xiaojun

    2016-10-01

    This paper presents a new method for extracting discontinuity orientation automatically from rock mass surface 3D point cloud. The proposed method consists of four steps: (1) automatic grouping of discontinuity sets using an improved K-means clustering method, (2) discontinuity segmentation and optimization, (3) discontinuity plane fitting using Random Sample Consensus (RANSAC) method, and (4) coordinate transformation of discontinuity plane. The method is first validated by the point cloud of a small piece of a rock slope acquired by photogrammetry. The extracted discontinuity orientations are compared with measured ones in the field. Then it is applied to a publicly available LiDAR data of a road cut rock slope at Rockbench repository. The extracted discontinuity orientations are compared with the method proposed by Riquelme et al. (2014). The results show that the presented method is reliable and of high accuracy, and can meet the engineering needs.

  20. Parameter Estimation of Fossil Oysters from High Resolution 3D Point Cloud and Image Data

    NASA Astrophysics Data System (ADS)

    Djuricic, Ana; Harzhauser, Mathias; Dorninger, Peter; Nothegger, Clemens; Mandic, Oleg; Székely, Balázs; Molnár, Gábor; Pfeifer, Norbert

    2014-05-01

    A unique fossil oyster reef was excavated at Stetten in Lower Austria, which is also the highlight of the geo-edutainment park 'Fossilienwelt Weinviertel'. It provides the rare opportunity to study the Early Miocene flora and fauna of the Central Paratethys Sea. The site presents the world's largest fossil oyster biostrome formed about 16.5 million years ago in a tropical estuary of the Korneuburg Basin. About 15,000 up to 80-cm-long shells of Crassostrea gryphoides cover a 400 m2 large area. Our project 'Smart-Geology for the World's largest fossil oyster reef' combines methods of photogrammetry, geology and paleontology to document, evaluate and quantify the shell bed. This interdisciplinary approach will be applied to test hypotheses on the genesis of the taphocenosis (e.g.: tsunami versus major storm) and to reconstruct pre- and post-event processes. Hence, we are focusing on using visualization technologies from photogrammetry in geology and paleontology in order to develop new methods for automatic and objective evaluation of 3D point clouds. These will be studied on the basis of a very dense surface reconstruction of the oyster reef. 'Smart Geology', as extension of the classic discipline, exploits massive data, automatic interpretation, and visualization. Photogrammetry provides the tools for surface acquisition and objective, automated interpretation. We also want to stress the economic aspect of using automatic shape detection in paleontology, which saves manpower and increases efficiency during the monitoring and evaluation process. Currently, there are many well known algorithms for 3D shape detection of certain objects. We are using dense 3D laser scanning data from an instrument utilizing the phase shift measuring principle, which provides accurate geometrical basis < 3 mm. However, the situation is difficult in this multiple object scenario where more than 15,000 complete or fragmentary parts of an object with random orientation are found. The goal

  1. Deriving 3d Point Clouds from Terrestrial Photographs - Comparison of Different Sensors and Software

    NASA Astrophysics Data System (ADS)

    Niederheiser, Robert; Mokroš, Martin; Lange, Julia; Petschko, Helene; Prasicek, Günther; Oude Elberink, Sander

    2016-06-01

    Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives. We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (1.2.0.286), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud. While PhotoScan and Pix4D offer the user-friendliest workflows, they are also "black-box" programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.

  2. PointCloudExplore 2: Visual exploration of 3D gene expression

    SciTech Connect

    International Research Training Group Visualization of Large and Unstructured Data Sets, University of Kaiserslautern, Germany; Institute for Data Analysis and Visualization, University of California, Davis, CA; Computational Research Division, Lawrence Berkeley National Laboratory , Berkeley, CA; Genomics Division, LBNL; Computer Science Department, University of California, Irvine, CA; Computer Science Division,University of California, Berkeley, CA; Life Sciences Division, LBNL; Department of Molecular and Cellular Biology and the Center for Integrative Genomics, University of California, Berkeley, CA; Ruebel, Oliver; Rubel, Oliver; Weber, Gunther H.; Huang, Min-Yu; Bethel, E. Wes; Keranen, Soile V.E.; Fowlkes, Charless C.; Hendriks, Cris L. Luengo; DePace, Angela H.; Simirenko, L.; Eisen, Michael B.; Biggin, Mark D.; Hagen, Hand; Malik, Jitendra; Knowles, David W.; Hamann, Bernd

    2008-03-31

    To better understand how developmental regulatory networks are defined inthe genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP)has developed a suite of methods to describe 3D gene expression data, i.e.,the output of the network at cellular resolution for multiple time points. To allow researchersto explore these novel data sets we have developed PointCloudXplore (PCX).In PCX we have linked physical and information visualization views via the concept ofbrushing (cell selection). For each view dedicated operations for performing selectionof cells are available. In PCX, all cell selections are stored in a central managementsystem. Cells selected in one view can in this way be highlighted in any view allowingfurther cell subset properties to be determined. Complex cell queries can be definedby combining different cell selections using logical operations such as AND, OR, andNOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), thelatest publicly available version of PCX. PCX2 has shown to be an effective tool forvisual exploration of 3D gene expression data. We discuss (i) all views available inPCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture ofPCX2., and (iv) illustrate the usefulness of PCX2 using selected examples.

  3. 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.

  4. 3D registration method based on scattered point cloud from B-model ultrasound image

    NASA Astrophysics Data System (ADS)

    Hu, Lei; Xu, Xiaojun; Wang, Lifeng; Guo, Na; Xie, Feng

    2017-01-01

    The paper proposes a registration method on 3D point cloud of the bone tissue surface extracted by B-mode ultrasound image and the CT model . The B-mode ultrasound is used to get two-dimensional images of the femur tissue . The binocular stereo vision tracker is used to obtain spatial position and orientation of the optical positioning device fixed on the ultrasound probe. The combining of the two kind of data generates 3D point cloud of the bone tissue surface. The pixel coordinates of the bone surface are automatically obtained from ultrasound image using an improved local phase symmetry (phase symmetry, PS) . The mapping of the pixel coordinates on the ultrasound image and 3D space is obtained through a series of calibration methods. In order to detect the effect of registration, six markers are implanted on a complete fresh pig femoral .The actual coordinates of the marks are measured with two methods. The first method is to get the coordinates with measuring tools under a coordinate system. The second is to measure the coordinates of the markers in the CT model registered with 3D point cloud using the ICP registration algorithm under the same coordinate system. Ten registration experiments are carried out in the same way. Error results are obtained by comparing the two sets of mark point coordinates obtained by two different methods. The results is that a minimum error is 1.34mm, the maximum error is 3.22mm,and the average error of 2.52mm; ICP registration algorithm calculates the average error of 0.89mm and a standard deviation of 0.62mm.This evaluation standards of registration accuracy is different from the average error obtained by the ICP registration algorithm. It can be intuitive to show the error caused by the operation of clinical doctors. Reference to the accuracy requirements of different operation in the Department of orthopedics, the method can be apply to the bone reduction and the anterior cruciate ligament surgery.

  5. An adaptive learning approach for 3-D surface reconstruction from point clouds.

    PubMed

    Junior, Agostinho de Medeiros Brito; Neto, Adrião Duarte Dória; de Melo, Jorge Dantas; Goncalves, Luiz Marcos Garcia

    2008-06-01

    In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.

  6. 3D Modeling of Building Indoor Spaces and Closed Doors from Imagery and Point Clouds

    PubMed Central

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

    2015-01-01

    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. PMID:25654723

  7. 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.

  8. 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.

  9. Biview Learning for Human Posture Segmentation from 3D Points Cloud

    PubMed Central

    Qiao, Maoying; Cheng, Jun; Bian, Wei; Tao, Dacheng

    2014-01-01

    Posture segmentation plays an essential role in human motion analysis. The state-of-the-art method extracts sufficiently high-dimensional features from 3D depth images for each 3D point and learns an efficient body part classifier. However, high-dimensional features are memory-consuming and difficult to handle on large-scale training dataset. In this paper, we propose an efficient two-stage dimension reduction scheme, termed biview learning, to encode two independent views which are depth-difference features (DDF) and relative position features (RPF). Biview learning explores the complementary property of DDF and RPF, and uses two stages to learn a compact yet comprehensive low-dimensional feature space for posture segmentation. In the first stage, discriminative locality alignment (DLA) is applied to the high-dimensional DDF to learn a discriminative low-dimensional representation. In the second stage, canonical correlation analysis (CCA) is used to explore the complementary property of RPF and the dimensionality reduced DDF. Finally, we train a support vector machine (SVM) over the output of CCA. We carefully validate the effectiveness of DLA and CCA utilized in the two-stage scheme on our 3D human points cloud dataset. Experimental results show that the proposed biview learning scheme significantly outperforms the state-of-the-art method for human posture segmentation. PMID:24465721

  10. Biview learning for human posture segmentation from 3D points cloud.

    PubMed

    Qiao, Maoying; Cheng, Jun; Bian, Wei; Tao, Dacheng

    2014-01-01

    Posture segmentation plays an essential role in human motion analysis. The state-of-the-art method extracts sufficiently high-dimensional features from 3D depth images for each 3D point and learns an efficient body part classifier. However, high-dimensional features are memory-consuming and difficult to handle on large-scale training dataset. In this paper, we propose an efficient two-stage dimension reduction scheme, termed biview learning, to encode two independent views which are depth-difference features (DDF) and relative position features (RPF). Biview learning explores the complementary property of DDF and RPF, and uses two stages to learn a compact yet comprehensive low-dimensional feature space for posture segmentation. In the first stage, discriminative locality alignment (DLA) is applied to the high-dimensional DDF to learn a discriminative low-dimensional representation. In the second stage, canonical correlation analysis (CCA) is used to explore the complementary property of RPF and the dimensionality reduced DDF. Finally, we train a support vector machine (SVM) over the output of CCA. We carefully validate the effectiveness of DLA and CCA utilized in the two-stage scheme on our 3D human points cloud dataset. Experimental results show that the proposed biview learning scheme significantly outperforms the state-of-the-art method for human posture segmentation.

  11. PointCloudXplore: a visualization tool for 3D gene expressiondata

    SciTech Connect

    Rubel, Oliver; Weber, Gunther H.; Keranen, Soile V.E.; Fowlkes,Charles C.; Luengo Hendriks, Cristian L.; Simirenko, Lisa; Shah, NameetaY.; Eisen, Michael B.; Biggn, Mark D.; Hagen, Hans; Sudar, Damir J.; Malik, Jitendra; Knowles, David W.; Hamann, Bernd

    2006-10-01

    The Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of three-dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos, aiming at a more in-depth understanding of gene regulatory networks. We describe a new tool, called PointCloudXplore (PCX), that supports effective 3D gene expression data exploration. PCX is a visualization tool that uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PCX, physical views of the data are linked to abstract data displays such as parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within an embryo. Abstract gene expression data displays on the other hand allow for an analysis of relationships between different genes directly in the gene expression space. We discuss on parallel coordinates as one example abstract data view currently available in PCX. We have developed several extensions to standard parallel coordinates to facilitate brushing and the visualization of 3D gene expression data.

  12. Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping

    PubMed Central

    2013-01-01

    Background Laserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization. Results A surface feature histogram based approach from the field of robotics was adapted to close-up laserscans of plants. Local geometric point features describe class characteristics, which were used to distinguish among different plant organs. This approach has been proven and tested on several plant species. Grapevine stems and leaves were classified with an accuracy of up to 98%. The proposed method was successfully transferred to 3D-laserscans of wheat plants for yield estimation. Wheat ears were separated with an accuracy of 96% from other plant organs. Subsequently, the ear volume was calculated and correlated to the ear weight, the kernel weights and the number of kernels. Furthermore the impact of the data resolution was evaluated considering point to point distances between 0.3 and 4.0 mm with respect to the classification accuracy. Conclusion We introduced an approach using surface feature histograms for automated plant organ parameterization. Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained. This approach was found to be independent of the point to point distance and applicable to multiple plant species. Its reliability, flexibility and its high order of automation make this method well suited for the demands of

  13. 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.

  14. 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.

  15. Feature extraction from 3D lidar point clouds using image processing methods

    NASA Astrophysics Data System (ADS)

    Zhu, Ling; Shortridge, Ashton; Lusch, David; Shi, Ruoming

    2011-10-01

    Airborne LiDAR data have become cost-effective to produce at local and regional scales across the United States and internationally. These data are typically collected and processed into surface data products by contractors for state and local communities. Current algorithms for advanced processing of LiDAR point cloud data are normally implemented in specialized, expensive software that is not available for many users, and these users are therefore unable to experiment with the LiDAR point cloud data directly for extracting desired feature classes. The objective of this research is to identify and assess automated, readily implementable GIS procedures to extract features like buildings, vegetated areas, parking lots and roads from LiDAR data using standard image processing tools, as such tools are relatively mature with many effective classification methods. The final procedure adopted employs four distinct stages. First, interpolation is used to transfer the 3D points to a high-resolution raster. Raster grids of both height and intensity are generated. Second, multiple raster maps - a normalized surface model (nDSM), difference of returns, slope, and the LiDAR intensity map - are conflated to generate a multi-channel image. Third, a feature space of this image is created. Finally, supervised classification on the feature space is implemented. The approach is demonstrated in both a conceptual model and on a complex real-world case study, and its strengths and limitations are addressed.

  16. A continuous surface reconstruction method on point cloud captured from a 3D surface photogrammetry system

    SciTech Connect

    Liu, Wenyang; Cheung, Yam; Sabouri, Pouya; Arai, Tatsuya J.; Sawant, Amit; Ruan, Dan

    2015-11-15

    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.

  17. 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

    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

  18. 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.

  19. Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features

    PubMed Central

    Lu, Min; Guo, Yulan; Zhang, Jun; Ma, Yanxin; Lei, Yinjie

    2014-01-01

    Recognizing 3D objects from point clouds in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we present a coarse-to-fine 3D object recognition algorithm. During the phase of offline training, each model is represented with a set of multi-scale local surface features. During the phase of online recognition, a set of keypoints are first detected from each scene. The local surfaces around these keypoints are further encoded with multi-scale feature descriptors. These scene features are then matched against all model features to generate recognition hypotheses, which include model hypotheses and pose hypotheses. Finally, these hypotheses are verified to produce recognition results. The proposed algorithm was tested on two standard datasets, with rigorous comparisons to the state-of-the-art algorithms. Experimental results show that our algorithm was fully automatic and highly effective. It was also very robust to occlusion and clutter. It achieved the best recognition performance on all of these datasets, showing its superiority compared to existing algorithms. PMID:25517694

  20. Recognizing objects in 3D point clouds with multi-scale local features.

    PubMed

    Lu, Min; Guo, Yulan; Zhang, Jun; Ma, Yanxin; Lei, Yinjie

    2014-12-15

    Recognizing 3D objects from point clouds in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we present a coarse-to-fine 3D object recognition algorithm. During the phase of offline training, each model is represented with a set of multi-scale local surface features. During the phase of online recognition, a set of keypoints are first detected from each scene. The local surfaces around these keypoints are further encoded with multi-scale feature descriptors. These scene features are then matched against all model features to generate recognition hypotheses, which include model hypotheses and pose hypotheses. Finally, these hypotheses are verified to produce recognition results. The proposed algorithm was tested on two standard datasets, with rigorous comparisons to the state-of-the-art algorithms. Experimental results show that our algorithm was fully automatic and highly effective. It was also very robust to occlusion and clutter. It achieved the best recognition performance on all of these datasets, showing its superiority compared to existing algorithms.

  1. Evaluation of a 3D point cloud tetrahedral tomographic reconstruction method

    PubMed Central

    Pereira, N F; Sitek, A

    2011-01-01

    Tomographic reconstruction on an irregular grid may be superior to reconstruction on a regular grid. This is achieved through an appropriate choice of the image space model, the selection of an optimal set of points and the use of any available prior information during the reconstruction process. Accordingly, a number of reconstruction-related parameters must be optimized for best performance. In this work, a 3D point cloud tetrahedral mesh reconstruction method is evaluated for quantitative tasks. A linear image model is employed to obtain the reconstruction system matrix and five point generation strategies are studied. The evaluation is performed using the recovery coefficient, as well as voxel- and template-based estimates of bias and variance measures, computed over specific regions in the reconstructed image. A similar analysis is performed for regular grid reconstructions that use voxel basis functions. The maximum likelihood expectation maximization reconstruction algorithm is used. For the tetrahedral reconstructions, of the five point generation methods that are evaluated, three use image priors. For evaluation purposes, an object consisting of overlapping spheres with varying activity is simulated. The exact parallel projection data of this object are obtained analytically using a parallel projector, and multiple Poisson noise realizations of these exact data are generated and reconstructed using the different point generation strategies. The unconstrained nature of point placement in some of the irregular mesh-based reconstruction strategies has superior activity recovery for small, low-contrast image regions. The results show that, with an appropriately generated set of mesh points, the irregular grid reconstruction methods can out-perform reconstructions on a regular grid for mathematical phantoms, in terms of the performance measures evaluated. PMID:20736496

  2. 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

  3. Attribute-based point cloud visualization in support of 3-D classification

    NASA Astrophysics Data System (ADS)

    Zlinszky, András; Otepka, Johannes; Kania, Adam

    2016-04-01

    Despite the rich information available in LIDAR point attributes through full waveform recording, radiometric calibration and advanced texture metrics, LIDAR-based classification is mostly done in the raster domain. Point-based analyses such as noise removal or terrain filtering are often carried out without visual investigation of the point cloud attributes used. This is because point cloud visualization software usually handle only a limited number of pre-defined point attributes and only allow colorizing the point cloud with one of these at a time. Meanwhile, point cloud classification is rapidly evolving, and uses not only the individual attributes but combinations of these. In order to understand input data and output results better, more advanced methods for visualization are needed. Here we propose an algorithm of the OPALS software package that handles visualization of the point cloud together with its attributes. The algorithm is based on the .odm (OPALS data manager) file format that efficiently handles a large number of pre-defined point attributes and also allows the user to generate new ones. Attributes of interest can be visualized individually, by applying predefined or user-generated palettes in a simple .xml format. The colours of the palette are assigned to the points by setting the respective Red, Green and Blue attributes of the point to result in the colour pre-defined by the palette for the corresponding attribute value. The algorithm handles scaling and histogram equalization based on the distribution of the point attribute to be considered. Additionally, combinations of attributes can be visualized based on RBG colour mixing. The output dataset can be in any standard format where RGB attributes are supported and visualized with conventional point cloud viewing software. Viewing the point cloud together with its attributes allows efficient selection of filter settings and classification parameters. For already classified point clouds, a large

  4. Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds

    NASA Astrophysics Data System (ADS)

    Yang, Bisheng; Fang, Lina; Li, Jonathan

    2013-05-01

    Accurate 3D road information is important for applications such as road maintenance and virtual 3D modeling. Mobile laser scanning (MLS) is an efficient technique for capturing dense point clouds that can be used to construct detailed road models for large areas. This paper presents a method for extracting and delineating roads from large-scale MLS point clouds. The proposed method partitions MLS point clouds into a set of consecutive "scanning lines", which each consists of a road cross section. A moving window operator is used to filter out non-ground points line by line, and curb points are detected based on curb patterns. The detected curb points are tracked and refined so that they are both globally consistent and locally similar. To evaluate the validity of the proposed method, experiments were conducted using two types of street-scene point clouds captured by Optech's Lynx Mobile Mapper System. The completeness, correctness, and quality of the extracted roads are over 94.42%, 91.13%, and 91.3%, respectively, which proves the proposed method is a promising solution for extracting 3D roads from MLS point clouds.

  5. Knowledge guided object detection and identification in 3D point clouds

    NASA Astrophysics Data System (ADS)

    Karmacharya, A.; Boochs, F.; Tietz, B.

    2015-05-01

    Modern instruments like laser scanner and 3D cameras or image based techniques like structure from motion produce huge point clouds as base for further object analysis. This has considerably changed the way of data compilation away from selective manually guided processes towards automatic and computer supported strategies. However it's still a long way to achieve the quality and robustness of manual processes as data sets are mostly very complex. Looking at existing strategies 3D data processing for object detections and reconstruction rely heavily on either data driven or model driven approaches. These approaches come with their limitation on depending highly on the nature of data and inability to handle any deviation. Furthermore, the lack of capabilities to integrate other data or information in between the processing steps further exposes their limitations. This restricts the approaches to be executed with strict predefined strategy and does not allow deviations when and if new unexpected situations arise. We propose a solution that induces intelligence in the processing activities through the usage of semantics. The solution binds the objects along with other related knowledge domains to the numerical processing to facilitate the detection of geometries and then uses experts' inference rules to annotate them. The solution was tested within the prototypical application of the research project "Wissensbasierte Detektion von Objekten in Punktwolken für Anwendungen im Ingenieurbereich (WiDOP)". The flexibility of the solution is demonstrated through two entirely different USE Case scenarios: Deutsche Bahn (German Railway System) for the outdoor scenarios and Fraport (Frankfort Airport) for the indoor scenarios. Apart from the difference in their environments, they provide different conditions, which the solution needs to consider. While locations of the objects in Fraport were previously known, that of DB were not known at the beginning.

  6. 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

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

    PubMed

    Wang, Yunsheng; Weinacker, Holger; Koch, Barbara

    2008-06-12

    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.

  8. A closed-form expression of the positional uncertainty for 3D point clouds.

    PubMed

    Bae, Kwang-Ho; Belton, David; Lichti, Derek D

    2009-04-01

    We present a novel closed-form expression of positional uncertainty measured by a near-monostatic and time-of-flight laser range finder with consideration of its measurement uncertainties. An explicit form of the angular variance of the estimated surface normal vector is also derived. This expression is useful for the precise estimation of the surface normal vector and the outlier detection for finding correspondence in order to register multiple three-dimensional point clouds. Two practical algorithms using these expressions are presented: a method for finding optimal local neighbourhood size which minimizes the variance of the estimated normal vector and a resampling method of point clouds.

  9. 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.

  10. 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

  11. A new approach for semi-automatic rock mass joints recognition from 3D point clouds

    NASA Astrophysics Data System (ADS)

    Riquelme, Adrián J.; Abellán, A.; Tomás, R.; Jaboyedoff, M.

    2014-07-01

    Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information - synthetic and 3D scanned data - were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.

  12. Evaluating the Potential of Rtk-Uav for Automatic Point Cloud Generation in 3d Rapid Mapping

    NASA Astrophysics Data System (ADS)

    Fazeli, H.; Samadzadegan, F.; Dadrasjavan, F.

    2016-06-01

    During disaster and emergency situations, 3D geospatial data can provide essential information for decision support systems. The utilization of geospatial data using digital surface models as a basic reference is mandatory to provide accurate quick emergency response in so called rapid mapping activities. The recipe between accuracy requirements and time restriction is considered critical in this situations. UAVs as alternative platforms for 3D point cloud acquisition offer potentials because of their flexibility and practicability combined with low cost implementations. Moreover, the high resolution data collected from UAV platforms have the capabilities to provide a quick overview of the disaster area. The target of this paper is to experiment and to evaluate a low-cost system for generation of point clouds using imagery collected from a low altitude small autonomous UAV equipped with customized single frequency RTK module. The customized multi-rotor platform is used in this study. Moreover, electronic hardware is used to simplify user interaction with the UAV as RTK-GPS/Camera synchronization, and beside the synchronization, lever arm calibration is done. The platform is equipped with a Sony NEX-5N, 16.1-megapixel camera as imaging sensor. The lens attached to camera is ZEISS optics, prime lens with F1.8 maximum aperture and 24 mm focal length to deliver outstanding images. All necessary calibrations are performed and flight is implemented over the area of interest at flight height of 120 m above the ground level resulted in 2.38 cm GSD. Earlier to image acquisition, 12 signalized GCPs and 20 check points were distributed in the study area and measured with dualfrequency GPS via RTK technique with horizontal accuracy of σ = 1.5 cm and vertical accuracy of σ = 2.3 cm. results of direct georeferencing are compared to these points and experimental results show that decimeter accuracy level for 3D points cloud with proposed system is achievable, that is suitable

  13. 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.

  14. 3-D earthquake surface displacements from differencing pre- and post-event LiDAR point clouds

    NASA Astrophysics Data System (ADS)

    Krishnan, A. K.; Nissen, E.; Arrowsmith, R.; Saripalli, S.

    2012-12-01

    The explosion in aerial LiDAR surveying along active faults across the western United States and elsewhere provides a high-resolution topographic baseline against which to compare repeat LiDAR datasets collected after future earthquakes. We present a new method for determining 3-D coseismic surface displacements and rotations by differencing pre- and post-earthquake LiDAR point clouds using an adaptation of the Iterative Closest Point (ICP) algorithm, a point set registration technique widely used in medical imaging, computer vision and graphics. There is no need for any gridding or smoothing of the LiDAR data and the method works well even with large mismatches in the density of the two point clouds. To explore the method's performance, we simulate pre- and post-event point clouds using real ("B4") LiDAR data on the southern San Andreas Fault perturbed with displacements of known magnitude. For input point clouds with ~2 points per square meter, we are able to reproduce displacements with a 50 m grid spacing and with horizontal and vertical accuracies of ~20 cm and ~4 cm. In the future, finer grids and improved precisions should be possible with higher shot densities and better survey geo-referencing. By capturing near-fault deformation in 3-D, LiDAR differencing with ICP will complement satellite-based techniques such as InSAR which map only certain components of the surface deformation and which often break down close to surface faulting or in areas of dense vegetation. It will be especially useful for mapping shallow fault slip and rupture zone deformation, helping inform paleoseismic studies and better constrain fault zone rheology. Because ICP can image rotations directly, the technique will also help resolve the detailed kinematics of distributed zones of faulting where block rotations may be common.

  15. A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

    PubMed Central

    Yang, Qiyao; Wang, Zhiguo; Zhang, Guoxu

    2017-01-01

    The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = −0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = −0.496, ED = 25.847) and the compared method (NC = −0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one. PMID:28316979

  16. Calibration of an outdoor distributed camera network with a 3D point cloud.

    PubMed

    Ortega, Agustín; Silva, Manuel; Teniente, Ernesto H; Ferreira, Ricardo; Bernardino, Alexandre; Gaspar, José; Andrade-Cetto, Juan

    2014-07-29

    Outdoor camera networks are becoming ubiquitous in critical urban areas of the largest cities around the world. Although current applications of camera networks are mostly tailored to video surveillance, recent research projects are exploiting their use to aid robotic systems in people-assisting tasks. Such systems require precise calibration of the internal and external parameters of the distributed camera network. Despite the fact that camera calibration has been an extensively studied topic, the development of practical methods for user-assisted calibration that minimize user intervention time and maximize precision still pose significant challenges. These camera systems have non-overlapping fields of view, are subject to environmental stress, and are likely to suffer frequent recalibration. In this paper, we propose the use of a 3D map covering the area to support the calibration process and develop an automated method that allows quick and precise calibration of a large camera network. We present two cases of study of the proposed calibration method: one is the calibration of the Barcelona Robot Lab camera network, which also includes direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms. The second case consist of improving the GPS positioning of geo-tagged images taken with a mobile device in the Facultat de Matemàtiques i Estadística (FME) patio at the Universitat Politècnica de Catalunya (UPC).

  17. Calibration of an Outdoor Distributed Camera Network with a 3D Point Cloud

    PubMed Central

    Ortega, Agustín; Silva, Manuel; Teniente, Ernesto H.; Ferreira, Ricardo; Bernardino, Alexandre; Gaspar, José; Andrade-Cetto, Juan

    2014-01-01

    Outdoor camera networks are becoming ubiquitous in critical urban areas of the largest cities around the world. Although current applications of camera networks are mostly tailored to video surveillance, recent research projects are exploiting their use to aid robotic systems in people-assisting tasks. Such systems require precise calibration of the internal and external parameters of the distributed camera network. Despite the fact that camera calibration has been an extensively studied topic, the development of practical methods for user-assisted calibration that minimize user intervention time and maximize precision still pose significant challenges. These camera systems have non-overlapping fields of view, are subject to environmental stress, and are likely to suffer frequent recalibration. In this paper, we propose the use of a 3D map covering the area to support the calibration process and develop an automated method that allows quick and precise calibration of a large camera network. We present two cases of study of the proposed calibration method: one is the calibration of the Barcelona Robot Lab camera network, which also includes direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms. The second case consist of improving the GPS positioning of geo-tagged images taken with a mobile device in the Facultat de Matemàtiques i Estadística (FME) patio at the Universitat Politècnica de Catalunya (UPC). PMID:25076221

  18. Registration of 3D point clouds and meshes: a survey from rigid to nonrigid.

    PubMed

    Tam, Gary K L; Cheng, Zhi-Quan; Lai, Yu-Kun; Langbein, Frank C; Liu, Yonghuai; Marshall, David; Martin, Ralph R; Sun, Xian-Fang; Rosin, Paul L

    2013-07-01

    Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization. Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends.

  19. A 3D clustering approach for point clouds to detect and quantify changes at a rock glacier front

    NASA Astrophysics Data System (ADS)

    Micheletti, Natan; Tonini, Marj; Lane, Stuart N.

    2016-04-01

    Terrestrial Laser Scanners (TLS) are extensively used in geomorphology to remotely-sense landforms and surfaces of any type and to derive digital elevation models (DEMs). Modern devices are able to collect many millions of points, so that working on the resulting dataset is often troublesome in terms of computational efforts. Indeed, it is not unusual that raw point clouds are filtered prior to DEM creation, so that only a subset of points is retained and the interpolation process becomes less of a burden. Whilst this procedure is in many cases necessary, it implicates a considerable loss of valuable information. First, and even without eliminating points, the common interpolation of points to a regular grid causes a loss of potentially useful detail. Second, it inevitably causes the transition from 3D information to only 2.5D data where each (x,y) pair must have a unique z-value. Vector-based DEMs (e.g. triangulated irregular networks) partially mitigate these issues, but still require a set of parameters to be set and a considerable burden in terms of calculation and storage. Because of the reasons above, being able to perform geomorphological research directly on point clouds would be profitable. Here, we propose an approach to identify erosion and deposition patterns on a very active rock glacier front in the Swiss Alps to monitor sediment dynamics. The general aim is to set up a semiautomatic method to isolate mass movements using 3D-feature identification directly from LiDAR data. An ultra-long range LiDAR RIEGL VZ-6000 scanner was employed to acquire point clouds during three consecutive summers. In order to isolate single clusters of erosion and deposition we applied the Density-Based Scan Algorithm with Noise (DBSCAN), previously successfully employed by Tonini and Abellan (2014) in a similar case for rockfall detection. DBSCAN requires two input parameters, strongly influencing the number, shape and size of the detected clusters: the minimum number of

  20. 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

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

    PubMed

    Dorninger, Peter; Pfeifer, Norbert

    2008-11-17

    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.

  2. On the Estimation of Forest Resources Using 3D Remote Sensing Techniques and Point Cloud Data

    NASA Astrophysics Data System (ADS)

    Karjalainen, Mika; Karila, Kirsi; Liang, Xinlian; Yu, Xiaowei; Huang, Guoman; Lu, Lijun

    2016-08-01

    In recent years, 3D capable remote sensing techniques have shown great potential in forest biomass estimation because of their ability to measure the forest canopy structure, tree height and density. The objective of the Dragon3 forest resources research project (ID 10667) and the supporting ESA young scientist project (ESA contract NO. 4000109483/13/I-BG) was to study the use of satellite based 3D techniques in forest tree height estimation, and consequently in forest biomass and biomass change estimation, by combining satellite data with terrestrial measurements. Results from airborne 3D techniques were also used in the project. Even though, forest tree height can be estimated from 3D satellite SAR data to some extent, there is need for field reference plots. For this reason, we have also been developing automated field plot measurement techniques based on Terrestrial Laser Scanning data, which can be used to train and calibrate satellite based estimation models. In this paper, results of canopy height models created from TerraSAR-X stereo and TanDEM-X INSAR data are shown as well as preliminary results from TLS field plot measurement system. Also, results from the airborne CASMSAR system to measure forest canopy height from P- and X- band INSAR are presented.

  3. Recent advances in analysis and prediction of Rock Falls, Rock Slides, and Rock Avalanches using 3D point clouds

    NASA Astrophysics Data System (ADS)

    Abellan, A.; Carrea, D.; Jaboyedoff, M.; Riquelme, A.; Tomas, R.; Royan, M. J.; Vilaplana, J. M.; Gauvin, N.

    2014-12-01

    The acquisition of dense terrain information using well-established 3D techniques (e.g. LiDAR, photogrammetry) and the use of new mobile platforms (e.g. Unmanned Aerial Vehicles) together with the increasingly efficient post-processing workflows for image treatment (e.g. Structure From Motion) are opening up new possibilities for analysing, modeling and predicting rock slope failures. Examples of applications at different scales ranging from the monitoring of small changes at unprecedented level of detail (e.g. sub millimeter-scale deformation under lab-scale conditions) to the detection of slope deformation at regional scale. In this communication we will show the main accomplishments of the Swiss National Foundation project "Characterizing and analysing 3D temporal slope evolution" carried out at Risk Analysis group (Univ. of Lausanne) in close collaboration with the RISKNAT and INTERES groups (Univ. of Barcelona and Univ. of Alicante, respectively). We have recently developed a series of innovative approaches for rock slope analysis using 3D point clouds, some examples include: the development of semi-automatic methodologies for the identification and extraction of rock-slope features such as discontinuities, type of material, rockfalls occurrence and deformation. Moreover, we have been improving our knowledge in progressive rupture characterization thanks to several algorithms, some examples include the computing of 3D deformation, the use of filtering techniques on permanently based TLS, the use of rock slope failure analogies at different scales (laboratory simulations, monitoring at glacier's front, etc.), the modelling of the influence of external forces such as precipitation on the acceleration of the deformation rate, etc. We have also been interested on the analysis of rock slope deformation prior to the occurrence of fragmental rockfalls and the interaction of this deformation with the spatial location of future events. In spite of these recent advances

  4. Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data

    NASA Astrophysics Data System (ADS)

    Dittrich, André; Weinmann, Martin; Hinz, Stefan

    2017-04-01

    In photogrammetry, remote sensing, computer vision and robotics, a topic of major interest is represented by the automatic analysis of 3D point cloud data. This task often relies on the use of geometric features amongst which particularly the ones derived from the eigenvalues of the 3D structure tensor (e.g. the three dimensionality features of linearity, planarity and sphericity) have proven to be descriptive and are therefore commonly involved for classification tasks. Although these geometric features are meanwhile considered as standard, very little attention has been paid to their accuracy and robustness. In this paper, we hence focus on the influence of discretization and noise on the most commonly used geometric features. More specifically, we investigate the accuracy and robustness of the eigenvalues of the 3D structure tensor and also of the features derived from these eigenvalues. Thereby, we provide both analytical and numerical considerations which clearly reveal that certain features are more susceptible to discretization and noise whereas others are more robust.

  5. a Semi-Automated Point Cloud Processing Methodology for 3d Cultural Heritage Documentation

    NASA Astrophysics Data System (ADS)

    Kıvılcım, C. Ö.; Duran, Z.

    2016-06-01

    The preliminary phase in any architectural heritage project is to obtain metric measurements and documentation of the building and its individual elements. On the other hand, conventional measurement techniques require tremendous resources and lengthy project completion times for architectural surveys and 3D model production. Over the past two decades, the widespread use of laser scanning and digital photogrammetry have significantly altered the heritage documentation process. Furthermore, advances in these technologies have enabled robust data collection and reduced user workload for generating various levels of products, from single buildings to expansive cityscapes. More recently, the use of procedural modelling methods and BIM relevant applications for historic building documentation purposes has become an active area of research, however fully automated systems in cultural heritage documentation still remains open. In this paper, we present a semi-automated methodology, for 3D façade modelling of cultural heritage assets based on parametric and procedural modelling techniques and using airborne and terrestrial laser scanning data. We present the contribution of our methodology, which we implemented in an open source software environment using the example project of a 16th century early classical era Ottoman structure, Sinan the Architect's Şehzade Mosque in Istanbul, Turkey.

  6. Historical Buildings Models and Their Handling via 3d Survey: from Points Clouds to User-Oriented Hbim

    NASA Astrophysics Data System (ADS)

    Chiabrando, F.; Sammartano, G.; Spanò, A.

    2016-06-01

    This paper retraces some research activities and application of 3D survey techniques and Building Information Modelling (BIM) in the environment of Cultural Heritage. It describes the diffusion of as-built BIM approach in the last years in Heritage Assets management, the so-called Built Heritage Information Modelling/Management (BHIMM or HBIM), that is nowadays an important and sustainable perspective in documentation and administration of historic buildings and structures. The work focuses the documentation derived from 3D survey techniques that can be understood like a significant and unavoidable knowledge base for the BIM conception and modelling, in the perspective of a coherent and complete management and valorisation of CH. It deepens potentialities, offered by 3D integrated survey techniques, to acquire productively and quite easilymany 3D information, not only geometrical but also radiometric attributes, helping the recognition, interpretation and characterization of state of conservation and degradation of architectural elements. From these data, they provide more and more high descriptive models corresponding to the geometrical complexity of buildings or aggregates in the well-known 5D (3D + time and cost dimensions). Points clouds derived from 3D survey acquisition (aerial and terrestrial photogrammetry, LiDAR and their integration) are reality-based models that can be use in a semi-automatic way to manage, interpret, and moderately simplify geometrical shapes of historical buildings that are examples, as is well known, of non-regular and complex geometry, instead of modern constructions with simple and regular ones. In the paper, some of these issues are addressed and analyzed through some experiences regarding the creation and the managing of HBIMprojects on historical heritage at different scales, using different platforms and various workflow. The paper focuses on LiDAR data handling with the aim to manage and extract geometrical information; on

  7. Combination of Tls Point Clouds and 3d Data from Kinect v2 Sensor to Complete Indoor Models

    NASA Astrophysics Data System (ADS)

    Lachat, E.; Landes, T.; Grussenmeyer, P.

    2016-06-01

    The combination of data coming from multiple sensors is more and more applied for remote sensing issues (multi-sensor imagery) but also in cultural heritage or robotics, since it often results in increased robustness and accuracy of the final data. In this paper, the reconstruction of building elements such as window frames or door jambs scanned thanks to a low cost 3D sensor (Kinect v2) is presented. Their combination within a global point cloud of an indoor scene acquired with a terrestrial laser scanner (TLS) is considered. If the added elements acquired with the Kinect sensor enable to reach a better level of detail of the final model, an adapted acquisition protocol may also provide several benefits as for example time gain. The paper aims at analyzing whether the two measurement techniques can be complementary in this context. The limitations encountered during the acquisition and reconstruction steps are also investigated.

  8. Incremental Refinement of FAÇADE Models with Attribute Grammar from 3d Point Clouds

    NASA Astrophysics Data System (ADS)

    Dehbi, Y.; Staat, C.; Mandtler, L.; Pl¨umer, L.

    2016-06-01

    Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on façades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

  9. See-Through Imaging of Laser-Scanned 3d Cultural Heritage Objects Based on Stochastic Rendering of Large-Scale Point Clouds

    NASA Astrophysics Data System (ADS)

    Tanaka, S.; Hasegawa, K.; Okamoto, N.; Umegaki, R.; Wang, S.; Uemura, M.; Okamoto, A.; Koyamada, K.

    2016-06-01

    We propose a method for the precise 3D see-through imaging, or transparent visualization, of the large-scale and complex point clouds acquired via the laser scanning of 3D cultural heritage objects. Our method is based on a stochastic algorithm and directly uses the 3D points, which are acquired using a laser scanner, as the rendering primitives. This method achieves the correct depth feel without requiring depth sorting of the rendering primitives along the line of sight. Eliminating this need allows us to avoid long computation times when creating natural and precise 3D see-through views of laser-scanned cultural heritage objects. The opacity of each laser-scanned object is also flexibly controllable. For a laser-scanned point cloud consisting of more than 107 or 108 3D points, the pre-processing requires only a few minutes, and the rendering can be executed at interactive frame rates. Our method enables the creation of cumulative 3D see-through images of time-series laser-scanned data. It also offers the possibility of fused visualization for observing a laser-scanned object behind a transparent high-quality photographic image placed in the 3D scene. We demonstrate the effectiveness of our method by applying it to festival floats of high cultural value. These festival floats have complex outer and inner 3D structures and are suitable for see-through imaging.

  10. Detecting and Analyzing Corrosion Spots on the Hull of Large Marine Vessels Using Colored 3d LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Aijazi, A. K.; Malaterre, L.; Tazir, M. L.; Trassoudaine, L.; Checchin, P.

    2016-06-01

    This work presents a new method that automatically detects and analyzes surface defects such as corrosion spots of different shapes and sizes, on large ship hulls. In the proposed method several scans from different positions and viewing angles around the ship are registered together to form a complete 3D point cloud. The R, G, B values associated with each scan, obtained with the help of an integrated camera are converted into HSV space to separate out the illumination invariant color component from the intensity. Using this color component, different surface defects such as corrosion spots of different shapes and sizes are automatically detected, within a selected zone, using two different methods depending upon the level of corrosion/defects. The first method relies on a histogram based distribution whereas the second on adaptive thresholds. The detected corrosion spots are then analyzed and quantified to help better plan and estimate the cost of repair and maintenance. Results are evaluated on real data using different standard evaluation metrics to demonstrate the efficacy as well as the technical strength of the proposed method.

  11. Evaluation of the Convergence Region of an Automated Registration Method for 3D Laser Scanner Point Clouds.

    PubMed

    Bae, Kwang-Ho

    2009-01-01

    Using three dimensional point clouds from both simulated and real datasets from close and terrestrial laser scanners, the rotational and translational convergence regions of Geometric Primitive Iterative Closest Points (GP-ICP) are empirically evaluated. The results demonstrate the GP-ICP has a larger rotational convergence region than the existing methods, e.g., the Iterative Closest Point (ICP).

  12. [An automatic extraction algorithm for individual tree crown projection area and volume based on 3D point cloud data].

    PubMed

    Xu, Wei-Heng; Feng, Zhong-Ke; Su, Zhi-Fang; Xu, Hui; Jiao, You-Quan; Deng, Ou

    2014-02-01

    fixed angles to estimate crown projections, and (2) different regular volume formula to simulate crown volume according to the tree crown shapes. Based on the high-resolution 3D LIDAR point cloud data of individual tree, tree crown structure was reconstructed at a high rate of speed with high accuracy, and crown projection and volume of individual tree were extracted by this automatical untouched method, which can provide a reference for tree crown structure studies and be worth to popularize in the field of precision forestry.

  13. 3D point cloud data from laser scanning along the 2014 South Napa Earthquake surface rupture, California, USA

    USGS Publications Warehouse

    DeLong, Stephen B.

    2016-01-01

    Point cloud data collected along a 500 meter portion of the 2014 South Napa Earthquake surface rupture near Cuttings Wharf Road, Napa, CA, USA. The data include 7 point cloud files (.laz). The files are named with the location and date of collection and either ALSM for airborne laser scanner data or TLS for terrestrial laser scanner data. The ALSM data re previously released but are included here because they have been precisely aligned with the TLS data as described in the processing section of this metadata. 

  14. Terrestrial laser scanning point clouds time series for the monitoring of slope movements: displacement measurement using image correlation and 3D feature tracking

    NASA Astrophysics Data System (ADS)

    Bornemann, Pierrick; Jean-Philippe, Malet; André, Stumpf; Anne, Puissant; Julien, Travelletti

    2016-04-01

    Dense multi-temporal point clouds acquired with terrestrial laser scanning (TLS) have proved useful for the study of structure and kinematics of slope movements. Most of the existing deformation analysis methods rely on the use of interpolated data. Approaches that use multiscale image correlation provide a precise and robust estimation of the observed movements; however, for non-rigid motion patterns, these methods tend to underestimate all the components of the movement. Further, for rugged surface topography, interpolated data introduce a bias and a loss of information in some local places where the point cloud information is not sufficiently dense. Those limits can be overcome by using deformation analysis exploiting directly the original 3D point clouds assuming some hypotheses on the deformation (e.g. the classic ICP algorithm requires an initial guess by the user of the expected displacement patterns). The objective of this work is therefore to propose a deformation analysis method applied to a series of 20 3D point clouds covering the period October 2007 - October 2015 at the Super-Sauze landslide (South East French Alps). The dense point clouds have been acquired with a terrestrial long-range Optech ILRIS-3D laser scanning device from the same base station. The time series are analyzed using two approaches: 1) a method of correlation of gradient images, and 2) a method of feature tracking in the raw 3D point clouds. The estimated surface displacements are then compared with GNSS surveys on reference targets. Preliminary results tend to show that the image correlation method provides a good estimation of the displacement fields at first order, but shows limitations such as the inability to track some deformation patterns, and the use of a perspective projection that does not maintain original angles and distances in the correlated images. Results obtained with 3D point clouds comparison algorithms (C2C, ICP, M3C2) bring additional information on the

  15. Documenting a Complex Modern Heritage Building Using Multi Image Close Range Photogrammetry and 3d Laser Scanned Point Clouds

    NASA Astrophysics Data System (ADS)

    Vianna Baptista, M. L.

    2013-07-01

    Integrating different technologies and expertises help fill gaps when optimizing documentation of complex buildings. Described below is the process used in the first part of a restoration project, the architectural survey of Theatre Guaira Cultural Centre in Curitiba, Brazil. To diminish time on fieldwork, the two-person-field-survey team had to juggle, during three days, the continuous artistic activities and performers' intense schedule. Both technologies (high definition laser scanning and close-range photogrammetry) were used to record all details in the least amount of time without disturbing the artists' rehearsals and performances. Laser Scanning was ideal to record the monumental stage structure with all of its existing platforms, light fixtures, scenery walls and curtains. Although scanned with high-definition, parts of the exterior façades were also recorded using Close Range Photogrammetry. Tiny cracks on the marble plaques and mosaic tiles, not visible in the point clouds, were then able to be precisely documented in order to create the exterior façades textures and damages mapping drawings. The combination of technologies and the expertise of service providers, knowing how and what to document, and what to deliver to the client, enabled maximum benefits to the following restoration project.

  16. Registration of overlapping 3D point clouds using extracted line segments. (Polish Title: Rejestracja chmur punktów 3D w oparciu o wyodrębnione krawędzie)

    NASA Astrophysics Data System (ADS)

    Poręba, M.; Goulette, F.

    2014-12-01

    The registration of 3D point clouds collected from different scanner positions is necessary in order to avoid occlusions, ensure a full coverage of areas, and collect useful data for analyzing and documenting the surrounding environment. This procedure involves three main stages: 1) choosing appropriate features, which can be reliably extracted; 2) matching conjugate primitives; 3) estimating the transformation parameters. Currently, points and spheres are most frequently chosen as the registration features. However, due to limited point cloud resolution, proper identification and precise measurement of a common point within the overlapping laser data is almost impossible. One possible solution to this problem may be a registration process based on the Iterative Closest Point (ICP) algorithm or its variation. Alternatively, planar and linear feature-based registration techniques can also be applied. In this paper, we propose the use of line segments obtained from intersecting planes modelled within individual scans. Such primitives can be easily extracted even from low-density point clouds. Working with synthetic data, several existing line-based registration methods are evaluated according to their robustness to noise and the precision of the estimated transformation parameters. For the purpose of quantitative assessment, an accuracy criterion based on a modified Hausdorff distance is defined. Since an automated matching of segments is a challenging task that influences the correctness of the transformation parameters, a correspondence-finding algorithm is developed. The tests show that our matching algorithm provides a correct p airing with an accuracy of 99 % at least, and about 8% of omitted line pairs.

  17. Disentangling the history of complex multi-phased shell beds based on the analysis of 3D point cloud data

    NASA Astrophysics Data System (ADS)

    Harzhauser, Mathias; Djuricic, Ana; Mandic, Oleg; Dorninger, Peter; Nothegger, Clemens; Székely, Balázs; Molnár, Gábor; Pfeifer, Norbert

    2015-04-01

    Shell beds are key features in sedimentary records throughout the Phanerozoic. The interplay between burial rates and population productivity is reflected in distinct degrees of shelliness. Consequently, shell beds may provide informations on various physical processes, which led to the accumulation and preservation of hard parts. Many shell beds pass through a complex history of formation being shaped by more than one factor. In shallow marine settings, the composition of shell beds is often strongly influenced by winnowing, reworking and transport. These processes may cause considerable time averaging and the accumulation of specimens, which have lived thousands of years apart. In the best case, the environment remained stable during that time span and the mixing does not mask the overall composition. A major obstacle for the interpretation of shell beds, however, is the amalgamation of shell beds of several depositional units in a single concentration, as typically for tempestites and tsunamites. Disentangling such mixed assemblages requires deep understanding of the ecological requirements of the taxa involved - which is achievable for geologically young shell beds with living relatives - and a statistic approach to quantify the contribution by the various death assemblages. Furthermore it requires understanding of sedimentary processes potentially involved into their formation. Here we present the first attempt to describe and decipher such a multi-phase shell-bed based on a high resolution digital surface model (1 mm) combined with ortho-photos with a resolution of 0.5 mm per pixel. Documenting the oyster reef requires precisely georeferenced data; owing to high redundancy of the point cloud an accuracy of a few mm was achieved. The shell accumulation covers an area of 400 m2 with thousands of specimens, which were excavated by a three months campaign at Stetten in Lower Austria. Formed in an Early Miocene estuary of the Paratethys Sea it is mainly composed

  18. 3D-Modeling of Vegetation from Lidar Point Clouds and Assessment of its Impact on Façade Solar Irradiation

    NASA Astrophysics Data System (ADS)

    Peronato, G.; Rey, E.; Andersen, M.

    2016-10-01

    The presence of vegetation can significantly affect the solar irradiation received on building surfaces. Due to the complex shape and seasonal variability of vegetation geometry, this topic has gained much attention from researchers. However, existing methods are limited to rooftops as they are based on 2.5D geometry and use simplified radiation algorithms based on view-sheds. This work contributes to overcoming some of these limitations, providing support for 3D geometry to include facades. Thanks to the use of ray-tracing-based simulations and detailed characterization of the 3D surfaces, we can also account for inter-reflections, which might have a significant impact on façade irradiation. In order to construct confidence intervals on our results, we modeled vegetation from LiDAR point clouds as 3D convex hulls, which provide the biggest volume and hence the most conservative obstruction scenario. The limits of the confidence intervals were characterized with some extreme scenarios (e.g. opaque trees and absence of trees). Results show that uncertainty can vary significantly depending on the characteristics of the urban area and the granularity of the analysis (sensor, building and group of buildings). We argue that this method can give us a better understanding of the uncertainties due to vegetation in the assessment of solar irradiation in urban environments, and therefore, the potential for the installation of solar energy systems.

  19. 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.

  20. Extensible 3D (X3D) Graphics Clouds for Geographic Information Systems

    DTIC Science & Technology

    2008-03-01

    browser such as Microsoft Internet Explorer or Netscape using an X3D or VRML supporting plug-in. The benefits of diverse support can cause...typing model output with a particular method of 3D cloud production. Data-driven adaptation and production of cloud models for web -based delivery...and production of cloud models for web -based delivery is an achievable capability given continued research and development. vi THIS PAGE

  1. Comparison of UAV-Enabled Photogrammetry-Based 3D Point Clouds and Interpolated DSMs of Sloping Terrain for Rockfall Hazard Analysis

    NASA Astrophysics Data System (ADS)

    Manousakis, J.; Zekkos, D.; Saroglou, F.; Clark, M.

    2016-10-01

    UAVs are expected to be particularly valuable to define topography for natural slopes that may be prone to geological hazards, such as landslides or rockfalls. UAV-enabled imagery and aerial mapping can lead to fast and accurate qualitative and quantitative results for photo documentation as well as basemap 3D analysis that can be used for geotechnical stability analyses. In this contribution, the case study of a rockfall near Ponti village that was triggered during the November 17th 2015 Mw 6.5 earthquake in Lefkada, Greece is presented with a focus on feature recognition and 3D terrain model development for use in rockfall hazard analysis. A significant advantage of the UAV was the ability to identify from aerial views the rockfall trajectory along the terrain, the accuracy of which is crucial to subsequent geotechnical back-analysis. Fast static GPS control points were measured for optimizing internal and external camera parameters and model georeferencing. Emphasis is given on an assessment of the error associated with the basemap when fewer and poorly distributed ground control points are available. Results indicate that spatial distribution and image occurrences of control points throughout the mapped area and image block is essential in order to produce accurate geospatial data with minimum distortions.

  2. 3D reconstruction of tropospheric cirrus clouds by stereovision system

    NASA Astrophysics Data System (ADS)

    Nadjib Kouahla, Mohamed; Moreels, Guy; Seridi, Hamid

    2016-07-01

    A stereo imaging method is applied to measure the altitude of cirrus clouds and provide a 3D map of the altitude of the layer centroid. They are located in the high troposphere and, sometimes in the lower stratosphere, between 6 and 10 km high. Two simultaneous images of the same scene are taken with Canon cameras (400D) in two sites distant of 37 Km. Each image processed in order to invert the perspective effect and provide a satellite-type view of the layer. Pairs of matched points that correspond to a physical emissive point in the common area are identified in calculating a correlation coefficient (ZNCC: Zero mean Normalized Cross-correlation or ZSSD: as Zero mean Sum of Squared Differences). This method is suitable for obtaining 3D representations in the case of low-contrast objects. An observational campaign was conducted in June 2014 in France. The images were taken simultaneously at Marnay (47°17'31.5" N, 5°44'58.8" E; altitude 275 m) 25 km northwest of Besancon and in Mont poupet (46°58'31.5" N, 5°52'22.7" E; altitude 600 m) southwest of Besancon at 43 km. 3D maps of the Natural cirrus clouds and artificial like "aircraft trails" are retrieved. They are compared with pseudo-relief intensity maps of the same region. The mean altitude of the cirrus barycenter is located at 8.5 ± 1km on June 11.

  3. Comparing Point Clouds

    DTIC Science & Technology

    2004-04-01

    Point clouds are one of the most primitive and fundamental surface representations. A popular source of point clouds are three dimensional shape...acquisition devices such as laser range scanners. Another important field where point clouds are found is in the representation of high-dimensional...framework for comparing manifolds given by point clouds is presented in this paper. The underlying theory is based on Gromov-Hausdorff distances, leading

  4. 3D reconstruction of tropospheric cirrus clouds

    NASA Astrophysics Data System (ADS)

    Kouahla, M. N.; Faivre, M.; Moreels, G.; Seridi, H.

    2016-10-01

    In this paper, we present a series of results from stereo-imagery of cirrus clouds in the troposphere. These clouds are either of natural origin or are created by aircraft exhausts. They are presently considered to be a major cause for the climate change. Two observation campaigns were conducted in France in 2013 and 2014. The observing sites were located in Marnay (47°17‧31.5″ N, 5°44‧58.8″ E; altitude 275 m) and in Mont Poupet (46°58‧31.5″ N, 5°52‧22.7″ E; altitude 600 m). The distance between both sites was 36 km. We used numeric CMOS photographic cameras. The image processing sequence included a contrast enhancement and a perspective inversion to obtain a satellite-type view. Finally, the triangulation procedure was used in an area that is a common part of both fields of view.

  5. B4 2 After, 3D Deformation Field From Matching Pre- To Post-Event Aerial LiDAR Point Clouds, The 2010 El Mayor-Cucapah M7.2 Earthquake Case

    NASA Astrophysics Data System (ADS)

    Hinojosa-Corona, A.; Nissen, E.; Limon-Tirado, J. F.; Arrowsmith, R.; Krishnan, A.; Saripalli, S.; Oskin, M. E.; Glennie, C. L.; Arregui, S. M.; Fletcher, J. M.; Teran, O. J.

    2013-05-01

    Aerial LiDAR surveys reconstruct with amazing fidelity the sinuosity of terrain relief. In this research we explore the 3D deformation field at the surface after a big earthquake (M7.2) by comparing pre- to post-event aerial LiDAR point clouds. The April 4 2010 earthquake produced a NW-SE surface rupture ~110km long with right-lateral normal slip up to 3m in magnitude over a very favorable target: scarcely vegetated and unaltered desert mountain range, sierras El Mayor and Cucapah, in northern Baja California, close to the US-México border. It is a plate boundary region between the Pacific and North American plates. The pre-event LiDAR with lower point density (0.013-0.033 pts m-2) required filtering and post-processing before comparing with the denser (9-18 pts m-2) more accurate post event dataset. The 3D surface displacement field was determined using an adaptation of the Iterative Closest Point (ICP) algorithm, implemented in the open source Point Cloud Library (PCL). The LiDAR datasets are first split into a grid of windows, and for each one, ICP iteratively converges on the rigid body transformation (comprising translations and rotations) that best aligns the pre- to post-event points. Perturbing the pre- and post-event point clouds independently with a synthetic right lateral inverse displacements of known magnitude along a proposed fault, ICP recovered the synthetically introduced translations. Windows with dimensions of 100-200m gave the best results for datasets with these densities. The simplified surface rupture photo interpreted and mapped in the field, delineates very well the vertical displacements patterns unveiled by ICP. The method revealed block rotations, some with clockwise and others counter clockwise direction along the simplified surface rupture. As ground truth, displacements from ICP have similar values as those measured in the field along the main rupture by Fletcher and collaborators. The vertical component was better estimated than the

  6. SACR ADVance 3-D Cartesian Cloud Cover (SACR-ADV-3D3C) product

    DOE Data Explorer

    Meng Wang, Tami Toto, Eugene Clothiaux, Katia Lamer, Mariko Oue

    2017-03-08

    SACR-ADV-3D3C remaps the outputs of SACRCORR for cross-wind range-height indicator (CW-RHI) scans to a Cartesian grid and reports reflectivity CFAD and best estimate domain averaged cloud fraction. The final output is a single NetCDF file containing all aforementioned corrected radar moments remapped on a 3-D Cartesian grid, the SACR reflectivity CFAD, a profile of best estimate cloud fraction, a profile of maximum observable x-domain size (xmax), a profile time to horizontal distance estimate and a profile of minimum observable reflectivity (dBZmin).

  7. Coupling high resolution 3D point clouds from terrestrial LiDAR with high precision displacement time series from GB-InSAR to understand landslide kinematic: example of the La Perraire instability, Swiss Alps.

    NASA Astrophysics Data System (ADS)

    Michoud, Clément; Baillifard, François; Harald Blikra, Lars; Derron, Marc-Henri; Jaboyedoff, Michel; Kristensen, Lene; Leva, Davide; Metzger, Richard; Rivolta, Carlo

    2014-05-01

    Terrestrial Laser Scanning and Ground-Based Radar Interferometry have changed our perception and interpretation of slope activities for the last 20 years and are now routinely used for monitoring and even early warning purposes. Terrestrial LiDAR allows indeed to model topography with very high point density, even in steep slopes, and to extract 3D displacements of rock masses by comparing successive datasets. GB-InSAR techniques are able to detect mm displacements over large areas. Nevertheless, both techniques suffer of some limitations. The precision of LiDAR devices actually limits its ability to monitor very slow-moving landslides, as well as by the dam resolution and the particular geometry (in azimuth/range) of GB-InSAR data may complicate their interpretations. To overcome those limitations, tools were produced to truly combine strong advantages of both techniques, by coupling high resolution geometrical data from terrestrial LiDAR or photogrammetry with high precision displacement time series from GB-InSAR. We thus developed a new exportation module into the processing chain of LiSAmobile (GB-InSAR) devices in order to wrap radar results from their particular geometry on high resolution 3D point clouds with cm mean point spacing. Furthermore, we also added new importation and visualization functionalities into Coltop3D (software for geological interpretations of laser scanning data) to display those results in 3D and even analyzing displacement time series. This new method has also been optimized to create as few and small files as possible and for time processing. Advantages of coupling terrestrial LiDAR and GB-InSAR data will be illustrated on the La Perraire instability, an active large rockslide involving frequent rockfalls and threatening inhabitant within the Val de Bagnes in the Swiss Alps. This rock mass, monitored by LiDAR and GPS since 2006, is huge enough and long-term movements are big (up to 1.6 m in 6 years) and complex enough to make

  8. Cloud-resolving component in the quasi-3D multi-scale modeling framework

    NASA Astrophysics Data System (ADS)

    Jung, Joon-Hee; Arakawa, Akio

    2010-05-01

    A quasi-3D multi-scale modeling framework (Q3D MMF), which combines a GCM with a Q3D CRM, is an attempt to include three dimensional cloud effects in a GCM without necessarily using a global cloud-resolving model. The horizontal domain of the Q3D CRM consists of two perpendicular sets of channels crossing at the center of a GCM grid box, each of which includes two grid-point arrays. Through coupling this structure with a GCM, the whole system of the Q3D MMF can converge to a fully 3D global CRM as the GCM's resolution is refined. Consequently, the horizontal resolution of the GCM can be freely chosen depending on the objective of application. However, due to the use of very narrow channels for the cloud-resolving component, its prediction algorithm must be specially designed. As a step in developing a Q3D MMF, we have first constructed a prediction algorithm for the Q3D CRM applying a 3D anelastic vector vorticity equation model to the Q3D network of grid points. Preliminary tests of the Q3D CRM have been performed for an idealized small domain. Comparing the results with those of the straightforward application of a 3D CRM, it is concluded that the Q3D CRM can reproduce most of the important statistics of the 3D solutions and the MMF based on the Q3D CRM will be a useful framework for climate modeling. This paper presents an outline of the Q3D algorithm and highlights of the results.

  9. Automatic determination of trunk diameter, crown base and height of scots pine (Pinus Sylvestris L.) Based on analysis of 3D point clouds gathered from multi-station terrestrial laser scanning. (Polish Title: Automatyczne okreslanie srednicy pnia, podstawy korony oraz wysokosci sosny zwyczajnej (Pinus Silvestris L.) Na podstawie analiz chmur punktow 3D pochodzacych z wielostanowiskowego naziemnego skanowania laserowego)

    NASA Astrophysics Data System (ADS)

    Ratajczak, M.; Wężyk, P.

    2015-12-01

    Rapid development of terrestrial laser scanning (TLS) in recent years resulted in its recognition and implementation in many industries, including forestry and nature conservation. The use of the 3D TLS point clouds in the process of inventory of trees and stands, as well as in the determination of their biometric features (trunk diameter, tree height, crown base, number of trunk shapes), trees and lumber size (volume of trees) is slowly becoming a practice. In addition to the measurement precision, the primary added value of TLS is the ability to automate the processing of the clouds of points 3D in the direction of the extraction of selected features of trees and stands. The paper presents the original software (GNOM) for the automatic measurement of selected features of trees, based on the cloud of points obtained by the ground laser scanner FARO. With the developed algorithms (GNOM), the location of tree trunks on the circular research surface was specified and the measurement was performed; the measurement covered the DBH (l: 1.3m), further diameters of tree trunks at different heights of the tree trunk, base of the tree crown and volume of the tree trunk (the selection measurement method), as well as the tree crown. Research works were performed in the territory of the Niepolomice Forest in an unmixed pine stand (Pinussylvestris L.) on the circular surface with a radius of 18 m, within which there were 16 pine trees (14 of them were cut down). It was characterized by a two-storey and even-aged construction (147 years old) and was devoid of undergrowth. Ground scanning was performed just before harvesting. The DBH of 16 pine trees was specified in a fully automatic way, using the algorithm GNOM with an accuracy of +2.1%, as compared to the reference measurement by the DBH measurement device. The medium, absolute measurement error in the cloud of points - using semi-automatic methods "PIXEL" (between points) and PIPE (fitting the cylinder) in the FARO Scene 5.x

  10. Cloud Based Web 3d GIS Taiwan Platform

    NASA Astrophysics Data System (ADS)

    Tsai, W.-F.; Chang, J.-Y.; Yan, S. Y.; Chen, B.

    2011-09-01

    This article presents the status of the web 3D GIS platform, which has been developed in the National Applied Research Laboratories. The purpose is to develop a global earth observation 3D GIS platform for applications to disaster monitoring and assessment in Taiwan. For quick response to preliminary and detailed assessment after a natural disaster occurs, the web 3D GIS platform is useful to access, transfer, integrate, display and analyze the multi-scale huge data following the international OGC standard. The framework of cloud service for data warehousing management and efficiency enhancement using VMWare is illustrated in this article.

  11. Reconstruction of 3-D cloud geometry using a scanning cloud radar

    NASA Astrophysics Data System (ADS)

    Ewald, F.; Winkler, C.; Zinner, T.

    2014-11-01

    Clouds are one of the main reasons of uncertainties in the forecasts of weather and climate. In part, this is due to limitations of remote sensing of cloud microphysics. Present approaches often use passive spectral measurements for the remote sensing of cloud microphysical parameters. Large uncertainties are introduced by three dimensional (3-D) radiative transfer effects and cloud inhomogeneities. Such effects are largely caused by unknown orientation of cloud sides or by shadowed areas on the cloud. Passive ground based remote sensing of cloud properties at high spatial resolution could be improved crucially with this kind of additional knowledge of cloud geometry. To this end, a method for the accurate reconstruction of 3-D cloud geometry from cloud radar measurements is developed in this work. Using a radar simulator and simulated passive measurements of static LES model clouds, the effects of different radar scan resolutions and varying interpolation methods are evaluated. In reality a trade-off between scan resolution and scan duration has to be found as clouds are changing quickly. A reasonable choice is a scan resolution of 1 to 2°. The most suitable interpolation procedure identified is the barycentric interpolation method. The 3-D reconstruction method is demonstrated using radar scans of convective cloud cases with the Munich miraMACS, a 35 GHz scanning cloud radar. As a successful proof of concept, camera imagery collected at the radar location is reproduced for the observed cloud cases via 3-D volume reconstruction and 3-D radiative transfer simulation. Data sets provided by the presented reconstruction method will aid passive spectral ground-based measurements of cloud sides to retrieve microphysical parameters.

  12. Extraction of features from 3D laser scanner cloud data

    NASA Astrophysics Data System (ADS)

    Chan, Vincent H.; Bradley, Colin H.; Vickers, Geoffrey W.

    1997-12-01

    One of the road blocks on the path of automated reverse engineering has been the extraction of useful data from the copious range data generated from 3-D laser scanning systems. A method to extract the relevant features of a scanned object is presented. A 3-D laser scanner is automatically directed to obtain discrete laser cloud data on each separate patch that constitutes the object's surface. With each set of cloud data treated as a separate entity, primitives are fitted to the data resulting in a geometric and topologic database. Using a feed-forewarn neural network, the data is analyzed for geometric combinations that make up machine features such as through holes and slots. These features are required for the reconstruction of the solid model by a machinist or feature based CAM algorithms, thus completing the reverse engineering cycle.

  13. The application of iterative closest point (ICP) registration to improve 3D terrain mapping estimates using the flash 3D ladar system

    NASA Astrophysics Data System (ADS)

    Woods, Jack; Armstrong, Ernest E.; Armbruster, Walter; Richmond, Richard

    2010-04-01

    The primary purpose of this research was to develop an effective means of creating a 3D terrain map image (point-cloud) in GPS denied regions from a sequence of co-bore sighted visible and 3D LIDAR images. Both the visible and 3D LADAR cameras were hard mounted to a vehicle. The vehicle was then driven around the streets of an abandoned village used as a training facility by the German Army and imagery was collected. The visible and 3D LADAR images were then fused and 3D registration performed using a variation of the Iterative Closest Point (ICP) algorithm. The ICP algorithm is widely used for various spatial and geometric alignment of 3D imagery producing a set of rotation and translation transformations between two 3D images. ICP rotation and translation information obtain from registering the fused visible and 3D LADAR imagery was then used to calculate the x-y plane, range and intensity (xyzi) coordinates of various structures (building, vehicles, trees etc.) along the driven path. The xyzi coordinates information was then combined to create a 3D terrain map (point-cloud). In this paper, we describe the development and application of 3D imaging techniques (most specifically the ICP algorithm) used to improve spatial, range and intensity estimates of imagery collected during urban terrain mapping using a co-bore sighted, commercially available digital video camera with focal plan of 640×480 pixels and a 3D FLASH LADAR. Various representations of the reconstructed point-clouds for the drive through data will also be presented.

  14. A 3D Cloud-Construction Algorithm for the EarthCARE Satellite Mission

    NASA Technical Reports Server (NTRS)

    Barker, H. W.; Jerg, M. P.; Wehr, T.; Kato, S.; Donovan, D. P.; Hogan, R. J.

    2011-01-01

    This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data.

  15. Iterative consolidation of unorganized point clouds.

    PubMed

    Liu, Shengjun; Chan, Kwan-Chung; Wang, Charlie C L

    2012-01-01

    Unorganized point clouds obtained from 3D shape acquisition devices usually present noise, outliers, and nonuniformities. The proposed framework consolidates unorganized points through an iterative procedure of interlaced downsampling and upsampling. Selection operations remove outliers while preserving geometric details. The framework improves the uniformity of points by moving the downsampled particles and refining point samples. Surface extrapolation fills missed regions. Moreover, an adaptive sampling strategy speeds up the iterations. Experimental results demonstrate the framework's effectiveness.

  16. 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

    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.

  17. Scanning Cloud Radar Observations at Azores: Preliminary 3D Cloud Products

    SciTech Connect

    Kollias, P.; Johnson, K.; Jo, I.; Tatarevic, A.; Giangrande, S.; Widener, K.; Bharadwaj, N.; Mead, J.

    2010-03-15

    The deployment of the Scanning W-Band ARM Cloud Radar (SWACR) during the AMF campaign at Azores signals the first deployment of an ARM Facility-owned scanning cloud radar and offers a prelude for the type of 3D cloud observations that ARM will have the capability to provide at all the ARM Climate Research Facility sites by the end of 2010. The primary objective of the deployment of Scanning ARM Cloud Radars (SACRs) at the ARM Facility sites is to map continuously (operationally) the 3D structure of clouds and shallow precipitation and to provide 3D microphysical and dynamical retrievals for cloud life cycle and cloud-scale process studies. This is a challenging task, never attempted before, and requires significant research and development efforts in order to understand the radar's capabilities and limitations. At the same time, we need to look beyond the radar meteorology aspects of the challenge and ensure that the hardware and software capabilities of the new systems are utilized for the development of 3D data products that address the scientific needs of the new Atmospheric System Research (ASR) program. The SWACR observations at Azores provide a first look at such observations and the challenges associated with their analysis and interpretation. The set of scan strategies applied during the SWACR deployment and their merit is discussed. The scan strategies were adjusted for the detection of marine stratocumulus and shallow cumulus that were frequently observed at the Azores deployment. Quality control procedures for the radar reflectivity and Doppler products are presented. Finally, preliminary 3D-Active Remote Sensing of Cloud Locations (3D-ARSCL) products on a regular grid will be presented, and the challenges associated with their development discussed. In addition to data from the Azores deployment, limited data from the follow-up deployment of the SWACR at the ARM SGP site will be presented. This effort provides a blueprint for the effort required for the

  18. Desktop Cloud Visualization: the new technology to remote access 3D interactive applications in the Cloud.

    PubMed

    Torterolo, Livia; Ruffino, Francesco

    2012-01-01

    In the proposed demonstration we will present DCV (Desktop Cloud Visualization): a unique technology that allows users to remote access 2D and 3D interactive applications over a standard network. This allows geographically dispersed doctors work collaboratively and to acquire anatomical or pathological images and visualize them for further investigations.

  19. 3D Cloud Effects in OCO-2 Observations - Evidence and Mitigation

    NASA Astrophysics Data System (ADS)

    Schmidt, Sebastian; Massie, Steven; Iwabuchi, Hironobu; Okamura, Rintaro; Crisp, David

    2016-04-01

    In July 2014, the NASA Orbiting Carbon Observatory (OCO-2) satellite was inserted into the 705-km Afternoon Constellation (A-Train). OCO-2 provides estimates of column-averaged CO2 dry air mixing ratios (XCO2), based on high spectral resolution radiance observations of reflected sunlight in the O2 A-band and in the weak and strong absorption CO2 bands at 1.6 and 2.1 μm. The accuracy requirement for OCO-2 XCO2 retrievals is 1 ppmv on regional scales (> 1000 km). At the single sounding level, inhomogeneous clouds, surface albedo, and aerosols introduce wavelength-dependent perturbations into the sensed radiance fields, affecting the retrieval products. Scattering and shadowing by clouds outside of the field of view (FOV) may be a leading source of error for clear-sky XCO2 retrievals in partially cloudy regions. To understand these effects, we developed a 3D OCO-2 simulator, which uses observations by MODIS (also in the A-Train) and other scene information as input to simulate OCO-2 radiance spectra at the full wavelength resolution of the three bands. It is based on MCARaTS (Monte Carlo Atmospheric Radiative Transfer Simulator) as the 3D radiative transfer solver. The OCO-2 3D simulator was applied to an observed scene near a Total Carbon Column Observing Network (TCCON) station. The 3D calculations reproduced the OCO-2 radiances, including the perturbations due to clouds, at the single sounding level. The analysis further suggests that clouds near an OCO-2 footprint leave systematic spectral imprints on the radiances, which could be parameterized to be included in the retrieval state vector. If successful, this new state vector element could account for 3D effects without the need for operational 3D radiative transfer calculations. This may be the starting point not only for the improved screening of low-level broken boundary layer clouds, but also for mitigating the effects of nearby clouds at the radiance level, thus improving the accuracy of retrievals in

  20. 3D Atmospheric Radiative Transfer for Cloud System-Resolving Models: Forward Modelling and Observations

    SciTech Connect

    Howard Barker; Jason Cole

    2012-05-17

    Utilization of cloud-resolving models and multi-dimensional radiative transfer models to investigate the importance of 3D radiation effects on the numerical simulation of cloud fields and their properties.

  1. Effects of point configuration on the accuracy in 3D reconstruction from biplane images

    SciTech Connect

    Dmochowski, Jacek; Hoffmann, Kenneth R.; Singh, Vikas; Xu Jinhui; Nazareth, Daryl P.

    2005-09-15

    Two or more angiograms are being used frequently in medical imaging to reconstruct locations in three-dimensional (3D) space, e.g., for reconstruction of 3D vascular trees, implanted electrodes, or patient positioning. A number of techniques have been proposed for this task. In this simulation study, we investigate the effect of the shape of the configuration of the points in 3D (the 'cloud' of points) on reconstruction errors for one of these techniques developed in our laboratory. Five types of configurations (a ball, an elongated ellipsoid (cigar), flattened ball (pancake), flattened cigar, and a flattened ball with a single distant point) are used in the evaluations. For each shape, 100 random configurations were generated, with point coordinates chosen from Gaussian distributions having a covariance matrix corresponding to the desired shape. The 3D data were projected into the image planes using a known imaging geometry. Gaussian distributed errors were introduced in the x and y coordinates of these projected points. Gaussian distributed errors were also introduced into the gantry information used to calculate the initial imaging geometry. The imaging geometries and 3D positions were iteratively refined using the enhanced-Metz-Fencil technique. The image data were also used to evaluate the feasible R-t solution volume. The 3D errors between the calculated and true positions were determined. The effects of the shape of the configuration, the number of points, the initial geometry error, and the input image error were evaluated. The results for the number of points, initial geometry error, and image error are in agreement with previously reported results, i.e., increasing the number of points and reducing initial geometry and/or image error, improves the accuracy of the reconstructed data. The shape of the 3D configuration of points also affects the error of reconstructed 3D configuration; specifically, errors decrease as the 'volume' of the 3D configuration

  2. Coupled fvGCM-GCE Modeling System, 3D Cloud-Resolving Model and Cloud Library

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo

    2005-01-01

    Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud- resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. A seed fund is available at NASA Goddard to build a MMF based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM). A prototype MMF in being developed and production runs will be conducted at the beginning of 2005. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes, ( 2 ) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), (3) A cloud library generated by Goddard MMF, and 3D GCE model, and (4) A brief discussion on the GCE model on developing a global cloud simulator.

  3. Ground point filtering of UAV-based photogrammetric point clouds

    NASA Astrophysics Data System (ADS)

    Anders, Niels; Seijmonsbergen, Arie; Masselink, Rens; Keesstra, Saskia

    2016-04-01

    Unmanned Aerial Vehicles (UAVs) have proved invaluable for generating high-resolution and multi-temporal imagery. Based on photographic surveys, 3D surface reconstructions can be derived photogrammetrically so producing point clouds, orthophotos and surface models. For geomorphological or ecological applications it may be necessary to separate ground points from vegetation points. Existing filtering methods are designed for point clouds derived using other methods, e.g. laser scanning. The purpose of this paper is to test three filtering algorithms for the extraction of ground points from point clouds derived from low-altitude aerial photography. Three subareas were selected from a single flight which represent different scenarios: 1) low relief, sparsely vegetated area, 2) low relief, moderately vegetated area, 3) medium relief and moderately vegetated area. The three filtering methods are used to classify ground points in different ways, based on 1) RGB color values from training samples, 2) TIN densification as implemented in LAStools, and 3) an iterative surface lowering algorithm. Ground points are then interpolated into a digital terrain model using inverse distance weighting. The results suggest that different landscapes require different filtering methods for optimal ground point extraction. While iterative surface lowering and TIN densification are fully automated, color-based classification require fine-tuning in order to optimize the filtering results. Finally, we conclude that filtering photogrammetric point clouds could provide a cheap alternative to laser scan surveys for creating digital terrain models in sparsely vegetated areas.

  4. Do Fractal Models of Clouds Produces the Right 3D Radiative Effects?

    NASA Technical Reports Server (NTRS)

    Varnai, Tamas; Marshak, Alexander; Einaudi, Franco (Technical Monitor)

    2001-01-01

    Stochastic fractal models of clouds are often used to study 3D radiative effects and their influence on the remote sensing of cloud properties. Since it is important that the cloud models produce a correct radiative response, some researchers require the model parameters to match observed cloud properties such as scale-independent optical thickness variability. Unfortunately, matching these properties does not necessarily imply that the cloud models will cause the right 3D radiative effects. First, the matched properties alone only influence the 3D effects but do not completely determine them. Second, in many cases the retrieved cloud properties have been already biased by 3D radiative effects, and so the models may not match the true real clouds. Finally, the matched cloud properties cannot be considered independent from the scales at which they have been retrieved. This paper proposes an approach that helps ensure that fractal cloud models are realistic and produce the right 3D effects. The technique compares the results of radiative transfer simulations for the model clouds to new direct observations of 3D radiative effects in satellite images.

  5. Coupled fvGCM-GCE Modeling System, 3D Cloud-Resolving Model and Cloud Library

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo

    2005-01-01

    Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional singlecolumn models in simulating various types of clouds and cloud systems from Merent geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloudscale model (termed a super-parameterization or multiscale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameteridon NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. A seed fund is available at NASA Goddard to build a MMF based on the 2D Goddard cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM). A prototype MMF in being developed and production nms will be conducted at the beginning of 2005. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes, (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), (3) A cloud library generated by Goddard MMF, and 3D GCE model, and (4) A brief discussion on the GCE model on developing a global cloud simulator.

  6. Momentum Transport: 2D and 3D Cloud Resolving Model Simulations

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo

    2001-01-01

    The major objective of this study is to investigate the momentum budgets associated with several convective systems that developed during the TOGA COARE IOP (west Pacific warm pool region) and GATE (east Atlantic region). The tool for this study is the improved Goddard Cumulas Ensemble (GCE) model which includes a 3-class ice-phase microphysical scheme, explicit cloud radiative interactive processes and air-sea interactive surface processes. The model domain contains 256 x 256 grid points (with 2 km resolution) in the horizontal and 38 grid points (to a depth of 22 km) in the vertical. The 2D domain has 1024 grid points. The simulations were performed over a 7-day time period (December 19-26, 1992, for TOGA COARE and September 1-7, 1994 for GATE). Cyclic literal boundary conditions are required for this type of long-term integration. Two well organized squall systems (TOGA, COARE February 22, 1993, and GATE September 12, 1994) were also simulated using the 3D GCE model. Only 9 h simulations were required to cover the life time of the squall systems. the lateral boundary conditions were open for these two squall systems simulations. the following will be examined: (1) the momentum budgets in the convective and stratiform regions, (2) the relationship between momentum transport and cloud organization (i.e., well organized squall lines versus less organized convective), (3) the differences and similarities in momentum transport between 2D and 3D simulated convective systems, and (4) the differences and similarities in momentum budgets between cloud systems simulated with open and cyclic lateral boundary conditions. Preliminary results indicate that there are only small differences between 2D and 3D simulated momentum budgets. Major differences occur, however, between momentum budgets associated with squall systems simulated using different lateral boundary conditions.

  7. Parameterization and analysis of 3-D radiative transfer in clouds

    SciTech Connect

    Varnai, Tamas

    2012-03-16

    This report provides a summary of major accomplishments from the project. The project examines the impact of radiative interactions between neighboring atmospheric columns, for example clouds scattering extra sunlight toward nearby clear areas. While most current cloud models don't consider these interactions and instead treat sunlight in each atmospheric column separately, the resulting uncertainties have remained unknown. This project has provided the first estimates on the way average solar heating is affected by interactions between nearby columns. These estimates have been obtained by combining several years of cloud observations at three DOE Atmospheric Radiation Measurement (ARM) Climate Research Facility sites (in Alaska, Oklahoma, and Papua New Guinea) with simulations of solar radiation around the observed clouds. The importance of radiative interactions between atmospheric columns was evaluated by contrasting simulations that included the interactions with those that did not. This study provides lower-bound estimates for radiative interactions: It cannot consider interactions in cross-wind direction, because it uses two-dimensional vertical cross-sections through clouds that were observed by instruments looking straight up as clouds drifted aloft. Data from new DOE scanning radars will allow future radiative studies to consider the full three-dimensional nature of radiative processes. The results reveal that two-dimensional radiative interactions increase overall day-and-night average solar heating by about 0.3, 1.2, and 4.1 Watts per meter square at the three sites, respectively. This increase grows further if one considers that most large-domain cloud simulations have resolutions that cannot specify small-scale cloud variability. For example, the increases in solar heating mentioned above roughly double for a fairly typical model resolution of 1 km. The study also examined the factors that shape radiative interactions between atmospheric columns and

  8. Observation of a 3D Magnetic Null Point

    NASA Astrophysics Data System (ADS)

    Romano, P.; Falco, M.; Guglielmino, S. L.; Murabito, M.

    2017-03-01

    We describe high-resolution observations of a GOES B-class flare characterized by a circular ribbon at the chromospheric level, corresponding to the network at the photospheric level. We interpret the flare as a consequence of a magnetic reconnection event that occurred at a three-dimensional (3D) coronal null point located above the supergranular cell. The potential field extrapolation of the photospheric magnetic field indicates that the circular chromospheric ribbon is cospatial with the fan footpoints, while the ribbons of the inner and outer spines look like compact kernels. We found new interesting observational aspects that need to be explained by models: (1) a loop corresponding to the outer spine became brighter a few minutes before the onset of the flare; (2) the circular ribbon was formed by several adjacent compact kernels characterized by a size of 1″–2″ (3) the kernels with a stronger intensity emission were located at the outer footpoint of the darker filaments, departing radially from the center of the supergranular cell; (4) these kernels started to brighten sequentially in clockwise direction; and (5) the site of the 3D null point and the shape of the outer spine were detected by RHESSI in the low-energy channel between 6.0 and 12.0 keV. Taking into account all these features and the length scales of the magnetic systems involved in the event, we argue that the low intensity of the flare may be ascribed to the low amount of magnetic flux and to its symmetric configuration.

  9. Asymmetric effects at 3D Ising-like critical points

    NASA Astrophysics Data System (ADS)

    Tsypin, M.

    2003-05-01

    The Standard Model of electroweak interactions has a line of first order phase transition in the plane (higgs mass, temperature) that ends in a critical point belonging to the 3D Ising model universality class [K. Rummukainen et al, hep-lat/9805013. Similar critical points are found in finite-temperature QCD [M. Stephanov et al, hep-ph/9806219; F. Karsch et al, hep-lat/0107020. When these critical points are studied by Monte Carlo simulations on the lattice, one observes certain residual deviations from Z2 symmetry (which is exact for the Ising model). Here we study whether such deviations can be attributed to asymmetric corrections to scaling, which are relatively poorly studied. We compute the critical exponents in the local potential approximation (LPA), that is, in the framework of the Wegner-Houghton equation. We find that the exponent for the leading antisymmetric correction to scaling is approximately 1.691 in the LPA. This high value implies that such corrections cannot explain observed asymmetries.

  10. a Review of Point Clouds Segmentation and Classification Algorithms

    NASA Astrophysics Data System (ADS)

    Grilli, E.; Menna, F.; Remondino, F.

    2017-02-01

    Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.

  11. Multiframe image point matching and 3-d surface reconstruction.

    PubMed

    Tsai, R Y

    1983-02-01

    This paper presents two new methods, the Joint Moment Method (JMM) and the Window Variance Method (WVM), for image matching and 3-D object surface reconstruction using multiple perspective views. The viewing positions and orientations for these perspective views are known a priori, as is usually the case for such applications as robotics and industrial vision as well as close range photogrammetry. Like the conventional two-frame correlation method, the JMM and WVM require finding the extrema of 1-D curves, which are proved to theoretically approach a delta function exponentially as the number of frames increases for the JMM and are much sharper than the two-frame correlation function for both the JMM and the WVM, even when the image point to be matched cannot be easily distinguished from some of the other points. The theoretical findings have been supported by simulations. It is also proved that JMM and WVM are not sensitive to certain radiometric effects. If the same window size is used, the computational complexity for the proposed methods is about n - 1 times that for the two-frame method where n is the number of frames. Simulation results show that the JMM and WVM require smaller windows than the two-frame correlation method with better accuracy, and therefore may even be more computationally feasible than the latter since the computational complexity increases quadratically as a function of the window size.

  12. Extending 3D Near-Cloud Corrections from Shorter to Longer Wavelengths

    NASA Technical Reports Server (NTRS)

    Marshak, Alexander; Evans, K. Frank; Varnai, Tamas; Guoyong, Wen

    2014-01-01

    Satellite observations have shown a positive correlation between cloud amount and aerosol optical thickness (AOT) that can be explained by the humidification of aerosols near clouds, and/or by cloud contamination by sub-pixel size clouds and the cloud adjacency effect. The last effect may substantially increase reflected radiation in cloud-free columns, leading to overestimates in the retrieved AOT. For clear-sky areas near boundary layer clouds the main contribution to the enhancement of clear sky reflectance at shorter wavelengths comes from the radiation scattered into clear areas by clouds and then scattered to the sensor by air molecules. Because of the wavelength dependence of air molecule scattering, this process leads to a larger reflectance increase at shorter wavelengths, and can be corrected using a simple two-layer model. However, correcting only for molecular scattering skews spectral properties of the retrieved AOT. Kassianov and Ovtchinnikov proposed a technique that uses spectral reflectance ratios to retrieve AOT in the vicinity of clouds; they assumed that the cloud adjacency effect influences the spectral ratio between reflectances at two wavelengths less than it influences the reflectances themselves. This paper combines the two approaches: It assumes that the 3D correction for the shortest wavelength is known with some uncertainties, and then it estimates the 3D correction for longer wavelengths using a modified ratio method. The new approach is tested with 3D radiances simulated for 26 cumulus fields from Large-Eddy Simulations, supplemented with 40 aerosol profiles. The results showed that (i) for a variety of cumulus cloud scenes and aerosol profiles over ocean the 3D correction due to cloud adjacency effect can be extended from shorter to longer wavelengths and (ii) the 3D corrections for longer wavelengths are not very sensitive to unbiased random uncertainties in the 3D corrections at shorter wavelengths.

  13. A multi-resolution fractal additive scheme for blind watermarking of 3D point data

    NASA Astrophysics Data System (ADS)

    Rahmes, Mark; Wilder, Kathy; Fox, Kevin

    2013-05-01

    We present a fractal feature space for 3D point watermarking to make geospatial systems more secure. By exploiting the self similar nature of fractals, hidden information can be spatially embedded in point cloud data in an acceptable manner as described within this paper. Our method utilizes a blind scheme which provides automatic retrieval of the watermark payload without the need of the original cover data. Our method for locating similar patterns and encoding information in LiDAR point cloud data is accomplished through a look-up table or code book. The watermark is then merged into the point cloud data itself resulting in low distortion effects. With current advancements in computing technologies, such as GPGPUs, fractal processing is now applicable for processing of big data which is present in geospatial as well as other systems. This watermarking technique described within this paper can be important for systems where point data is handled by numerous aerial collectors including analysts use for systems such as a National LiDAR Data Layer.

  14. Use of the ARM Measurement of Spectral Zenith Radiance For Better Understanding Of 3D Cloud-Radiation Processes and Aerosol-Cloud Interaction

    SciTech Connect

    Chiu, Jui-Yuan

    2010-10-19

    Our proposal focuses on cloud-radiation processes in a general 3D cloud situation, with particular emphasis on cloud optical depth and effective particle size. We also focus on zenith radiance measurements, both active and passive. The proposal has three main parts. Part One exploits the "solar-background" mode of ARM lidars to allow them to retrieve cloud optical depth not just for thin clouds but for all clouds. This also enables the study of aerosol cloud interactions with a single instrument. Part Two exploits the large number of new wavelengths offered by ARM's zenith-pointing ShortWave Spectrometer (SWS), especially during CLASIC, to develop better retrievals not only of cloud optical depth but also of cloud particle size. We also propose to take advantage of the SWS's 1 Hz sampling to study the "twilight zone" around clouds where strong aerosol-cloud interactions are taking place. Part Three involves continuing our cloud optical depth and cloud fraction retrieval research with ARM's 2NFOV instrument by, first, analyzing its data from the AMF-COPS/CLOWD deployment, and second, making our algorithms part of ARM's operational data processing.

  15. Road Curb Extraction From Mobile LiDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Xu, Sheng; Wang, Ruisheng; Zheng, Han

    2017-02-01

    Automatic extraction of road curbs from uneven, unorganized, noisy and massive 3D point clouds is a challenging task. Existing methods often project 3D point clouds onto 2D planes to extract curbs. However, the projection causes loss of 3D information which degrades the performance of the detection. This paper presents a robust, accurate and efficient method to extract road curbs from 3D mobile LiDAR point clouds. Our method consists of two steps: 1) extracting the candidate points of curbs based on the proposed novel energy function and 2) refining the candidate points using the proposed least cost path model. We evaluated our method on a large-scale of residential area (16.7GB, 300 million points) and an urban area (1.07GB, 20 million points) mobile LiDAR point clouds. Results indicate that the proposed method is superior to the state-of-the-art methods in terms of robustness, accuracy and efficiency. The proposed curb extraction method achieved a completeness of 78.62% and a correctness of 83.29%. These experiments demonstrate that the proposed method is a promising solution to extract road curbs from mobile LiDAR point clouds.

  16. A 3D Current Loop Model of Magnetic Clouds

    NASA Astrophysics Data System (ADS)

    Chen, James

    1992-05-01

    A magnetohydrodynamic (MHD) model is developed to study magnetic clouds (Burlaga et al. 1981). In this model, magnetic clouds observed near 1 AU are treated as a consequence of eruptive solar current loops. It is shown that current loops intially in MHD equilibrium can be triggered to rise rapidly, propelling material of up to 10(16) g at up to ~ 1000 km s(-1) and dissipating ~ 10(32) erg of magnetic energy in tens of minutes. The initial rise profile is consistent with observed height-time profiles of erupting filaments (Kahler et al. 1988). Two triggering mechanisms for eruption are suggested: (1)subphotospheric energy storage and trigger and (2) in situ (coronal) energy storage and trigger. In the former, eruption occurs as a result of changes in the subphotospheric magnetic topology and subsequent relaxation to a new equilibrium. In the latter, the current loop can evolve to exceed a local maximum in the magnetic potential associated with the ambient magnetic fields. The former scenario leads to more energetic and longer-lasting eruption than the latter. Burlaga, L. F., Sittler, E., Mariani, F., and Schwenn, R. 1981, J. Geophys. Res., 86, 6673. Kahler, S. W., Moore, R. L., Kane, S. R., and Zirin, H. 1988, Ap. J., 328, 824.

  17. Parameterization and Analysis of 3-D Solar Radiative Transfer in Clouds: Final Report

    SciTech Connect

    Jerry Y. Harrington

    2012-09-21

    This document reports on the research that we have done over the course of our two-year project. The report also covers the research done on this project during a 1 year no-cost extension of the grant. Our work has had two main, inter-related thrusts: The first thrust was to characterize the response of stratocumulus cloud structure and dynamics to systematic changes in cloud infrared radiative cooling and solar heating using one-dimensional radiative transfer models. The second was to couple a three-dimensional (3-D) solar radiative transfer model to the Large Eddy Simulation (LES) model that we use to simulate stratocumulus. The purpose of the studies with 3-D radiative transfer was to examine the possible influences of 3-D photon transport on the structure, evolution, and radiative properties of stratocumulus. While 3-D radiative transport has been examined in static cloud environments, few studies have attempted to examine whether the 3-D nature of radiative absorption and emission influence the structure and evolution of stratocumulus. We undertook this dual approach because only a small number of LES simulations with the 3-D radiative transfer model are possible due to the high computational costs. Consequently, LES simulations with a 1-D radiative transfer solver were used in order to examine the portions of stratocumulus parameter space that may be most sensitive to perturbations in the radiative fields. The goal was then to explore these sensitive regions with LES using full 3-D radiative transfer. Our overall goal was to discover whether 3-D radiative processes alter cloud structure and evolution, and whether this may have any indirect implications for cloud radiative properties. In addition, we collaborated with Dr. Tamas Varni, providing model output fields for his attempt at parameterizing 3-D radiative effects for cloud models.

  18. Dynamic 3-D chemical agent cloud mapping using a sensor constellation deployed on mobile platforms

    NASA Astrophysics Data System (ADS)

    Cosofret, Bogdan R.; Konno, Daisei; Rossi, David; Marinelli, William J.; Seem, Pete

    2014-05-01

    The need for standoff detection technology to provide early Chem-Bio (CB) threat warning is well documented. Much of the information obtained by a single passive sensor is limited to bearing and angular extent of the threat cloud. In order to obtain absolute geo-location, range to threat, 3-D extent and detailed composition of the chemical threat, fusion of information from multiple passive sensors is needed. A capability that provides on-the-move chemical cloud characterization is key to the development of real-time Battlespace Awareness. We have developed, implemented and tested algorithms and hardware to perform the fusion of information obtained from two mobile LWIR passive hyperspectral sensors. The implementation of the capability is driven by current Nuclear, Biological and Chemical Reconnaissance Vehicle operational tactics and represents a mission focused alternative of the already demonstrated 5-sensor static Range Test Validation System (RTVS).1 The new capability consists of hardware for sensor pointing and attitude information which is made available for streaming and aggregation as part of the data fusion process for threat characterization. Cloud information is generated using 2-sensor data ingested into a suite of triangulation and tomographic reconstruction algorithms. The approaches are amenable to using a limited number of viewing projections and unfavorable sensor geometries resulting from mobile operation. In this paper we describe the system architecture and present an analysis of results obtained during the initial testing of the system at Dugway Proving Ground during BioWeek 2013.

  19. Representing 3-D cloud radiation effects in two-stream schemes: 1. Longwave considerations and effective cloud edge length

    NASA Astrophysics Data System (ADS)

    Schäfer, Sophia A. K.; Hogan, Robin J.; Klinger, Carolin; Chiu, J. Christine; Mayer, Bernhard

    2016-07-01

    Current weather and climate models neglect 3-D radiative transfer through cloud sides, which can change the cloud radiative effect (CRE) significantly. This two-part paper describes the development of the SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides (SPARTACUS) to capture these effects efficiently in a two-stream radiation scheme for use in global models. The present paper concerns the longwave spectral region, where not much work has been done previously, although the limited previous work has suggested that radiative transfer through cloud sides increases the longwave surface CRE of shallow cumulus by around 30%. To assist the development of a longwave capability for SPARTACUS, we use a reference case of an isolated, isothermal, optically thick, cubic cloud in vacuum, for which 3-D effects increase CRE by exactly 200%. It is shown that for any cloud shape, the 3-D effect can be represented in SPARTACUS provided that correct account is made for (1) the effective zenith angle of diffuse radiation emitted from a cloud, (2) the spatial distribution of fluxes in the cloud, (3) cloud clustering that enhances the interception of emitted radiation by neighboring clouds, and (4) radiative smoothing leading to the effective cloud edge length being less than the measured value. We find empirically that the circumference of an ellipse fitted to a horizontal cross section through a cumulus cloud provides a good estimate of the radiatively effective cloud edge length, which provides some guidance to how cloud observations could be analyzed to extract their most important properties for radiation.

  20. 3D Aerosol-Cloud Radiative Interaction Observed in Collocated MODIS and ASTER Images of Cumulus Cloud Fields

    NASA Technical Reports Server (NTRS)

    Wen, Guoyong; Marshak, Alexander; Cahalan, Robert F.; Remer, Lorraine A.; Kleidman, Richard G.

    2007-01-01

    3D aerosol-cloud interaction is examined by analyzing two images containing cumulus clouds in biomass burning regions in Brazil. The research consists of two parts. The first part focuses on identifying 3D clo ud impacts on the reflectance of pixel selected for the MODIS aerosol retrieval based purely on observations. The second part of the resea rch combines the observations with radiative transfer computations to identify key parameters in 3D aerosol-cloud interaction. We found that 3D cloud-induced enhancement depends on optical properties of nearb y clouds as well as wavelength. The enhancement is too large to be ig nored. Associated biased error in 1D aerosol optical thickness retrie val ranges from 50% to 140% depending on wavelength and optical prope rties of nearby clouds as well as aerosol optical thickness. We caution the community to be prudent when applying 1D approximations in comp uting solar radiation in dear regions adjacent to clouds or when usin g traditional retrieved aerosol optical thickness in aerosol indirect effect research.

  1. Modeling the Impact of Drizzle and 3D Cloud Structure on Remote Sensing of Effective Radius

    NASA Technical Reports Server (NTRS)

    Platnick, Steven; Zinner, Tobias; Ackerman, S.

    2008-01-01

    Remote sensing of cloud particle size with passive sensors like MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave infrared channels. MODIS observations sometimes show significantly larger effective radii in marine boundary layer cloud fields derived from the 1.6 and 2.1 pm channel observations than for 3.7 pm retrievals. Possible explanations range from 3D radiative transport effects and sub-pixel cloud inhomogeneity to the impact of drizzle formation on the droplet distribution. To investigate the potential influence of these factors, we use LES boundary layer cloud simulations in combination with 3D Monte Carlo simulations of MODIS observations. LES simulations of warm cloud spectral microphysics for cases of marine stratus and broken stratocumulus, each for two different values of cloud condensation nuclei density, produce cloud structures comprising droplet size distributions with and without drizzle size drops. In this study, synthetic MODIS observations generated from 3D radiative transport simulations that consider the full droplet size distribution will be generated for each scene. The operational MODIS effective radius retrievals will then be applied to the simulated reflectances and the results compared with the LES microphysics.

  2. 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.

  3. Parameterization of Solar Radiative Fluxes For 3d-inhomogeneous Clouds

    NASA Astrophysics Data System (ADS)

    Schewski, M.; Macke, A.

    radiative fluxes for 3d clouds appears to be a promis- ing approach.

  4. Dynamic mineral clouds on HD 189733b. I. 3D RHD with kinetic, non-equilibrium cloud formation

    NASA Astrophysics Data System (ADS)

    Lee, G.; Dobbs-Dixon, I.; Helling, Ch.; Bognar, K.; Woitke, P.

    2016-10-01

    Context. Observations of exoplanet atmospheres have revealed the presence of cloud particles in their atmospheres. 3D modelling of cloud formation in atmospheres of extrasolar planets coupled to the atmospheric dynamics has long been a challenge. Aims: We investigate the thermo-hydrodynamic properties of cloud formation processes in the atmospheres of hot Jupiter exoplanets. Methods: We simulate the dynamic atmosphere of HD 189733b with a 3D model that couples 3D radiative-hydrodynamics with a kinetic, microphysical mineral cloud formation module designed for RHD/GCM exoplanet atmosphere simulations. Our simulation includes the feedback effects of cloud advection and settling, gas phase element advection and depletion/replenishment and the radiative effects of cloud opacity. We model the cloud particles as a mix of mineral materials which change in size and composition as they travel through atmospheric thermo-chemical environments. All local cloud properties such as number density, grain size and material composition are time-dependently calculated. Gas phase element depletion as a result of cloud formation is included in the model. In situ effective medium theory and Mie theory is applied to calculate the wavelength dependent opacity of the cloud component. Results: We present a 3D cloud structure of a chemically complex, gaseous atmosphere of the hot Jupiter HD 189733b. Mean cloud particle sizes are typically sub-micron (0.01-0.5 μm) at pressures less than 1 bar with hotter equatorial regions containing the smallest grains. Denser cloud structures occur near terminator regions and deeper (~1 bar) atmospheric layers. Silicate materials such as MgSiO3[s] are found to be abundant at mid-high latitudes, while TiO2[s] and SiO2[s] dominate the equatorial regions. Elements involved in the cloud formation can be depleted by several orders of magnitude. Conclusions: The interplay between radiative-hydrodynamics and cloud kinetics leads to an inhomogeneous, wavelength

  5. Airborne LIDAR point cloud tower inclination judgment

    NASA Astrophysics Data System (ADS)

    liang, Chen; zhengjun, Liu; jianguo, Qian

    2016-11-01

    Inclined transmission line towers for the safe operation of the line caused a great threat, how to effectively, quickly and accurately perform inclined judgment tower of power supply company safety and security of supply has played a key role. In recent years, with the development of unmanned aerial vehicles, unmanned aerial vehicles equipped with a laser scanner, GPS, inertial navigation is one of the high-precision 3D Remote Sensing System in the electricity sector more and more. By airborne radar scan point cloud to visually show the whole picture of the three-dimensional spatial information of the power line corridors, such as the line facilities and equipment, terrain and trees. Currently, LIDAR point cloud research in the field has not yet formed an algorithm to determine tower inclination, the paper through the existing power line corridor on the tower base extraction, through their own tower shape characteristic analysis, a vertical stratification the method of combining convex hull algorithm for point cloud tower scarce two cases using two different methods for the tower was Inclined to judge, and the results with high reliability.

  6. 3D AMR simulations of the evolution of the diffuse gas cloud G2 in the Galactic Centre

    NASA Astrophysics Data System (ADS)

    Schartmann, M.; Ballone, A.; Burkert, A.; Gillessen, S.; Genzel, R.; Pfuhl, O.; Eisenhauer, F.; Plewa, P. M.; Ott, T.; George, E. M.; Habibi, M.

    2017-01-01

    With the help of 3D AMR hydrodynamical simulations we aim at understanding G2's nature, recent evolution and fate in the coming years. By exploring the possible parameter space of the diffuse cloud scenario, we find that a starting point within the disc of young stars is favoured by the observations, which may hint at G2 being the result of stellar wind interactions.

  7. Virtual and Printed 3D Models for Teaching Crystal Symmetry and Point Groups

    ERIC Educational Resources Information Center

    Casas, Lluís; Estop, Euge`nia

    2015-01-01

    Both, virtual and printed 3D crystal models can help students and teachers deal with chemical education topics such as symmetry and point groups. In the present paper, two freely downloadable tools (interactive PDF files and a mobile app) are presented as examples of the application of 3D design to study point-symmetry. The use of 3D printing to…

  8. CAST: Effective and Efficient User Interaction for Context-Aware Selection in 3D Particle Clouds.

    PubMed

    Yu, Lingyun; Efstathiou, Konstantinos; Isenberg, Petra; Isenberg, Tobias

    2016-01-01

    We present a family of three interactive Context-Aware Selection Techniques (CAST) for the analysis of large 3D particle datasets. For these datasets, spatial selection is an essential prerequisite to many other analysis tasks. Traditionally, such interactive target selection has been particularly challenging when the data subsets of interest were implicitly defined in the form of complicated structures of thousands of particles. Our new techniques SpaceCast, TraceCast, and PointCast improve usability and speed of spatial selection in point clouds through novel context-aware algorithms. They are able to infer a user's subtle selection intention from gestural input, can deal with complex situations such as partially occluded point clusters or multiple cluster layers, and can all be fine-tuned after the selection interaction has been completed. Together, they provide an effective and efficient tool set for the fast exploratory analysis of large datasets. In addition to presenting Cast, we report on a formal user study that compares our new techniques not only to each other but also to existing state-of-the-art selection methods. Our results show that Cast family members are virtually always faster than existing methods without tradeoffs in accuracy. In addition, qualitative feedback shows that PointCast and TraceCast were strongly favored by our participants for intuitiveness and efficiency.

  9. A line segment based registration method for Terrestrial Laser Scanning point cloud data

    NASA Astrophysics Data System (ADS)

    Cheng, Jun; Cheng, Ming; Lin, Yangbin; Wang, Cheng

    2016-03-01

    This paper proposed a 3d line segment based registration method for terrestrial laser scanning (TLS) data. The 3D line segment is adopted to describe the point cloud data and reduce geometric complexity. After that, we introduce a framework for registration. We demonstrate the accuracy of our method for rigid transformations in the presence of terrestrial laser scanning point cloud.

  10. 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.

  11. A Radiative Transfer Case Study for 3-d cloud effects in the UV

    NASA Astrophysics Data System (ADS)

    Meerkötter, Ralf; Degünther, Markus

    Satellite UV mapping is usually based on the independent pixel approximation (IPA) which neglects horizontal photon transport between adjacent columns. Horizontal inhomogeneity of cloud fields therefore causes uncertainties in the derived UV radiation fields. While these effects are small for large pixel satellites, the broken-cloud errors increase as the pixel size decreases. By comparing results of 1-d and 3-d UV radiative transfer calculations for three selected cloud scenes that cover a rather broad range of cloud inhomogeneity the main 3-d cloud effects on the atmospheric UV transmission are identified and quantified in their order of magnitude. With respect to the different spatial resolutions of satellite instruments it is further shown how 3-d cloud effects average out with increasing spatial scale. It turns out that locally the IPA cause maximum uncertainties up to ±100% for a spatial resolution of about 1 × 1 km² (e.g., AVHRR), they are reduced to ±10% for a resolution of about 15 × 15 km² and below 5% for a resolution greater than 30 km (e.g., TOMS).

  12. Processing Uav and LIDAR Point Clouds in Grass GIS

    NASA Astrophysics Data System (ADS)

    Petras, V.; Petrasova, A.; Jeziorska, J.; Mitasova, H.

    2016-06-01

    Today's methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.

  13. Measuring 3D point configurations in pictorial space

    PubMed Central

    Wagemans, Johan; van Doorn, Andrea J; Koenderink, Jan J

    2011-01-01

    We propose a novel method to probe the depth structure of the pictorial space evoked by paintings. The method involves an exocentric pointing paradigm that allows one to find the slope of the geodesic connection between any pair of points in pictorial space. Since the locations of the points in the picture plane are known, this immediately yields the depth difference between the points. A set of depth differences between all pairs of points from an N-point (N > 2) configuration then yields the configuration in depth up to an arbitrary depth offset. Since an N-point configuration implies N(N−1) (ordered) pairs, the number of observations typically far exceeds the number of inferred depths. This yields a powerful check on the geometrical consistency of the results. We report that the remaining inconsistencies are fully accounted for by the spread encountered in repeated observations. This implies that the concept of ‘pictorial space’ indeed has an empirical significance. The method is analyzed and empirically verified in considerable detail. We report large quantitative interobserver differences, though the results of all observers agree modulo a certain affine transformation that describes the basic cue ambiguities. This is expected on the basis of a formal analysis of monocular optical structure. The method will prove useful in a variety of potential applications. PMID:23145227

  14. D Land Cover Classification Based on Multispectral LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

    2016-06-01

    Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

  15. Accuracy assessment of building point clouds automatically generated from iphone images

    NASA Astrophysics Data System (ADS)

    Sirmacek, B.; Lindenbergh, R.

    2014-06-01

    Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (σ) of roughness histograms are calculated as (μ1 = 0.44 m., σ1 = 0.071 m.) and (μ2 = 0.025 m., σ2 = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

  16. Extracting Feature Points of the Human Body Using the Model of a 3D Human Body

    NASA Astrophysics Data System (ADS)

    Shin, Jeongeun; Ozawa, Shinji

    The purpose of this research is to recognize 3D shape features of a human body automatically using a 3D laser-scanning machine. In order to recognize the 3D shape features, we selected the 23 feature points of a body and modeled its 3D features. The set of 23 feature points consists of the motion axis of a joint, the main point for the bone structure of a human body. For extracting feature points of object model, we made 2.5D templates neighbor for each feature points were extracted according to the feature points of the standard model of human body. And the feature points were extracted by the template matching. The extracted feature points can be applied as body measurement, the 3D virtual fitting system for apparel etc.

  17. Retrieval of cloud microphysical parameters from INSAT-3D: a feasibility study using radiative transfer simulations

    NASA Astrophysics Data System (ADS)

    Jinya, John; Bipasha, Paul S.

    2016-05-01

    Clouds strongly modulate the Earths energy balance and its atmosphere through their interaction with the solar and terrestrial radiation. They interact with radiation in various ways like scattering, emission and absorption. By observing these changes in radiation at different wavelength, cloud properties can be estimated. Cloud properties are of utmost importance in studying different weather and climate phenomena. At present, no satellite provides cloud microphysical parameters over the Indian region with high temporal resolution. INSAT-3D imager observations in 6 spectral channels from geostationary platform offer opportunity to study continuous cloud properties over Indian region. Visible (0.65 μm) and shortwave-infrared (1.67 μm) channel radiances can be used to retrieve cloud microphysical parameters such as cloud optical thickness (COT) and cloud effective radius (CER). In this paper, we have carried out a feasibility study with the objective of cloud microphysics retrieval. For this, an inter-comparison of 15 globally available radiative transfer models (RTM) were carried out with the aim of generating a Look-up- Table (LUT). SBDART model was chosen for the simulations. The sensitivity of each spectral channel to different cloud properties was investigated. The inputs to the RT model were configured over our study region (50°S - 50°N and 20°E - 130°E) and a large number of simulations were carried out using random input vectors to generate the LUT. The determination of cloud optical thickness and cloud effective radius from spectral reflectance measurements constitutes the inverse problem and is typically solved by comparing the measured reflectances with entries in LUT and searching for the combination of COT and CER that gives the best fit. The products are available on the website www.mosdac.gov.in

  18. Matching point clouds: limits and possibilities.

    PubMed

    Rudolph, H; Quaas, S; Luthardt, R G

    2002-01-01

    In computer-aided production of fixed dental restorations, the process chain always starts with digitizing, independent of the type of further (data) processing, the material used, and the kind of restoration to be produced. The quality of the digitized data, followed by the influences of further data processing and the production parameters, decisively influence the fitting accuracy of the dental restoration to be fabricated. The accuracy with which individually measured 3D data sets in the form of point clouds can be matched for further processing in one common system of coordinates was the object of the present study. Casts of the maxilla and mandible were digitized in several partial measurements comprising two to three teeth in each case, using an optical three-coordinate measuring system. The individual segments were sequentially aligned to surfaces that were created on the basis of partial point clouds. The mean deviation between surfaces and point clouds was between 1.90 microns and 18.24 microns. The accuracy of the alignment was determined by the RMS (root mean square) error, and was on average 14.2 microns (SD 7 microns) for the maxilla and 17.2 microns (SD 9.4 microns) for the mandible. Combining a larger number of smaller segments did not improve the result, since the errors of the individual registrations are summed in sequential matching. In this study, the errors arising in matching are not negligible and can possibly negatively influence the quality (fitting accuracy) of the restoration produced on the basis of the matched data records.

  19. 3D MODELING OF GJ1214b's ATMOSPHERE: FORMATION OF INHOMOGENEOUS HIGH CLOUDS AND OBSERVATIONAL IMPLICATIONS

    SciTech Connect

    Charnay, B.; Meadows, V.; Misra, A.; Arney, G.; Leconte, J.

    2015-11-01

    The warm sub-Neptune GJ1214b has a featureless transit spectrum that may be due to the presence of high and thick clouds or haze. Here, we simulate the atmosphere of GJ1214b with a 3D General Circulation Model for cloudy hydrogen-dominated atmospheres, including cloud radiative effects. We show that the atmospheric circulation is strong enough to transport micrometric cloud particles to the upper atmosphere and generally leads to a minimum of cloud at the equator. By scattering stellar light, clouds increase the planetary albedo to 0.4–0.6 and cool the atmosphere below 1 mbar. However, the heating by ZnS clouds leads to the formation of a stratospheric thermal inversion above 10 mbar, with temperatures potentially high enough on the dayside to evaporate KCl clouds. We show that flat transit spectra consistent with Hubble Space Telescope observations are possible if cloud particle radii are around 0.5 μm, and that such clouds should be optically thin at wavelengths >3 μm. Using simulated cloudy atmospheres that fit the observed spectra we generate transit, emission, and reflection spectra and phase curves for GJ1214b. We show that a stratospheric thermal inversion would be readily accessible in near- and mid-infrared atmospheric spectral windows. We find that the amplitude of the thermal phase curves is strongly dependent on metallicity, but only slightly impacted by clouds. Our results suggest that primary and secondary eclipses and phase curves observed by the James Webb Space Telescope in the near- to mid-infrared should provide strong constraints on the nature of GJ1214b's atmosphere and clouds.

  20. Evolving point-cloud features for gender classification

    NASA Astrophysics Data System (ADS)

    Keen, Brittany; Fouts, Aaron; Rizki, Mateen; Tamburino, Louis; Mendoza-Schrock, Olga L.

    2011-06-01

    In this paper we explore the use of histogram features extracted from 3D point clouds of human subjects for gender classification. Experiments are conducted using point clouds drawn from the CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Features are extracted from each point cloud by embedding the cloud in series of cylindrical shapes and computing a point count for each cylinder that characterizes a region of the subject. These measurements define rotationally invariant histogram features that are processed by a classifier to label the gender of each subject. Preliminary results using cylinder sizes defined by human experts demonstrate that gender can be predicted with 98% accuracy for the type of high density point cloud found in the CAESAR database. When point cloud densities are reduced to levels that might be obtained using stand-off sensors; gender classification accuracy degrades. We introduce an evolutionary algorithm to optimize the number and size of the cylinders used to define histogram features. The objective of this optimization process is to identify a set of cylindrical features that reduces the error rate when predicting gender from low density point clouds. A wrapper approach is used to interleave feature selection with classifier evaluation to train the evolutionary algorithm. Results of classification accuracy achieved using the evolved features are compared to the baseline feature set defined by human experts.

  1. Distance Functions and Geodesics on Points Clouds

    DTIC Science & Technology

    2005-01-01

    An algorithm for computing intrinsic distance functions and geodesics on sub-manifolds vector r(sup d) given by point clouds is introduced in this...and geodesics on point clouds while skipping the manifold reconstruction step. The case of point clouds representing noisy samples of a sub-manifold of...boundaries, and obtain also for the case of intrinsic distance functions on sub-manifolds of vector r(sup d), a computationally optimal approach. For point

  2. Alignment of Point Cloud DSMs from Tls and Uav Platforms

    NASA Astrophysics Data System (ADS)

    Persad, R. A.; Armenakis, C.

    2015-08-01

    The co-registration of 3D point clouds has received considerable attention from various communities, particularly those in photogrammetry, computer graphics and computer vision. Although significant progress has been made, various challenges such as coarse alignment using multi-sensory data with different point densities and minimal overlap still exist. There is a need to address such data integration issues, particularly with the advent of new data collection platforms such as the unmanned aerial vehicles (UAVs). In this study, we propose an approach to align 3D point clouds derived photogrammetrically from UAV approximately vertical images with point clouds measured by terrestrial laser scanners (TLS). The method begins by automatically extracting 3D surface keypoints on both point cloud datasets. Afterwards, regions of interest around each keypoint are established to facilitate the establishment of scale-invariant descriptors for each of them. We use the popular SURF descriptor for matching the keypoints. In our experiments, we report the accuracies of the automatically derived transformation parameters in comparison to manually-derived reference parameter data.

  3. Estimating Anthropometric Marker Locations from 3-D LADAR Point Clouds

    DTIC Science & Technology

    2011-06-01

    results in large initial activities for neurons inside a dense region. The “transmitting” process is captured with the following learning rule : Si(t+ 1...discriminant function is defined as: δk(x) = x TΣ−1µk − 1 2 µTkΣ −1µk + log (πk), (25) resulting in the following classification rule : Ĝ(x) = argmax k δk(x...as: δk(x) = − 1 2 log |Σk| − 1 2 (x− µk)TΣ−1k (x− µk) + log πk. (32) The classification rule is Ĝ(x) = argmax k δk(x) (33) and the decision

  4. Continuation of point clouds via persistence diagrams

    NASA Astrophysics Data System (ADS)

    Gameiro, Marcio; Hiraoka, Yasuaki; Obayashi, Ippei

    2016-11-01

    In this paper, we present a mathematical and algorithmic framework for the continuation of point clouds by persistence diagrams. A key property used in the method is that the persistence map, which assigns a persistence diagram to a point cloud, is differentiable. This allows us to apply the Newton-Raphson continuation method in this setting. Given an original point cloud P, its persistence diagram D, and a target persistence diagram D‧, we gradually move from D to D‧, by successively computing intermediate point clouds until we finally find a point cloud P‧ having D‧ as its persistence diagram. Our method can be applied to a wide variety of situations in topological data analysis where it is necessary to solve an inverse problem, from persistence diagrams to point cloud data.

  5. vPresent: A cloud based 3D virtual presentation environment for interactive product customization

    NASA Astrophysics Data System (ADS)

    Nan, Xiaoming; Guo, Fei; He, Yifeng; Guan, Ling

    2013-09-01

    In modern society, many companies offer product customization services to their customers. There are two major issues in providing customized products. First, product manufacturers need to effectively present their products to the customers who may be located in any geographical area. Second, customers need to be able to provide their feedbacks on the product in real-time. However, the traditional presentation approaches cannot effectively convey sufficient information for the product or efficiently adjust product design according to customers' real-time feedbacks. In order to address these issues, we propose vPresent , a cloud based 3D virtual presentation environment, in this paper. In vPresent, the product expert can show the 3D virtual product to the remote customers and dynamically customize the product based on customers' feedbacks, while customers can provide their opinions in real time when they are viewing a vivid 3D visualization of the product. Since the proposed vPresent is a cloud based system, the customers are able to access the customized virtual products from anywhere at any time, via desktop, laptop, or even smart phone. The proposed vPresent is expected to effectively deliver 3D visual information to customers and provide an interactive design platform for the development of customized products.

  6. Influence of 3D Radiative Effects on Satellite Retrievals of Cloud Properties

    NASA Technical Reports Server (NTRS)

    Varnai, Tamas; Marshak, Alexander; Einaudi, Franco (Technical Monitor)

    2001-01-01

    When cloud properties are retrieved from satellite observations, the calculations apply 1D theory to the 3D world: they only consider vertical structures and ignore horizontal cloud variability. This presentation discusses how big the resulting errors can be in the operational retrievals of cloud optical thickness. A new technique was developed to estimate the magnitude of potential errors by analyzing the spatial patterns of visible and infrared images. The proposed technique was used to set error bars for optical depths retrieved from new MODIS measurements. Initial results indicate that the 1 km resolution retrievals are subject to abundant uncertainties. Averaging over 50 by 50 km areas reduces the errors, but does not remove them completely; even in the relatively simple case of high sun (30 degree zenith angle), about a fifth of the examined areas had biases larger than ten percent. As expected, errors increase substantially for more oblique illumination.

  7. Accelerating 3D radiative transfer for realistic OCO-2 cloud-aerosol scenes

    NASA Astrophysics Data System (ADS)

    Schmidt, S.; Massie, S. T.; Platnick, S. E.; Song, S.

    2014-12-01

    The recently launched NASA OCO-2 satellite is expected to provide important information about the carbon dioxide distribution in the troposphere down to Earth's surface. Among the challenges in accurately retrieving CO2 concentration from the hyperspectral observations in each of the three OCO-2 bands are cloud and aerosol impacts on the observed radiances. Preliminary studies based on idealized cloud fields have shown that they can lead to spectrally dependent radiance perturbations which differ from band to band and may lead to biases in the derived products. Since OCO-2 was inserted into the A-Train, it is only natural to capitalize on sensor synergies with other instruments, in this case on the cloud and aerosol scene context that is provided by MODIS and CALIOP. Our approach is to use cloud imagery (especially for inhomogeneous scenes) for predicting the hyperspectral observations within a collocated OCO-2 footprint and comparing with the observations, which allows a systematic assessment of the causes for biases in the retrievals themselves, and their manifestation in spectral residuals for various different cloud types and distributions. Simulating a large number of cases with line-by-line calculations using a 3D code is computationally prohibitive even on large parallel computers. Therefore, we developed a number of acceleration approaches. In this contribution, we will analyze them in terms of their speed and accuracy, using cloud fields from airborne imagery collected during a recent NASA field experiment (SEAC4RS) as proxy for different types of inhomogeneous cloud fields. The broader goal of this effort is to improve OCO-2 retrievals in the vicinity of cloud fields, and to extend the range of conditions under which the instrument will provide useful results.

  8. A Data-Driven Point Cloud Simplification Framework for City-Scale Image-Based Localization.

    PubMed

    Cheng, Wentao; Lin, Weisi; Zhang, Xinfeng; Goesele, Michael; Sun, Ming-Ting

    2017-01-01

    City-scale 3D point clouds reconstructed via structure-from-motion from a large collection of Internet images are widely used in the image-based localization task to estimate a 6-DOF camera pose of a query image. Due to prohibitive memory footprint of city-scale point clouds, image-based localization is difficult to be implemented on devices with limited memory resources. Point cloud simplification aims to select a subset of points to achieve a comparable localization performance using the original point cloud. In this paper, we propose a data-driven point cloud simplification framework by taking it as a weighted K-Cover problem, which mainly includes two complementary parts. First, a utility-based parameter determination method is proposed to select a reasonable parameter K for K-Cover-based approaches by evaluating the potential of a point cloud for establishing sufficient 2D-3D feature correspondences. Second, we formulate the 3D point cloud simplification problem as a weighted K-Cover problem, and propose an adaptive exponential weight function based on the visibility probability of 3D points. The experimental results on three popular datasets demonstrate that the proposed point cloud simplification framework outperforms the state-of-the-art methods for the image-based localization application with a well predicted parameter in the K-Cover problem.

  9. Auotomatic Classification of Point Clouds Extracted from Ultracam Stereo Images

    NASA Astrophysics Data System (ADS)

    Modiri, M.; Masumi, M.; Eftekhari, A.

    2015-12-01

    Automatic extraction of building roofs, street and vegetation are a prerequisite for many GIS (Geographic Information System) applications, such as urban planning and 3D building reconstruction. Nowadays with advances in image processing and image matching technique by using feature base and template base image matching technique together dense point clouds are available. Point clouds classification is an important step in automatic features extraction. Therefore, in this study, the classification of point clouds based on features color and shape are implemented. We use two images by proper overlap getting by Ultracam-x camera in this study. The images are from Yasouj in IRAN. It is semi-urban area by building with different height. Our goal is classification buildings and vegetation in these points. In this article, an algorithm is developed based on the color characteristics of the point's cloud, using an appropriate DEM (Digital Elevation Model) and points clustering method. So that, firstly, trees and high vegetation are classified by using the point's color characteristics and vegetation index. Then, bare earth DEM is used to separate ground and non-ground points. Non-ground points are then divided into clusters based on height and local neighborhood. One or more clusters are initialized based on the maximum height of the points and then each cluster is extended by applying height and neighborhood constraints. Finally, planar roof segments are extracted from each cluster of points following a region-growing technique.

  10. Extraction of Building Boundary Lines from Airborne LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Tseng, Yi-Hsing; Hung, Hsiao-Chu

    2016-10-01

    Building boundary lines are important spatial features that characterize the topographic maps and three-dimensional (3D) city models. Airborne LiDAR Point clouds provide adequate 3D spatial information for building boundary mapping. However, information of boundary features contained in point clouds is implicit. This study focuses on developing an automatic algorithm of building boundary line extraction from airborne LiDAR data. In an airborne LiDAR dataset, top surfaces of buildings, such as roofs, tend to have densely distributed points, but vertical surfaces, such as walls, usually have sparsely distributed points or even no points. The intersection lines of roof and wall planes are, therefore, not clearly defined in point clouds. This paper proposes a novel method to extract those boundary lines of building edges. The extracted line features can be used as fundamental data to generate topographic maps of 3D city model for an urban area. The proposed method includes two major process steps. The first step is to extract building boundary points from point clouds. Then the second step is followed to form building boundary line features based on the extracted boundary points. In this step, a line fitting algorithm is developed to improve the edge extraction from LiDAR data. Eight test objects, including 4 simple low buildings and 4 complicated tall buildings, were selected from the buildings in NCKU campus. The test results demonstrate the feasibility of the proposed method in extracting complicate building boundary lines. Some results which are not as good as expected suggest the need of further improvement of the method.

  11. 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.

  12. 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.

  13. 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.

  14. Distributed network, wireless and cloud computing enabled 3-D ultrasound; a new medical technology paradigm.

    PubMed

    Meir, Arie; Rubinsky, Boris

    2009-11-19

    Medical technologies are indispensable to modern medicine. However, they have become exceedingly expensive and complex and are not available to the economically disadvantaged majority of the world population in underdeveloped as well as developed parts of the world. For example, according to the World Health Organization about two thirds of the world population does not have access to medical imaging. In this paper we introduce a new medical technology paradigm centered on wireless technology and cloud computing that was designed to overcome the problems of increasing health technology costs. We demonstrate the value of the concept with an example; the design of a wireless, distributed network and central (cloud) computing enabled three-dimensional (3-D) ultrasound system. Specifically, we demonstrate the feasibility of producing a 3-D high end ultrasound scan at a central computing facility using the raw data acquired at the remote patient site with an inexpensive low end ultrasound transducer designed for 2-D, through a mobile device and wireless connection link between them. Producing high-end 3D ultrasound images with simple low-end transducers reduces the cost of imaging by orders of magnitude. It also removes the requirement of having a highly trained imaging expert at the patient site, since the need for hand-eye coordination and the ability to reconstruct a 3-D mental image from 2-D scans, which is a necessity for high quality ultrasound imaging, is eliminated. This could enable relatively untrained medical workers in developing nations to administer imaging and a more accurate diagnosis, effectively saving the lives of people.

  15. Automatic Detection of Building Points from LIDAR and Dense Image Matching Point Clouds

    NASA Astrophysics Data System (ADS)

    Maltezos, E.; Ioannidis, C.

    2015-08-01

    This study aims to detect automatically building points: (a) from LIDAR point cloud using simple techniques of filtering that enhance the geometric properties of each point, and (b) from a point cloud which is extracted applying dense image matching at high resolution colour-infrared (CIR) digital aerial imagery using the stereo method semi-global matching (SGM). At first step, the removal of the vegetation is carried out. At the LIDAR point cloud, two different methods are implemented and evaluated using initially the normals and the roughness values afterwards: (1) the proposed scan line smooth filtering and a thresholding process, and (2) a bilateral filtering and a thresholding process. For the case of the CIR point cloud, a variation of the normalized differential vegetation index (NDVI) is computed for the same purpose. Afterwards, the bare-earth is extracted using a morphological operator and removed from the rest scene so as to maintain the buildings points. The results of the extracted buildings applying each approach at an urban area in northern Greece are evaluated using an existing orthoimage as reference; also, the results are compared with the corresponding classified buildings extracted from two commercial software. Finally, in order to verify the utility and functionality of the extracted buildings points that achieved the best accuracy, the 3D models in terms of Level of Detail 1 (LoD 1) and a 3D building change detection process are indicatively performed on a sub-region of the overall scene.

  16. Maps of clouds modeled with the IPSL Titan 3D-GCM

    NASA Astrophysics Data System (ADS)

    Burgalat, J.; Rannou, P.; Lebonnois, S.

    2011-10-01

    A new climate model for Titan's atmosphere has been developed at the IPSL. This model uses the current version of the LMDZ General Circulation Model (GCM) dynamical core with the physics part of the 2D Titan's IPSL-GCM. First simulations made at the LMD (Laboratoire de Météorologie Dynamique) used a version of the model with coupled haze microphysics only. We update the model with the implementation of the clouds microphysics scheme inherited frome the previous 2D version. The model is now fully coupled with clouds processes and is a full 3D extension of the Titan IPSL-GCM ([2], [3]). Currently the model is not optimized and is demanding in term of computational time (approximatively 17 days of execution for one Titan's year simulation) and the model can not be used with its full capacities. Therefore all the microphysics is still computed as zonal averages. Nevertheless, new simulations performed including clouds, shows some encouraging results. The lack of asymmetry of the clouds coverage in the results of the 2D simulations. seems to vanish using the new model which tends to show that dissipation process in the 2D model was too strong. With this new model, we intented to get a better tool to understand Titan's climate and to interpret the large amount of data collected by the probes.

  17. Maps of clouds modeled with the IPSL Titan 3D-GCM

    NASA Astrophysics Data System (ADS)

    Burgalat, J.; Rannou, P.; Lebonnois, S.

    2012-09-01

    A new climate model for Titan's atmosphere has been developed at the IPSL. This model uses the current version of the LMDZ General Circulation Model (GCM) dynamical core with the physics part of the 2D Titan's IPSL-GCM. First simulations made at the LMD (Laboratoire de Météorologie Dynamique) used a version of the model with coupled haze microphysics only. We update the model with the implementation of the clouds microphysics scheme inherited from the previous 2D version. The model is now fully coupled with clouds processes and is a full 3D extension of the Titan IPSL-GCM ([2], [3]). Currently the model is not optimized and is demanding in term of computational time (approximatively 17 days of execution for one Titan's year simulation) and the model can not be used with its full capacities. Therefore all the microphysics is still computed as zonal averages. Nevertheless, new simulations performed including clouds, shows some encouraging results. The lack of asymmetry of the clouds coverage in the results of the 2D simulations seems to vanish using the new model which tends to show that dissipation process in the 2D model was too strong. With this new model, we intented to get a better tool to understand Titan's climate and to interpret the large amount of data collected by the probes.

  18. 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.

  19. Progress in Understanding the Impacts of 3-D Cloud Structure on MODIS Cloud Property Retrievals for Marine Boundary Layer Clouds

    NASA Technical Reports Server (NTRS)

    Zhang, Zhibo; Werner, Frank; Miller, Daniel; Platnick, Steven; Ackerman, Andrew; DiGirolamo, Larry; Meyer, Kerry; Marshak, Alexander; Wind, Galina; Zhao, Guangyu

    2016-01-01

    Theory: A novel framework based on 2-D Tayler expansion for quantifying the uncertainty in MODIS retrievals caused by sub-pixel reflectance inhomogeneity. (Zhang et al. 2016). How cloud vertical structure influences MODIS LWP retrievals. (Miller et al. 2016). Observation: Analysis of failed MODIS cloud property retrievals. (Cho et al. 2015). Cloud property retrievals from 15m resolution ASTER observations. (Werner et al. 2016). Modeling: LES-Satellite observation simulator (Zhang et al. 2012, Miller et al. 2016).

  20. Convergence of the point vortex method for the 3-D Euler equations

    NASA Astrophysics Data System (ADS)

    Hou, Thomas Y.; Lowengrub, John

    1990-11-01

    Consistency, stability, and convergence of a point vortex approximation to the 3-D incompressible Euler equations with smooth solutions. The 3-D algorithm considered is similar to the corresponding 3-D vortex are proved blob algorithm introduced by Beale and Majda; The discretization error is second-order accurate. Then the method is stable in l sup p norm for the particle trajectories and in w sup -1,p norm for discrete vorticity. Consequently, the method converges up to any time for which the Euler equations have a smooth solution. One immediate application of the convergence result is that the vortex filament method without smoothing also converges.

  1. Human body 3D posture estimation using significant points and two cameras.

    PubMed

    Juang, Chia-Feng; Chen, Teng-Chang; Du, Wei-Chin

    2014-01-01

    This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures.

  2. Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions

    PubMed Central

    Shuang, Bo; Wang, Wenxiao; Shen, Hao; Tauzin, Lawrence J.; Flatebo, Charlotte; Chen, Jianbo; Moringo, Nicholas A.; Bishop, Logan D. C.; Kelly, Kevin F.; Landes, Christy F.

    2016-01-01

    Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions. PMID:27488312

  3. 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.

  4. 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.

  5. 3D reconstruction method from biplanar radiography using non-stereocorresponding points and elastic deformable meshes.

    PubMed

    Mitton, D; Landry, C; Véron, S; Skalli, W; Lavaste, F; De Guise, J A

    2000-03-01

    Standard 3D reconstruction of bones using stereoradiography is limited by the number of anatomical landmarks visible in more than one projection. The proposed technique enables the 3D reconstruction of additional landmarks that can be identified in only one of the radiographs. The principle of this method is the deformation of an elastic object that respects stereocorresponding and non-stereocorresponding observations available in different projections. This technique is based on the principle that any non-stereocorresponding point belongs to a line joining the X-ray source and the projection of the point in one view. The aim is to determine the 3D position of these points on their line of projection when submitted to geometrical and topological constraints. This technique is used to obtain the 3D geometry of 18 cadaveric upper cervical vertebrae. The reconstructed geometry obtained is compared with direct measurements using a magnetic digitiser. The order of precision determined with the point-to-surface distance between the reconstruction obtained with that technique and reference measurements is about 1 mm, depending on the vertebrae studied. Comparison results indicate that the obtained reconstruction is close to the actual vertebral geometry. This method can therefore be proposed to obtain the 3D geometry of vertebrae.

  6. 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

  7. Congruence analysis of point clouds from unstable stereo image sequences

    NASA Astrophysics Data System (ADS)

    Jepping, C.; Bethmann, F.; Luhmann, T.

    2014-06-01

    This paper deals with the correction of exterior orientation parameters of stereo image sequences over deformed free-form surfaces without control points. Such imaging situation can occur, for example, during photogrammetric car crash test recordings where onboard high-speed stereo cameras are used to measure 3D surfaces. As a result of such measurements 3D point clouds of deformed surfaces are generated for a complete stereo sequence. The first objective of this research focusses on the development and investigation of methods for the detection of corresponding spatial and temporal tie points within the stereo image sequences (by stereo image matching and 3D point tracking) that are robust enough for a reliable handling of occlusions and other disturbances that may occur. The second objective of this research is the analysis of object deformations in order to detect stable areas (congruence analysis). For this purpose a RANSAC-based method for congruence analysis has been developed. This process is based on the sequential transformation of randomly selected point groups from one epoch to another by using a 3D similarity transformation. The paper gives a detailed description of the congruence analysis. The approach has been tested successfully on synthetic and real image data.

  8. Reconstruction of 3D Shapes of Opaque Cumulus Clouds from Airborne Multiangle Imaging: A Proof-of-Concept

    NASA Astrophysics Data System (ADS)

    Davis, A. B.; Bal, G.; Chen, J.

    2015-12-01

    Operational remote sensing of microphysical and optical cloud properties is invariably predicated on the assumption of plane-parallel slab geometry for the targeted cloud. The sole benefit of this often-questionable assumption about the cloud is that it leads to one-dimensional (1D) radiative transfer (RT)---a textbook, computationally tractable model. We present new results as evidence that, thanks to converging advances in 3D RT, inverse problem theory, algorithm implementation, and computer hardware, we are at the dawn of a new era in cloud remote sensing where we can finally go beyond the plane-parallel paradigm. Granted, the plane-parallel/1D RT assumption is reasonable for spatially extended stratiform cloud layers, as well as the smoothly distributed background aerosol layers. However, these 1D RT-friendly scenarios exclude cases that are critically important for climate physics. 1D RT---whence operational cloud remote sensing---fails catastrophically for cumuliform clouds that have fully 3D outer shapes and internal structures driven by shallow or deep convection. For these situations, the first order of business in a robust characterization by remote sensing is to abandon the slab geometry framework and determine the 3D geometry of the cloud, as a first step toward bone fide 3D cloud tomography. With this specific goal in mind, we deliver a proof-of-concept for an entirely new kind of remote sensing applicable to 3D clouds. It is based on highly simplified 3D RT and exploits multi-angular suites of cloud images at high spatial resolution. Airborne sensors like AirMSPI readily acquire such data. The key element of the reconstruction algorithm is a sophisticated solution of the nonlinear inverse problem via linearization of the forward model and an iteration scheme supported, where necessary, by adaptive regularization. Currently, the demo uses a 2D setting to show how either vertical profiles or horizontal slices of the cloud can be accurately reconstructed

  9. Examination about Influence for Precision of 3d Image Measurement from the Ground Control Point Measurement and Surface Matching

    NASA Astrophysics Data System (ADS)

    Anai, T.; Kochi, N.; Yamada, M.; Sasaki, T.; Otani, H.; Sasaki, D.; Nishimura, S.; Kimoto, K.; Yasui, N.

    2015-05-01

    As the 3D image measurement software is now widely used with the recent development of computer-vision technology, the 3D measurement from the image is now has acquired the application field from desktop objects as wide as the topography survey in large geographical areas. Especially, the orientation, which used to be a complicated process in the heretofore image measurement, can be now performed automatically by simply taking many pictures around the object. And in the case of fully textured object, the 3D measurement of surface features is now done all automatically from the orientated images, and greatly facilitated the acquisition of the dense 3D point cloud from images with high precision. With all this development in the background, in the case of small and the middle size objects, we are now furnishing the all-around 3D measurement by a single digital camera sold on the market. And we have also developed the technology of the topographical measurement with the air-borne images taken by a small UAV [1~5]. In this present study, in the case of the small size objects, we examine the accuracy of surface measurement (Matching) by the data of the experiments. And as to the topographic measurement, we examine the influence of GCP distribution on the accuracy by the data of the experiments. Besides, we examined the difference of the analytical results in each of the 3D image measurement software. This document reviews the processing flow of orientation and the 3D measurement of each software and explains the feature of the each software. And as to the verification of the precision of stereo-matching, we measured the test plane and the test sphere of the known form and assessed the result. As to the topography measurement, we used the air-borne image data photographed at the test field in Yadorigi of Matsuda City, Kanagawa Prefecture JAPAN. We have constructed Ground Control Point which measured by RTK-GPS and Total Station. And we show the results of analysis made

  10. LIVAS: a 3-D multi-wavelength aerosol/cloud database based on CALIPSO and EARLINET

    NASA Astrophysics Data System (ADS)

    Amiridis, V.; Marinou, E.; Tsekeri, A.; Wandinger, U.; Schwarz, A.; Giannakaki, E.; Mamouri, R.; Kokkalis, P.; Binietoglou, I.; Solomos, S.; Herekakis, T.; Kazadzis, S.; Gerasopoulos, E.; Proestakis, E.; Kottas, M.; Balis, D.; Papayannis, A.; Kontoes, C.; Kourtidis, K.; Papagiannopoulos, N.; Mona, L.; Pappalardo, G.; Le Rille, O.; Ansmann, A.

    2015-07-01

    We present LIVAS (LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies), a 3-D multi-wavelength global aerosol and cloud optical database, optimized to be used for future space-based lidar end-to-end simulations of realistic atmospheric scenarios as well as retrieval algorithm testing activities. The LIVAS database provides averaged profiles of aerosol optical properties for the potential spaceborne laser operating wavelengths of 355, 532, 1064, 1570 and 2050 nm and of cloud optical properties at the wavelength of 532 nm. The global database is based on CALIPSO observations at 532 and 1064 nm and on aerosol-type-dependent backscatter- and extinction-related Ångström exponents, derived from EARLINET (European Aerosol Research Lidar Network) ground-based measurements for the UV and scattering calculations for the IR wavelengths, using a combination of input data from AERONET, suitable aerosol models and recent literature. The required spectral conversions are calculated for each of the CALIPSO aerosol types and are applied to CALIPSO backscatter and extinction data corresponding to the aerosol type retrieved by the CALIPSO aerosol classification scheme. A cloud optical database based on CALIPSO measurements at 532 nm is also provided, neglecting wavelength conversion due to approximately neutral scattering behavior of clouds along the spectral range of LIVAS. Averages of particle linear depolarization ratio profiles at 532 nm are provided as well. Finally, vertical distributions for a set of selected scenes of specific atmospheric phenomena (e.g., dust outbreaks, volcanic eruptions, wild fires, polar stratospheric clouds) are analyzed and spectrally converted so as to be used as case studies for spaceborne lidar performance assessments. The final global data set includes 4-year (1 January 2008-31 December 2011) time-averaged CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) data on a uniform grid of 1

  11. 3D Cloud Radiative Effects on Aerosol Optical Thickness Retrievals in Cumulus Cloud Fields in the Biomass Burning Region in Brazil

    NASA Technical Reports Server (NTRS)

    Wen, Guo-Yong; Marshak, Alexander; Cahalan, Robert F.

    2004-01-01

    Aerosol amount in clear regions of a cloudy atmosphere is a critical parameter in studying the interaction between aerosols and clouds. Since the global cloud cover is about 50%, cloudy scenes are often encountered in any satellite images. Aerosols are more or less transparent, while clouds are extremely reflective in the visible spectrum of solar radiation. The radiative transfer in clear-cloudy condition is highly three- dimensional (3D). This paper focuses on estimating the 3D effects on aerosol optical thickness retrievals using Monte Carlo simulations. An ASTER image of cumulus cloud fields in the biomass burning region in Brazil is simulated in this study. The MODIS products (i-e., cloud optical thickness, particle effective radius, cloud top pressure, surface reflectance, etc.) are used to construct the cloud property and surface reflectance fields. To estimate the cloud 3-D effects, we assume a plane-parallel stratification of aerosol properties in the 60 km x 60 km ASTER image. The simulated solar radiation at the top of the atmosphere is compared with plane-parallel calculations. Furthermore, the 3D cloud radiative effects on aerosol optical thickness retrieval are estimated.

  12. Status of the phenomena representation, 3D modeling, and cloud-based software architecture development

    SciTech Connect

    Smith, Curtis L.; Prescott, Steven; Kvarfordt, Kellie; Sampath, Ram; Larson, Katie

    2015-09-01

    Early in 2013, researchers at the Idaho National Laboratory outlined a technical framework to support the implementation of state-of-the-art probabilistic risk assessment to predict the safety performance of advanced small modular reactors. From that vision of the advanced framework for risk analysis, specific tasks have been underway in order to implement the framework. This report discusses the current development of a several tasks related to the framework implementation, including a discussion of a 3D physics engine that represents the motion of objects (including collision and debris modeling), cloud-based analysis tools such as a Bayesian-inference engine, and scenario simulations. These tasks were performed during 2015 as part of the technical work associated with the Advanced Reactor Technologies Program.

  13. Self-Consistent 3D Modeling of Electron Cloud Dynamics and Beam Response

    SciTech Connect

    Furman, Miguel; Furman, M.A.; Celata, C.M.; Kireeff-Covo, M.; Sonnad, K.G.; Vay, J.-L.; Venturini, M.; Cohen, R.; Friedman, A.; Grote, D.; Molvik, A.; Stoltz, P.

    2007-04-02

    We present recent advances in the modeling of beam electron-cloud dynamics, including surface effects such as secondary electron emission, gas desorption, etc, and volumetric effects such as ionization of residual gas and charge-exchange reactions. Simulations for the HCX facility with the code WARP/POSINST will be described and their validity demonstrated by benchmarks against measurements. The code models a wide range of physical processes and uses a number of novel techniques, including a large-timestep electron mover that smoothly interpolates between direct orbit calculation and guiding-center drift equations, and a new computational technique, based on a Lorentz transformation to a moving frame, that allows the cost of a fully 3D simulation to be reduced to that of a quasi-static approximation.

  14. Video-Based Point Cloud Generation Using Multiple Action Cameras

    NASA Astrophysics Data System (ADS)

    Teo, T.

    2015-05-01

    Due to the development of action cameras, the use of video technology for collecting geo-spatial data becomes an important trend. The objective of this study is to compare the image-mode and video-mode of multiple action cameras for 3D point clouds generation. Frame images are acquired from discrete camera stations while videos are taken from continuous trajectories. The proposed method includes five major parts: (1) camera calibration, (2) video conversion and alignment, (3) orientation modelling, (4) dense matching, and (5) evaluation. As the action cameras usually have large FOV in wide viewing mode, camera calibration plays an important role to calibrate the effect of lens distortion before image matching. Once the camera has been calibrated, the author use these action cameras to take video in an indoor environment. The videos are further converted into multiple frame images based on the frame rates. In order to overcome the time synchronous issues in between videos from different viewpoints, an additional timer APP is used to determine the time shift factor between cameras in time alignment. A structure form motion (SfM) technique is utilized to obtain the image orientations. Then, semi-global matching (SGM) algorithm is adopted to obtain dense 3D point clouds. The preliminary results indicated that the 3D points from 4K video are similar to 12MP images, but the data acquisition performance of 4K video is more efficient than 12MP digital images.

  15. Exploratory Analysis Of The 3D Cloud Resolving Model Simulations of TOGA COARE: Preliminary Results

    NASA Astrophysics Data System (ADS)

    Mendes, S.; Bretherton, C.

    2007-12-01

    Global climate model studies suggest that cumulus momentum transport (CMT) in tropical oceanic convective cloud systems plays a significant role in the tropical mean circulation and transient variability. CMT is difficult to measure directly and can depend on the detailed structure and organization of the convection. Yet there have been comparatively few evaluations of CMT parameterizations and the assumptions underlying them using 3D cloud resolving model (CRM) simulations. We have analyzed CMT in a four month 3D 64x64x64 gridpoint CRM simulation of TOGA COARE with 1 km horizontal resolution. An additional 256x256x64 large-domain simulation was performed for a 10 day subperiod with strong convection combined with substantial mean vertical zonal wind shear, conditions favorably for strong CMT. Both simulations were identically forced with prescribed vertical motion, horizontal temperature and moisture advection, and relaxation of the domain-mean wind profile to observations on a one-hour timescale. Both were initialized with small amplitude white noise, but spun up realistic convection in less than a day. The domain-mean CMT in the small and large domain simulations for the 10-day common simulation period was compared. The two simulations showed remarkably similar CMT profiles on daily-mean timescales, suggesting that mesoscale contributions to CMT of scales greater than 64 km were small. The skill of a downgradient mixing-length parameterization CMT = Mc*L*DU/Dz was also tested. Here , Mc is convective mass flux, dU/dz is mean vertical shear, and L is a mixing length for updraft zonal velocity perturbations associated with entrainment and horizontal pressure gradient accelerations. This was done by regressing CMT at each height was regressed against Mc*DU/Dz at the same height across all 3D model snapshots over the 10 days. The correlation coefficient describes the accuracy of this downgradient parameterization, and L was calculated as the regression slope. In the

  16. 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.

  17. 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.

  18. Comparative analysis of video processing and 3D rendering for cloud video games using different virtualization technologies

    NASA Astrophysics Data System (ADS)

    Bada, Adedayo; Alcaraz-Calero, Jose M.; Wang, Qi; Grecos, Christos

    2014-05-01

    This paper describes a comprehensive empirical performance evaluation of 3D video processing employing the physical/virtual architecture implemented in a cloud environment. Different virtualization technologies, virtual video cards and various 3D benchmarks tools have been utilized in order to analyse the optimal performance in the context of 3D online gaming applications. This study highlights 3D video rendering performance under each type of hypervisors, and other factors including network I/O, disk I/O and memory usage. Comparisons of these factors under well-known virtual display technologies such as VNC, Spice and Virtual 3D adaptors reveal the strengths and weaknesses of the various hypervisors with respect to 3D video rendering and streaming.

  19. An Evaluation of the Observational Capabilities of A Scanning 95-GHz Radar in Studying the 3D Structures of Marine Stratocumulus Clouds

    NASA Astrophysics Data System (ADS)

    Bowley, Kevin

    Marine stratocumulus clouds play a critical role in Earth's radiative balance primarily due to the role of their high albedo reflecting incoming solar radiation, causing a cooling effect, while weakly reflecting outgoing infrared radiation. Characterization of the 3-Dimensional (3D) structure of these cloud systems over scales of 20-40 km is required to accurately account for the role of cloud inhomogeneity and structure on their shortwave forcing and lifetime, which has important applications for Global Climate Models. For first time, such 3D measurements in clouds were made available from a scanning cloud radar during the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) field campaign in the Azores Islands. The scanning radar observations were complemented by a suite of zenith-pointing active and passive remote sensors that were deployed to provide a detailed description of marine stratus over a long-term observation period in the ideal marine environment commonly found at the Azores. The scanning cloud radar observations present a shift from a multi-instrument, vertically pointing 'soda-straw' observation technique to a radar-only, 'radar-centric' observation technique. The scanning radar observations were gridded using a nearest-neighbor type scheme devised to take the natural variability of the observed field into account. The ability of the scheme to capture primary cloud properties (cloud fraction, cloud boundaries, drizzle detection) was assessed using measurements from the vertically pointing sensors. Despite the great sensitivity of the scanning cloud radar (-42.5 dBZ at 1 km range), the drop in sensitivity with range resulted in an artificial thinning of clouds with range from the radar. Drizzle-free cloud structures were undetectable beyond 5 km from the radar. Cloud fields containing drizzle were generally detectable to ranges exceeding 10 km from

  20. LIDAR vs dense image matching point clouds in complex urban scenes

    NASA Astrophysics Data System (ADS)

    Maltezos, Evangelos; Kyrkou, Athanasia; Ioannidis, Charalabos

    2016-08-01

    This study aims to highlight the differences, in terms of robustness and efficiency, of the use of LIDAR point clouds compared to dense image matching (DIM) point clouds at urban areas that contain buildings with complex structure. The application is conducted over an area in the Greek island of Milos using two different types of data: (a) a dense point cloud which extracted by DIM using a variation of the stereo-method semi-global matching (SGM) at RGB digital aerial images, and (b) a georeferenced LIDAR point cloud. For the case of the DIM point cloud, the following steps were applied: aerial triangulation, rectification of the original images to epipolar images, extraction of disparity maps and application of a 3D similarity transformation. The evaluations that were executed included urban and rural areas. At first step, a direct cloud-to-cloud comparison between the georeferenced DIM and LIDAR point clouds was carried out. Then, the corresponding orthoimages generated by the DIM and LIDAR point clouds undergo a quality control. Although the results show that the LIDAR point clouds respond better at such complex scenes compared to DIM point clouds, the latter gave promising results. In this context, the Quality Assurance issue is also discussed so as to be more efficient towards the challenge of the increasingly greater demands for accurate and cost effective applications.

  1. Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion.

    PubMed

    Skraba, Primoz; Rosen, Paul; Wang, Bei; Chen, Guoning; Bhatia, Harsh; Pascucci, Valerio

    2016-02-29

    Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with a guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. We apply our method to synthetic and simulation datasets to demonstrate its effectiveness.

  2. 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.

  3. Architecture of web services in the enhancement of real-time 3D video virtualization in cloud environment

    NASA Astrophysics Data System (ADS)

    Bada, Adedayo; Wang, Qi; Alcaraz-Calero, Jose M.; Grecos, Christos

    2016-04-01

    This paper proposes a new approach to improving the application of 3D video rendering and streaming by jointly exploring and optimizing both cloud-based virtualization and web-based delivery. The proposed web service architecture firstly establishes a software virtualization layer based on QEMU (Quick Emulator), an open-source virtualization software that has been able to virtualize system components except for 3D rendering, which is still in its infancy. The architecture then explores the cloud environment to boost the speed of the rendering at the QEMU software virtualization layer. The capabilities and inherent limitations of Virgil 3D, which is one of the most advanced 3D virtual Graphics Processing Unit (GPU) available, are analyzed through benchmarking experiments and integrated into the architecture to further speed up the rendering. Experimental results are reported and analyzed to demonstrate the benefits of the proposed approach.

  4. 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.

  5. LIDAR, Point Clouds, and their Archaeological Applications

    SciTech Connect

    White, Devin A

    2013-01-01

    It is common in contemporary archaeological literature, in papers at archaeological conferences, and in grant proposals to see heritage professionals use the term LIDAR to refer to high spatial resolution digital elevation models and the technology used to produce them. The goal of this chapter is to break that association and introduce archaeologists to the world of point clouds, in which LIDAR is only one member of a larger family of techniques to obtain, visualize, and analyze three-dimensional measurements of archaeological features. After describing how point clouds are constructed, there is a brief discussion on the currently available software and analytical techniques designed to make sense of them.

  6. Cloud 3D Effects Evidenced in Landsat Power Spectra and Autocorrelation Functions

    NASA Technical Reports Server (NTRS)

    Oreopoulos, Lazaros; Marshak, Alexander; Cahalan, Robert F.; Wen, Guoyong

    1999-01-01

    the spectral signatures of decorrelation between reflectance and optical depth at large scales becoming stronger as the magnitude of cloud top variations increase. Finally, the usefulness of power spectral analysis in evaluating the skill of novel optical depth retrieval techniques in removing 3D radiative effects is demonstrated. New techniques using inverse Non-local Independent Pixel Approximation (NIPA) and Normalized Difference of Nadir Reflectivity (NDNR) yield optical depth fields which better match the scale-by-scale variability of the true optical depth field.

  7. Extractive microbial fermentation in cloud point system.

    PubMed

    Wang, Zhilong; Dai, Zewen

    2010-05-05

    Extractive microbial fermentation of organic compounds in liquid-liquid two-phase systems is a potential strategy to overcome the limitations of microbial fermentation in an aqueous solution, such as low substrate solubility, substrate/product inhibition and product further degradation. A conventional aqueous-organic solvent two-phase system is inaccessible to extractive fermentation of a relatively high polar bioproduct as the confliction between the biocompatibility and the extraction ability of the corresponding organic solvent. An exploitation of cloud point system as a novel medium engineering method for extractive microbial fermentation is reviewed in present work. The relationship between phase separation of nonionic surfactant aqueous solution forming cloud point system and its corresponding biocompatibility to microorganisms, and the relationship between solubilization and bioavailability of organic compounds in a cloud point system are discussed. Paradigms of extractive microbial fermentation in cloud point system are highlighted with some cases in our lab. The downstream processing for nonionic surfactant recovery and product separation with microemulsion extraction is also presented.

  8. Overview of 3D-TRACE, a NASA Initiative in Three-Dimensional Tomography of the Aerosol-Cloud Environment

    NASA Astrophysics Data System (ADS)

    Davis, Anthony; Diner, David; Yanovsky, Igor; Garay, Michael; Xu, Feng; Bal, Guillaume; Schechner, Yoav; Aides, Amit; Qu, Zheng; Emde, Claudia

    2013-04-01

    Remote sensing is a key tool for sorting cloud ensembles by dynamical state, aerosol environments by source region, and establishing causal relationships between aerosol amounts, type, and cloud microphysics-the so-called indirect aerosol climate impacts, and one of the main sources of uncertainty in current climate models. Current satellite imagers use data processing approaches that invariably start with cloud detection/masking to isolate aerosol air-masses from clouds, and then rely on one-dimensional (1D) radiative transfer (RT) to interpret the aerosol and cloud measurements in isolation. Not only does this lead to well-documented biases for the estimates of aerosol radiative forcing and cloud optical depths in current missions, but it is fundamentally inadequate for future missions such as EarthCARE where capturing the complex, three-dimensional (3D) interactions between clouds and aerosols is a primary objective. In order to advance the state of the art, the next generation of satellite information processing systems must incorporate technologies that will enable the treatment of the atmosphere as a fully 3D environment, represented more realistically as a continuum. At one end, there is an optically thin background dominated by aerosols and molecular scattering that is strongly stratified and relatively homogeneous in the horizontal. At the other end, there are optically thick embedded elements, clouds and aerosol plumes, which can be more or less uniform and quasi-planar or else highly 3D with boundaries in all directions; in both cases, strong internal variability may be present. To make this paradigm shift possible, we propose to combine the standard models for satellite signal prediction physically grounded in 1D and 3D RT, both scalar and vector, with technologies adapted from biomedical imaging, digital image processing, and computer vision. This will enable us to demonstrate how the 3D distribution of atmospheric constituents, and their associated

  9. 3-D numerical simulations of eruption clouds: Effects of the environmental wind on the turbulent mixing

    NASA Astrophysics Data System (ADS)

    Suzuki, Y. J.; Koyaguchi, T.

    2011-12-01

    During an explosive volcanic eruption, a mixture of volcanic gas and solid pyroclasts are ejected from a volcanic vent with a high temperature. As it rises, the mixture entrains ambient air owing to turbulent mixing. The entrained air expands by heating from the hot pyroclasts, and the eruption cloud (i.e., the ejected material plus the entrained air) rises as a buoyant plume. Because the plume height is principally determined by the balance between the thermal energy ejected at the vent and the work done in transporting the ejected material plus entrained air through the atmospheric stratification, it is controlled by the efficiency of turbulent mixing; as the amount of entrained air increases, the plume height decreases. In the 1-D models of eruption column (e.g., Woods, 1988), the plume height is calculated on the assumption that the mean inflow velocity across the edge of turbulent jet and/or plume is proportional to the mean vertical velocity (Morton et al., 1956). Experimental studies suggest that the proportionality constant (i.e., entrainment coefficient, k), which represents the efficiency of turbulent mixing, is about 0.10 for pure plumes when there is no wind. When an environmental wind is present, however, the interaction between a buoyant plume and the wind may enhance the entrainment of air and can significantly decrease the plume height (Bursik, 2001). In order to investigate the effects of wind on the vortical structures and the efficiency of turbulent mixing in an eruption cloud, we have carried out 3-D numerical simulations of eruption column which is ejected in a wind field. The simulation results indicate that a buoyant plume vertically rises as a "strong plume" (e.g., Bonadonna et al., 2003) when the wind velocity is low: the cloud reaches the neutral buoyancy level and overshoots until the upward momentum is exhausted. In this case, the plume height is consistent with prediction by the 1-D model with k~0.10. When the wind velocity is high, on

  10. Representing 3-D cloud radiation effects in two-stream schemes: 2. Matrix formulation and broadband evaluation

    NASA Astrophysics Data System (ADS)

    Hogan, Robin J.; Schäfer, Sophia A. K.; Klinger, Carolin; Chiu, J. Christine; Mayer, Bernhard

    2016-07-01

    Estimating the impact of radiation transport through cloud sides on the global energy budget is hampered by the lack of a fast radiation scheme suitable for use in global atmospheric models that can represent these effects in both the shortwave and longwave. This two-part paper describes the development of such a scheme, which we refer to as the Speedy Algorithm for Radiative Transfer through Cloud Sides (SPARTACUS). The principle of the method is to add extra terms to the two-stream equations to represent lateral transport between clear and cloudy regions, which vary in proportion to the length of cloud edge as a function of height. The present paper describes a robust and accurate method for solving the coupled system of equations in both the shortwave and longwave in terms of matrix exponentials. This solver has been coupled to a correlated-k model for gas absorption. We then confirm the accuracy of SPARTACUS by performing broadband comparisons with fully 3-D radiation calculations by the Monte Carlo model "MYSTIC" for a cumulus cloud field, examining particularly the percentage change in cloud radiative effect (CRE) when 3-D effects are introduced. In the shortwave, SPARTACUS correctly captures this change to CRE, which varies with solar zenith angle between -25% and +120%. In the longwave, SPARTACUS captures well the increase in radiative cooling of the cloud, although it is only able to correctly simulate the 30% increase in surface CRE (around 4 W m-2) if an approximate correction is made for cloud clustering.

  11. Error analysis in stereo vision for location measurement of 3D point

    NASA Astrophysics Data System (ADS)

    Li, Yunting; Zhang, Jun; Tian, Jinwen

    2015-12-01

    Location measurement of 3D point in stereo vision is subjected to different sources of uncertainty that propagate to the final result. For current methods of error analysis, most of them are based on ideal intersection model to calculate the uncertainty region of point location via intersecting two fields of view of pixel that may produce loose bounds. Besides, only a few of sources of error such as pixel error or camera position are taken into account in the process of analysis. In this paper we present a straightforward and available method to estimate the location error that is taken most of source of error into account. We summed up and simplified all the input errors to five parameters by rotation transformation. Then we use the fast algorithm of midpoint method to deduce the mathematical relationships between target point and the parameters. Thus, the expectations and covariance matrix of 3D point location would be obtained, which can constitute the uncertainty region of point location. Afterwards, we turned back to the error propagation of the primitive input errors in the stereo system and throughout the whole analysis process from primitive input errors to localization error. Our method has the same level of computational complexity as the state-of-the-art method. Finally, extensive experiments are performed to verify the performance of our methods.

  12. Lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns

    NASA Astrophysics Data System (ADS)

    Dong, Pinliang

    2009-10-01

    Spatial scale plays an important role in many fields. As a scale-dependent measure for spatial heterogeneity, lacunarity describes the distribution of gaps within a set at multiple scales. In Earth science, environmental science, and ecology, lacunarity has been increasingly used for multiscale modeling of spatial patterns. This paper presents the development and implementation of a geographic information system (GIS) software extension for lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns. Depending on the application requirement, lacunarity analysis can be performed in two modes: global mode or local mode. The extension works for: (1) binary (1-bit) and grey-scale datasets in any raster format supported by ArcGIS and (2) 1D, 2D, and 3D point datasets as shapefiles or geodatabase feature classes. For more effective measurement of lacunarity for different patterns or processes in raster datasets, the extension allows users to define an area of interest (AOI) in four different ways, including using a polygon in an existing feature layer. Additionally, directionality can be taken into account when grey-scale datasets are used for local lacunarity analysis. The methodology and graphical user interface (GUI) are described. The application of the extension is demonstrated using both simulated and real datasets, including Brodatz texture images, a Spaceborne Imaging Radar (SIR-C) image, simulated 1D points on a drainage network, and 3D random and clustered point patterns. The options of lacunarity analysis and the effects of polyline arrangement on lacunarity of 1D points are also discussed. Results from sample data suggest that the lacunarity analysis extension can be used for efficient modeling of spatial patterns at multiple scales.

  13. Street environment change detection from mobile laser scanning point clouds

    NASA Astrophysics Data System (ADS)

    Xiao, Wen; Vallet, Bruno; Brédif, Mathieu; Paparoditis, Nicolas

    2015-09-01

    Mobile laser scanning (MLS) has become a popular technique for road inventory, building modelling, infrastructure management, mobility assessment, etc. Meanwhile, due to the high mobility of MLS systems, it is easy to revisit interested areas. However, change detection using MLS data of street environment has seldom been studied. In this paper, an approach that combines occupancy grids and a distance-based method for change detection from MLS point clouds is proposed. Unlike conventional occupancy grids, our occupancy-based method models space based on scanning rays and local point distributions in 3D without voxelization. A local cylindrical reference frame is presented for the interpolation of occupancy between rays according to the scanning geometry. The Dempster-Shafer theory (DST) is utilized for both intra-data evidence fusion and inter-data consistency assessment. Occupancy of reference point cloud is fused at the location of target points and then the consistency is evaluated directly on the points. A point-to-triangle (PTT) distance-based method is combined to improve the occupancy-based method. Because it is robust to penetrable objects, e.g. vegetation, which cause self-conflicts when modelling occupancy. The combined method tackles irregular point density and occlusion problems, also eliminates false detections on penetrable objects.

  14. Detectability limitations with 3-D point reconstruction algorithms using digital radiography

    SciTech Connect

    Lindgren, Erik

    2015-03-31

    The estimated impact of pores in clusters on component fatigue will be highly conservative when based on 2-D rather than 3-D pore positions. To 3-D position and size defects using digital radiography and 3-D point reconstruction algorithms in general require a lower inspection time and in some cases work better with planar geometries than X-ray computed tomography. However, the increase in prior assumptions about the object and the defects will increase the intrinsic uncertainty in the resulting nondestructive evaluation output. In this paper this uncertainty arising when detecting pore defect clusters with point reconstruction algorithms is quantified using simulations. The simulation model is compared to and mapped to experimental data. The main issue with the uncertainty is the possible masking (detectability zero) of smaller defects around some other slightly larger defect. In addition, the uncertainty is explored in connection to the expected effects on the component fatigue life and for different amount of prior object-defect assumptions made.

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

    DTIC Science & Technology

    2009-03-23

    are introduced through various examples that illustrate the performance and efficiency of the new methods Key Words: Surface fitting, point clouds , implicit...NAMES AND ADDRESSES U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS Surface fitting, point ... clouds , implicit least squares, isosurfaces, noisy 3D data, scattered data approximation Gregory M. Nielson Arizona State University Office of Research

  16. 3D Cloud Tomography, Followed by Mean Optical and Microphysical Properties, with Multi-Angle/Multi-Pixel Data

    NASA Astrophysics Data System (ADS)

    Davis, A. B.; von Allmen, P. A.; Marshak, A.; Bal, G.

    2010-12-01

    The geometrical assumption in all operational cloud remote sensing algorithms is that clouds are plane-parallel slabs, which applies relatively well to the most uniform stratus layers. Its benefit is to justify using classic 1D radiative transfer (RT) theory, where angular details (solar, viewing, azimuthal) are fully accounted for and precise phase functions can be used, to generate the look-up tables used in the retrievals. Unsurprisingly, these algorithms catastrophically fail when applied to cumulus-type clouds, which are highly 3D. This is unfortunate for the cloud-process modeling community that may thrive on in situ airborne data, but would very much like to use satellite data for more than illustrations in their presentations and publications. So, how can we obtain quantitative information from space-based observations of finite aspect ratio clouds? Cloud base/top heights, vertically projected area, mean liquid water content (LWC), and volume-averaged droplet size would be a good start. Motivated by this science need, we present a new approach suitable for sparse cumulus fields where we turn the tables on the standard procedure in cloud remote sensing. We make no a priori assumption about cloud shape, save an approximately flat base, but use brutal approximations about the RT that is necessarily 3D. Indeed, the first order of business is to roughly determine the cloud's outer shape in one of two ways, which we will frame as competing initial guesses for the next phase of shape refinement and volume-averaged microphysical parameter estimation. Both steps use multi-pixel/multi-angle techniques amenable to MISR data, the latter adding a bi-spectral dimension using collocated MODIS data. One approach to rough cloud shape determination is to fit the multi-pixel/multi-angle data with a geometric primitive such as a scalene hemi-ellipsoid with 7 parameters (translation in 3D space, 3 semi-axes, 1 azimuthal orientation); for the radiometry, a simple radiosity

  17. Steady state reconnection at a single 3D magnetic null point

    NASA Astrophysics Data System (ADS)

    Galsgaard, K.; Pontin, D. I.

    2011-05-01

    Aims: We systematically stress a rotationally symmetric 3D magnetic null point by advecting the opposite footpoints of the spine axis in opposite directions. This stress eventually concentrates in the vicinity of the null point, thereby forming a local current sheet through which magnetic reconnection takes place. The aim is to look for a steady state evolution of the current sheet dynamics, which may provide scaling relations for various characteristic parameters of the system. Methods: The evolution is followed by solving numerically the non-ideal MHD equations in a Cartesian domain. The null point is embedded in an initially constant density and temperature plasma. Results: It is shown that a quasi-steady reconnection process can be set up at a 3D null by continuous shear driving. It appears that a true steady state is unlikely to be realised because the current layer tends to grow until it is restricted by the geometry of the computational domain and the imposed driving profile. However, ratios between characteristic quantities clearly settle after some time to stable values, so that the evolution is quasi-steady. The experiments show a number of scaling relations, but they do not provide a clear consensus for extending to lower magnetic resistivity or faster driving velocities. More investigations are needed to fully clarify the properties of current sheets at magnetic null points.

  18. Articulated Non-Rigid Point Set Registration for Human Pose Estimation from 3D Sensors

    PubMed Central

    Ge, Song; Fan, Guoliang

    2015-01-01

    We propose a generative framework for 3D human pose estimation that is able to operate on both individual point sets and sequential depth data. We formulate human pose estimation as a point set registration problem, where we propose three new approaches to address several major technical challenges in this research. First, we integrate two registration techniques that have a complementary nature to cope with non-rigid and articulated deformations of the human body under a variety of poses. This unique combination allows us to handle point sets of complex body motion and large pose variation without any initial conditions, as required by most existing approaches. Second, we introduce an efficient pose tracking strategy to deal with sequential depth data, where the major challenge is the incomplete data due to self-occlusions and view changes. We introduce a visible point extraction method to initialize a new template for the current frame from the previous frame, which effectively reduces the ambiguity and uncertainty during registration. Third, to support robust and stable pose tracking, we develop a segment volume validation technique to detect tracking failures and to re-initialize pose registration if needed. The experimental results on both benchmark 3D laser scan and depth datasets demonstrate the effectiveness of the proposed framework when compared with state-of-the-art algorithms. PMID:26131673

  19. Robust affine-invariant feature points matching for 3D surface reconstruction of complex landslide scenes

    NASA Astrophysics Data System (ADS)

    Stumpf, André; Malet, Jean-Philippe; Allemand, Pascal; Skupinski, Grzegorz; Deseilligny, Marc-Pierrot

    2013-04-01

    Multi-view stereo surface reconstruction from dense terrestrial photographs is being increasingly applied for geoscience applications such as quantitative geomorphology, and a number of different software solution and processing streamlines have been suggested. For image matching, camera self-calibration and bundle block adjustment, most approaches make use of scale-invariant feature transform (SIFT) to identify homologous points in multiple images. SIFT-like point matching is robust to apparent translation, rotation, and scaling of objects in multiple viewing geometries but the number of correctly identified matching points typically declines drastically with increasing angles between the viewpoints. For the application of multi-view stereo of complex landslide scenes, the viewing geometry is often constrained by the local topography and barriers such as rocks and vegetation occluding the target. Under such conditions it is not uncommon to encounter view angle differences of > 30% that hinder the image matching and eventually prohibit the joint estimation of the camera parameters from all views. Recently an affine invariant extension of the SIFT detector (ASIFT) has been demonstrated to provide more robust matches when large view-angle differences become an issue. In this study the ASIFT detector was adopted to detect homologous points in terrestrial photographs preceding 3D reconstruction of different parts (main scarp, toe) of the Super-Sauze landslide (Southern French Alps). 3D surface models for different time periods and different parts of the landslide were derived using the multi-view stereo framework implemented in MicMac (©IGN). The obtained 3D models were compared with reconstructions using the traditional SIFT detectors as well as alternative structure-from-motion implementations. An estimate of the absolute accuracy of the photogrammetric models was obtained through co-registration and comparison with high-resolution terrestrial LiDAR scans.

  20. Adapting histogram for automatic noise data removal in building interior point cloud data

    NASA Astrophysics Data System (ADS)

    Shukor, S. A. Abdul; Rushforth, E. J.

    2015-05-01

    3D point cloud data is now preferred by researchers to generate 3D models. These models can be used throughout a variety of applications including 3D building interior models. The rise of Building Information Modeling (BIM) for Architectural, Engineering, Construction (AEC) applications has given 3D interior modelling more attention recently. To generate a 3D model representing the building interior, a laser scanner is used to collect the point cloud data. However, this data often comes with noise. This is due to several factors including the surrounding objects, lighting and specifications of the laser scanner. This paper highlights on the usage of the histogram to remove the noise data. Histograms, used in statistics and probability, are regularly being used in a number of applications like image processing, where a histogram can represent the total number of pixels in an image at each intensity level. Here, histograms represent the number of points recorded at range distance intervals in various projections. As unwanted noise data has a sparser cloud density compared to the required data and is usually situated at a notable distance from the required data, noise data will have lower frequencies in the histogram. By defining the acceptable range using the average frequency, points below this range can be removed. This research has shown that these histograms have the capabilities to remove unwanted data from 3D point cloud data representing building interiors automatically. This feature will aid the process of data preprocessing in producing an ideal 3D model from the point cloud data.

  1. 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.

  2. Use of the ARM Measurements of Spectral Zenith Radiance for Better Understanding of 3D Cloud-Radiation Processes & Aerosol-Cloud Interaction

    SciTech Connect

    Alexander Marshak; Warren Wiscombe; Yuri Knyazikhin; Christine Chiu

    2011-05-24

    We proposed a variety of tasks centered on the following question: what can we learn about 3D cloud-radiation processes and aerosol-cloud interaction from rapid-sampling ARM measurements of spectral zenith radiance? These ARM measurements offer spectacular new and largely unexploited capabilities in both the temporal and spectral domains. Unlike most other ARM instruments, which average over many seconds or take samples many seconds apart, the new spectral zenith radiance measurements are fast enough to resolve natural time scales of cloud change and cloud boundaries as well as the transition zone between cloudy and clear areas. In the case of the shortwave spectrometer, the measurements offer high time resolution and high spectral resolution, allowing new discovery-oriented science which we intend to pursue vigorously. Research objectives are, for convenience, grouped under three themes: • Understand radiative signature of the transition zone between cloud-free and cloudy areas using data from ARM shortwave radiometers, which has major climatic consequences in both aerosol direct and indirect effect studies. • Provide cloud property retrievals from the ARM sites and the ARM Mobile Facility for studies of aerosol-cloud interactions. • Assess impact of 3D cloud structures on aerosol properties using passive and active remote sensing techniques from both ARM and satellite measurements.

  3. Point Cloud Oriented Shoulder Line Extraction in Loess Hilly Area

    NASA Astrophysics Data System (ADS)

    Min, Li; Xin, Yang; Liyang, Xiong

    2016-06-01

    Shoulder line is the significant line in hilly area of Loess Plateau in China, dividing the surface into positive and negative terrain (P-N terrains). Due to the point cloud vegetation removal methods of P-N terrains are different, there is an imperative need for shoulder line extraction. In this paper, we proposed an automatic shoulder line extraction method based on point cloud. The workflow is as below: (i) ground points were selected by using a grid filter in order to remove most of noisy points. (ii) Based on DEM interpolated by those ground points, slope was mapped and classified into two classes (P-N terrains), using Natural Break Classified method. (iii) The common boundary between two slopes is extracted as shoulder line candidate. (iv) Adjust the filter gird size and repeat step i-iii until the shoulder line candidate matches its real location. (v) Generate shoulder line of the whole area. Test area locates in Madigou, Jingbian County of Shaanxi Province, China. A total of 600 million points are acquired in the test area of 0.23km2, using Riegl VZ400 3D Laser Scanner in August 2014. Due to the limit Granted computing performance, the test area is divided into 60 blocks and 13 of them around the shoulder line were selected for filter grid size optimizing. The experiment result shows that the optimal filter grid size varies in diverse sample area, and a power function relation exists between filter grid size and point density. The optimal grid size was determined by above relation and shoulder lines of 60 blocks were then extracted. Comparing with the manual interpretation results, the accuracy of the whole result reaches 85%. This method can be applied to shoulder line extraction in hilly area, which is crucial for point cloud denoising and high accuracy DEM generation.

  4. Use of the ARM Measurements of Spectral Zenith Radiance for Better Understanding of 3D Cloud-Radiation Processes & Aerosol-Cloud Interaction

    SciTech Connect

    Chiu, Jui-Yuan Christine

    2014-04-10

    This project focuses on cloud-radiation processes in a general three-dimensional cloud situation, with particular emphasis on cloud optical depth and effective particle size. The proposal has two main parts. Part one exploits the large number of new wavelengths offered by the Atmospheric Radiation Measurement (ARM) zenith-pointing ShortWave Spectrometer (SWS), to develop better retrievals not only of cloud optical depth but also of cloud particle size. We also take advantage of the SWS’ high sampling resolution to study the “twilight zone” around clouds where strong aerosol-cloud interactions are taking place. Part two involves continuing our cloud optical depth and cloud fraction retrieval research with ARM’s 2-channel narrow vield-of-view radiometer and sunphotometer instrument by, first, analyzing its data from the ARM Mobile Facility deployments, and second, making our algorithms part of ARM’s operational data processing.

  5. Non-linear tearing of 3D null point current sheets

    SciTech Connect

    Wyper, P. F. Pontin, D. I.

    2014-08-15

    The manner in which the rate of magnetic reconnection scales with the Lundquist number in realistic three-dimensional (3D) geometries is still an unsolved problem. It has been demonstrated that in 2D rapid non-linear tearing allows the reconnection rate to become almost independent of the Lundquist number (the “plasmoid instability”). Here, we present the first study of an analogous instability in a fully 3D geometry, defined by a magnetic null point. The 3D null current layer is found to be susceptible to an analogous instability but is marginally more stable than an equivalent 2D Sweet-Parker-like layer. Tearing of the sheet creates a thin boundary layer around the separatrix surface, contained within a flux envelope with a hyperbolic structure that mimics a spine-fan topology. Efficient mixing of flux between the two topological domains occurs as the flux rope structures created during the tearing process evolve within this envelope. This leads to a substantial increase in the rate of reconnection between the two domains.

  6. Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration.

    PubMed

    Yang, Jiaolong; Li, Hongdong; Campbell, Dylan; Jia, Yunde

    2016-11-01

    The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the L2 error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space SE(3). By exploiting the special structure of SE(3) geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.

  7. On concise 3-D simple point characterizations: a marching cubes paradigm.

    PubMed

    Huang, Adam; Liu, Hon-Man; Lee, Chung-Wei; Yang, Chung-Yi; Tsang, Yuk-Ming

    2009-01-01

    The centerlines of tubular structures are useful for medical image visualization and computer-aided diagnosis applications. They can be effectively extracted by using a thinning algorithm that erodes an object layer by layer until only a skeleton is left. An object point is "simple" and can be safely deleted only if the resultant image is topologically equivalent to the original. Numerous characterizations of 3-D simple points based on digital topology already exist. However, little work has been done in the context of marching cubes (MC). This paper reviews several concise 3-D simple point characterizations in a MC paradigm. By using the Euler characteristic and a few newly observed properties in the context of connectivity-consistent MC, we present concise and more self-explanatory proofs. We also present an efficient method for computing the Euler characteristic locally for MC surfaces. Performance evaluations on different implementations are conducted on synthetic data and multidetector computed tomography examination of virtual colonoscopy and angiography.

  8. A successive three-point scheme for fast ray tracing in complex 3D geological models

    NASA Astrophysics Data System (ADS)

    Li, F.; Xu, T.; Zhang, M.; Zhang, Z.

    2013-12-01

    We present a new 3D ray-tracing method that can be applied to computations of traveltime and ray-paths of seismic transmitted, reflected and turning waves in complex geologic models, which consist of arbitrarily shaped blocks whose boundaries are matched by triangulated interfaces for computational efficiency. The new ray-tracing scheme combines the segmentally iterative ray tracing (SIRT) method and the pseudo-bending scheme so as to become a robust and fast ray-tracing method for seismic waves. The new method is extension of our previous constant block models and constant gradient block models to generally heterogeneous block models, and incorporates triangulated interfaces defining boundaries of complex geological bodies, so that it becomes applicable for practical problems. A successive three-point perturbation scheme is formulated that iteratively updates the midpoints of a segment based on an initial ray-path. The corrections of the midpoints are accomplished by first-order analytic formulae according to locations of the midpoint inside the block or on the boundaries of the blocks, to which the updating formulae of the pseudo-bending method and SIRT algorithm are applied instead of the traditional iterative methods. Numerical experiments, including an example in the Bohemian Massif, demonstrate that successive three-point scheme is effective and capable for kinematic ray tracing in complex 3D heterogeneous media.

  9. Unconventional superconductivity at mesoscopic point contacts on the 3D Dirac semimetal Cd3As2

    NASA Astrophysics Data System (ADS)

    Aggarwal, Leena; Gaurav, Abhishek; Thakur, Gohil S.; Haque, Zeba; Ganguli, Ashok K.; Sheet, Goutam

    2016-01-01

    Three-dimensional (3D) Dirac semimetals exist close to topological phase boundaries which, in principle, should make it possible to drive them into exotic new phases, such as topological superconductivity, by breaking certain symmetries. A practical realization of this idea has, however, hitherto been lacking. Here we show that the mesoscopic point contacts between pure silver (Ag) and the 3D Dirac semimetal Cd3As2 (ref. ) exhibit unconventional superconductivity with a critical temperature (onset) greater than 6 K whereas neither Cd3As2 nor Ag are superconductors. A gap amplitude of 6.5 meV is measured spectroscopically in this phase that varies weakly with temperature and survives up to a remarkably high temperature of 13 K, indicating the presence of a robust normal-state pseudogap. The observations indicate the emergence of a new unconventional superconducting phase that exists in a quantum mechanically confined region under a point contact between a Dirac semimetal and a normal metal.

  10. Testing remote sensing on artificial observations: impact of drizzle and 3-D cloud structure on effective radius retrievals

    NASA Astrophysics Data System (ADS)

    Zinner, T.; Wind, G.; Platnick, S.; Ackerman, A. S.

    2010-10-01

    Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation. For three cloud cases the possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES) with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics. We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1) a typical daytime stratocumulus deck at two times in the diurnal cycle and (2) one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three effective radius retrievals is noticed

  11. 3D Printed Molecules and Extended Solid Models for Teaching Symmetry and Point Groups

    ERIC Educational Resources Information Center

    Scalfani, Vincent F.; Vaid, Thomas P.

    2014-01-01

    Tangible models help students and researchers visualize chemical structures in three dimensions (3D). 3D printing offers a unique and straightforward approach to fabricate plastic 3D models of molecules and extended solids. In this article, we prepared a series of digital 3D design files of molecular structures that will be useful for teaching…

  12. 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.

  13. A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

    PubMed

    Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

    2014-03-01

    As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

  14. Commissioning a small-field biological irradiator using point, 2D, and 3D dosimetry techniques

    SciTech Connect

    Newton, Joseph; Oldham, Mark; Thomas, Andrew; Li Yifan; Adamovics, John; Kirsch, David G.; Das, Shiva

    2011-12-15

    Purpose: To commission a small-field biological irradiator, the XRad225Cx from Precision x-Ray, Inc., for research use. The system produces a 225 kVp x-ray beam and is equipped with collimating cones that produce both square and circular radiation fields ranging in size from 1 to 40 mm. This work incorporates point, 2D, and 3D measurements to determine output factors (OF), percent-depth-dose (PDD) and dose profiles at multiple depths. Methods: Three independent dosimetry systems were used: ion-chambers (a farmer chamber and a micro-ionisation chamber), 2D EBT2 radiochromic film, and a novel 3D dosimetry system (DLOS/PRESAGE registered ). Reference point dose rates and output factors were determined from in-air ionization chamber measurements for fields down to {approx}13 mm using the formalism of TG61. PDD, profiles, and output factors at three separate depths (0, 0.5, and 2 cm), were determined for all field sizes from EBT2 film measurements in solid water. Several film PDD curves required a scaling correction, reflecting the challenge of accurate film alignment in very small fields. PDDs, profiles, and output factors were also determined with the 3D DLOS/PRESAGE registered system which generated isotropic 0.2 mm data, in scan times of 20 min. Results: Surface output factors determined by ion-chamber were observed to gradually drop by {approx}9% when the field size was reduced from 40 to 13 mm. More dramatic drops were observed for the smallest fields as determined by EBT{approx}18% and {approx}42% for the 2.5 mm and 1 mm fields, respectively. PRESAGE registered and film output factors agreed well for fields <20 mm (where 3D data were available) with mean deviation of 2.2% (range 1%-4%). PDD values at 2 cm depth varied from {approx}72% for the 40 mm field, down to {approx}55% for the 1 mm field. EBT and PRESAGE registered PDDs agreed within {approx}3% in the typical therapy region (1-4 cm). At deeper depths the EBT curves were slightly steeper (2.5% at 5 cm

  15. Simulated KWAJEX Convective Systems Using a 2D and 3D Cloud Resolving Model and Their Comparisons with Radar Observations

    NASA Technical Reports Server (NTRS)

    Shie, Chung-Lin; Tao, Wei-Kuo; Simpson, Joanne

    2003-01-01

    The 1999 Kwajalein Atoll field experiment (KWAJEX), one of several major TRMM (Tropical Rainfall Measuring Mission) field experiments, has successfully obtained a wealth of information and observation data on tropical convective systems over the western Central Pacific region. In this paper, clouds and convective systems that developed during three active periods (Aug 7-12, Aug 17-21, and Aug 29-Sep 13) around Kwajalein Atoll site are simulated using both 2D and 3D Goddard Cumulus Ensemble (GCE) models. Based on numerical results, the clouds and cloud systems are generally unorganized and short lived. These features are validated by radar observations that support the model results. Both the 2D and 3D simulated rainfall amounts and their stratiform contribution as well as the heat, water vapor, and moist static energy budgets are examined for the three convective episodes. Rainfall amounts are quantitatively similar between the two simulations, but the stratiform contribution is considerably larger in the 2D simulation. Regardless of dimension, fo all three cases, the large-scale forcing and net condensation are the two major physical processes that account for the evolution of the budgets with surface latent heat flux and net radiation solar and long-wave radiation)being secondary processes. Quantitative budget differences between 2D and 3D as well as between various episodes will be detailed.Morover, simulated radar signatures and Q1/Q2 fields from the three simulations are compared to each other and with radar and sounding observations.

  16. Automatic roof plane detection and analysis in airborne lidar point clouds for solar potential assessment.

    PubMed

    Jochem, Andreas; Höfle, Bernhard; Rutzinger, Martin; Pfeifer, Norbert

    2009-01-01

    A relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point cloud into segments describing planar patches. An object-based error assessment is performed to determine the accuracy of the presented classification. It results in 94.4% completeness and 88.4% correctness. Once all roof planes are detected in the 3D point cloud, solar potential analysis is performed for each point. Shadowing effects of nearby objects are taken into account by calculating the horizon of each point within the point cloud. Effects of cloud cover are also considered by using data from a nearby meteorological station. As a result the annual sum of the direct and diffuse radiation for each roof plane is derived. The presented method uses the full 3D information for both feature extraction and solar potential analysis, which offers a number of new applications in fields where natural processes are influenced by the incoming solar radiation (e.g., evapotranspiration, distribution of permafrost). The presented method detected fully automatically a subset of 809 out of 1,071 roof planes where the arithmetic mean of the annual incoming solar radiation is more than 700 kWh/m(2).

  17. Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment

    PubMed Central

    Jochem, Andreas; Höfle, Bernhard; Rutzinger, Martin; Pfeifer, Norbert

    2009-01-01

    A relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point cloud into segments describing planar patches. An object-based error assessment is performed to determine the accuracy of the presented classification. It results in 94.4% completeness and 88.4% correctness. Once all roof planes are detected in the 3D point cloud, solar potential analysis is performed for each point. Shadowing effects of nearby objects are taken into account by calculating the horizon of each point within the point cloud. Effects of cloud cover are also considered by using data from a nearby meteorological station. As a result the annual sum of the direct and diffuse radiation for each roof plane is derived. The presented method uses the full 3D information for both feature extraction and solar potential analysis, which offers a number of new applications in fields where natural processes are influenced by the incoming solar radiation (e.g., evapotranspiration, distribution of permafrost). The presented method detected fully automatically a subset of 809 out of 1,071 roof planes where the arithmetic mean of the annual incoming solar radiation is more than 700 kWh/m2. PMID:22346695

  18. Strategy for long-term 3D cloud-resolving simulations over the ARM SGP site and preliminary results

    NASA Astrophysics Data System (ADS)

    Lin, W.; Liu, Y.; Song, H.; Endo, S.

    2011-12-01

    Parametric representations of cloud/precipitation processes continue having to be adopted in climate simulations with increasingly higher spatial resolution or with emerging adaptive mesh framework; and it is only becoming more critical that such parameterizations have to be scale aware. Continuous cloud measurements at DOE's ARM sites have provided a strong observational basis for novel cloud parameterization research at various scales. Despite significant progress in our observational ability, there are important cloud-scale physical and dynamical quantities that are either not currently observable or insufficiently sampled. To complement the long-term ARM measurements, we have explored an optimal strategy to carry out long-term 3-D cloud-resolving simulations over the ARM SGP site using Weather Research and Forecasting (WRF) model with multi-domain nesting. The factors that are considered to have important influences on the simulated cloud fields include domain size, spatial resolution, model top, forcing data set, model physics and the growth of model errors. The hydrometeor advection that may play a significant role in hydrological process within the observational domain but is often lacking, and the limitations due to the constraint of domain-wide uniform forcing in conventional cloud system-resolving model simulations, are at least partly accounted for in our approach. Conventional and probabilistic verification approaches are employed first for selected cases to optimize the model's capability of faithfully reproducing the observed mean and statistical distributions of cloud-scale quantities. This then forms the basis of our setup for long-term cloud-resolving simulations over the ARM SGP site. The model results will facilitate parameterization research, as well as understanding and dissecting parameterization deficiencies in climate models.

  19. 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.

  20. 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

  1. 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.

  2. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    DOE PAGES

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

  3. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  4. Active testing search for point cloud matching.

    PubMed

    Pinheiro, Miguel Amável; Sznitman, Raphael; Serradell, Eduard; Kybic, Jan; Moreno-Noguer, Francesc; Fua, Pascal

    2013-01-01

    We present a general approach for solving the point-cloud matching problem for the case of mildly nonlinear transformations. Our method quickly finds a coarse approximation of the solution by exploring a reduced set of partial matches using an approach to which we refer to as Active Testing Search (ATS). We apply the method to registration of graph structures by branching point matching. It is based solely on the geometric position of the points, no additional information is used nor the knowledge of an initial alignment. In the second stage, we use dynamic programming to refine the solution. We tested our algorithm on angiography, retinal fundus, and neuronal data gathered using electron and light microscopy. We show that our method solves cases not solved by most approaches, and is faster than the remaining ones.

  5. Point scanning confocal microscopy facilitates 3D human hair follicle imaging in tissue sections.

    PubMed

    Kloepper, Jennifer E; Bíró, Tamás; Paus, Ralf; Cseresnyés, Zoltán

    2010-07-01

    Efficiency is a key factor in determining whether a scientific method becomes widely accepted in practical applications. In dermatology, morphological characterisation of intact hair follicles by traditional methods can be rather inefficient. Samples are embedded, sliced, imaged and digitally reconstructed, which can be time-consuming. Confocal microscopy, on the other hand, is more efficient and readily applicable to study intact hair follicles. Modern confocal microscopes deliver and collect light very efficiently and thus allow high spatial resolution imaging of relatively thick samples. In this letter, we report that we successfully imaged entire intact human hair follicles using point scanning confocal microscopy. Light delivery and light-collection were further improved by preparing the samples in 2,2'-Thiodiethanol (TDE), thus reducing refractive index gradients. The relatively short total scan times and the high quality of the acquired 3D images make confocal microscopy a desirable method for studying intact hair follicles under normal and pathological conditions.

  6. Roof Modelling Potential of Unmanned Air Vehicle Point Clouds with Respect to Terrestrial Laser Scanning

    NASA Astrophysics Data System (ADS)

    Karakis, Serkan; Gunes Sefercik, Umut; Atalay, Can

    2016-07-01

    In parallel with the improvement of laser scanning technologies, dense point clouds which provide the detailed description of terrain and non-terrain objects became indispensable for remotely-sensed data users. Owing to the large demand, besides laser scanning, point clouds were started to achieve using photogrammetric images. Unmanned air vehicle (UAV) images are one of the most preferred data for creating dense point clouds by the advantage of low cost, rapid and periodically gain. In this study, we tried to assess the roof modelling potential of UAV point clouds by comparing three dimensional (3D) roof models produced from UAV and terrestrial laser scanning (TLS) point clouds. In the study, very popular low cost action camera SJ4000 and Faro Laser Scanner Focus3D X 330 were used to provide point clouds and the roof of Bulent Ecevit University Civil Aviation Academy building was utilized. For the validation of horizontal and vertical geolocation accuracies, standard deviation was used as the main indicator. The visual results demonstrated that UAV roof model is almost coherent with TLS roof model after the filtering-based refinement on noisy pixels and systematic bias correction. Moreover, the horizontal geolocation accuracy is approx. |5cm| both in X and Y directions and bias corrected vertical geolocation accuracy is approx. 17cm for zero roof slope.

  7. Point Cloud Derived Fromvideo Frames: Accuracy Assessment in Relation to Terrestrial Laser Scanningand Digital Camera Data

    NASA Astrophysics Data System (ADS)

    Delis, P.; Zacharek, M.; Wierzbicki, D.; Grochala, A.

    2017-02-01

    The use of image sequences in the form of video frames recorded on data storage is very useful in especially when working with large and complex structures. Two cameras were used in this study: Sony NEX-5N (for the test object) and Sony NEX-VG10 E (for the historic building). In both cases, a Sony α f = 16 mm fixed focus wide-angle lens was used. Single frames with sufficient overlap were selected from the video sequence using an equation for automatic frame selection. In order to improve the quality of the generated point clouds, each video frame underwent histogram equalization and image sharpening. Point clouds were generated from the video frames using the SGM-like image matching algorithm. The accuracy assessment was based on two reference point clouds: the first from terrestrial laser scanning and the second generated based on images acquired using a high resolution camera, the NIKON D800. The performed research has shown, that highest accuracies are obtained for point clouds generated from video frames, for which a high pass filtration and histogram equalization had been performed. Studies have shown that to obtain a point cloud density comparable to TLS, an overlap between subsequent video frames must be 85 % or more. Based on the point cloud generated from video data, a parametric 3D model can be generated. This type of the 3D model can be used in HBIM construction.

  8. 3D Modeling of interactions between Jupiter’s ammonia clouds and large anticyclones

    NASA Astrophysics Data System (ADS)

    Palotai, Csaba; Dowling, Timothy E.; Fletcher, Leigh N.

    2014-04-01

    The motions of Jupiter’s tropospheric jets and vortices are made visible by its outermost clouds, which are expected to be largely composed of ammonia ice. Several groups have demonstrated that much of this dynamics can be reproduced in the vorticity fields of high-resolution models that, surprisingly, do not contain any clouds. While this reductionist approach is valuable, it has natural limitations. Here we report on numerical simulations that use the EPIC Jupiter model with a realistic ammonia-cloud microphysics module, focusing on how observable ammonia clouds interact with the Great Red Spot (GRS) and Oval BA. Maps of column-integrated ammonia-cloud density in the model resemble visible-band images of Jupiter and potential-vorticity maps. On the other hand, vertical cross sections through the model vortices reveal considerable heterogeneity in cloud density values between pressure levels in the vicinity of large anticyclones, and interestingly, ammonia snow appears occasionally. Away from the vortices, the ammonia clouds form at the levels expected from traditional one-dimensional models, and inside the vortices, the clouds are elevated and thick, in agreement with Galileo NIMS observations. However, rather than gathering slowly into place as a result of Jupiter’s weak secondary circulation, the ammonia clouds instead form high and thick inside the large anticyclones as soon as the cloud microphysics module is enabled. This suggests that any weak secondary circulation that might be present in Jupiter’s anticyclones, such as may arise because of radiative damping of their temperature anomalies, may have little or no direct effect on the altitude or thickness of the ammonia clouds. Instead, clouds form at those locations because the top halves of large anticyclones must be cool for the vortex to be able to fit under the tropopause, which is a primary-circulation, thermal-wind-shear effect of the stratification, not a secondary-circulation thermal feature

  9. Integration of Libration Point Orbit Dynamics into a Universal 3-D Autonomous Formation Flying Algorithm

    NASA Technical Reports Server (NTRS)

    Folta, David; Bauer, Frank H. (Technical Monitor)

    2001-01-01

    The autonomous formation flying control algorithm developed by the Goddard Space Flight Center (GSFC) for the New Millennium Program (NMP) Earth Observing-1 (EO-1) mission is investigated for applicability to libration point orbit formations. In the EO-1 formation-flying algorithm, control is accomplished via linearization about a reference transfer orbit with a state transition matrix (STM) computed from state inputs. The effect of libration point orbit dynamics on this algorithm architecture is explored via computation of STMs using the flight proven code, a monodromy matrix developed from a N-body model of a libration orbit, and a standard STM developed from the gravitational and coriolis effects as measured at the libration point. A comparison of formation flying Delta-Vs calculated from these methods is made to a standard linear quadratic regulator (LQR) method. The universal 3-D approach is optimal in the sense that it can be accommodated as an open-loop or closed-loop control using only state information.

  10. 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.

  11. Tropical Oceanic Precipitation Processes Over Warm Pool: 2D and 3D Cloud Resolving Model Simulations

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.; Johnson, D.; Simpson, J.; Einaudi, Franco (Technical Monitor)

    2001-01-01

    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 topics. 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 model simulate 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 clouds systems. The major objective of this paper is to investigate the latent heating, moisture and momentum 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 (GCE) model which includes a 3-class ice-phase microphysics scheme.

  12. Airborne Lidar Point Cloud Density Indices

    NASA Astrophysics Data System (ADS)

    Shih, P. T.; Huang, C.-M.

    2006-12-01

    Airborne lidar is useful for collecting a large volume and high density of points with three dimensional coordinates. Among these points are terrain points, as well as those points located aboveground. For DEM production, the density of the terrain points is an important quality index. While the penetration rate of laser points is dependent on the surface type characteristics, there are also different ways to present the point density. Namely, the point density could be measured by subdividing the surveyed area into cells, then computing the ratio of the number of points in each respective cell to its area. In this case, there will be one density value for each cell. The other method is to construct the TIN, and count the number of triangles in the cell, divided by the area of the cell. Aside from counting the number of triangles, the area of the largest, or the 95% ranking, triangle, could be used as an index as well. The TIN could also be replaced by Voronoi diagrams (Thiessen Polygon), and a polygon with even density could be derived from human interpretation. The nature of these indices is discussed later in this research paper. Examples of different land cover types: bare earth, built-up, low vegetation, low density forest, and high density forest; are extracted from point clouds collected in 2005 by ITRI under a contract from the Ministry of the Interior. It is found that all these indices are capable of reflecting the differences of the land cover type. However, further investigation is necessary to determine which the most descriptive one is.

  13. Probabilistic Reconstruction of Orthodox Churches from Precision Point Clouds Using Bayesian Networks and Cellular Automata

    NASA Astrophysics Data System (ADS)

    Chizhova, M.; Korovin, D.; Gurianov, A.; Brodovskii, M.; Brunn, A.; Stilla, U.; Luhmann, T.

    2017-02-01

    The point cloud interpretation and reconstruction of 3d-buildings from point clouds has already been treated for a few decades. There are many articles which consider the different methods and workows of the automatic detection and reconstruction of geometrical objects from point clouds. Each method is suitable for the special geometry type of object or sensor. General approaches are rare. In our work we present an algorithm which develops the optimal process sequence of the automatic search, detection and reconstruction of buildings and building components from a point cloud. It can be used for the detection of the set of geometric objects to be reconstructed, independent of its destruction. In a simulated example we reconstruct a complete Russian-orthodox church starting from the set of detected structural components and reconstruct missing components with high probability.

  14. Segmentation and Reconstruction of Buildings with Aerial Oblique Photography Point Clouds

    NASA Astrophysics Data System (ADS)

    Liu, P.; Li, Y. C.; Hu, W.; Ding, X. B.

    2015-06-01

    Oblique photography technology as an excellent method for 3-D city model construction has brought itself to large-scale recognition and undeniable high social status. Tilt and vertical images with the high overlaps and different visual angles can produce a large number of dense matching point clouds data with spectral information. This paper presents a method of buildings reconstruction with stereo matching dense point clouds from aerial oblique images, which includes segmentation of buildings and reconstruction of building roofs. We summarize the characteristics of stereo matching point clouds from aerial oblique images and outline the problems with existing methods. Then we present the method for segmentation of building roofs, which based on colors and geometrical derivatives such as normal and curvature. Finally, a building reconstruction approach is developed based on the geometrical relationship. The experiment and analysis show that the methods are effective on building reconstruction with stereo matching point clouds from aerial oblique images.

  15. From Point Cloud to Bim: a Survey of Existing Approaches

    NASA Astrophysics Data System (ADS)

    Hichri, N.; Stefani, C.; De Luca, L.; Veron, P.; Hamon, G.

    2013-07-01

    In order to handle more efficiently projects of restoration, documentation and maintenance of historical buildings, it is essential to rely on a 3D enriched model for the building. Today, the concept of Building Information Modelling (BIM) is widely adopted for the semantization of digital mockups and few research focused on the value of this concept in the field of cultural heritage. In addition historical buildings are already built, so it is necessary to develop a performing approach, based on a first step of building survey, to develop a semantically enriched digital model. For these reasons, this paper focuses on this chain starting with a point cloud and leading to the well-structured final BIM; and proposes an analysis and a survey of existing approaches on the topics of: acquisition, segmentation and BIM creation. It also, presents a critical analysis on the application of this chain in the field of cultural heritage.

  16. Automatic fusion of photogrammetric imagery and laser scanner point clouds

    NASA Astrophysics Data System (ADS)

    Forkuo, Eric Kwabena

    Close-range photogrammetry and the relatively new technology of terrestrial laser scanning can be considered as complementary rather than competitive technologies. For instance, terrestrial laser scanners (TLS) have the ability to rapidly collect high-resolution 3D surface information about an object. The same type of data can be generated using close-range photogrammetric (CRP) techniques, but image disparities common to close-range scenes makes this an operator intensive task. The imaging systems of some TLSs do not have very high radiometric resolution whereas high-resolution digital cameras used in modern CRP do. Finally, TLSs are essentially earth-bound whereas cameras can be moved at will around the object being imaged. This thesis, therefore, explores and attempts to provide a solution to the problems of developing a methodology to fuse terrestrial laser scanner generated 3D data and high-resolution digital images. Four phases of the methodology have been investigated: data pre-processing (fusion of data from the two sensors), automatic measurements (feature detection and correspondence matching), mapping (creation of point cloud visual index), and orientation (calculation of exterior orientation parameters). Individual phases were initially investigated in a manually controlled environment, typically using commercial photogrammetric software, and then combined in a completely automated system. Focusing on the amount of geometric primitives, three different scenes (data set A, data set B, and data set C) representing three levels of complexity (low, medium and high) were scanned with the laser scanner, and for each scan, a 2D photographic image was taken with a digital camera. To overcome the differences in datasets, a hybrid matching (both feature and area-based) algorithm was successfully developed and implemented. The fidelity of the concept of generating synthetic camera images has been tested by determining the exterior orientation of the synthetic

  17. Estimability of thrusting trajectories in 3-D from a single passive sensor with unknown launch point

    NASA Astrophysics Data System (ADS)

    Yuan, Ting; Bar-Shalom, Yaakov; Willett, Peter; Ben-Dov, R.; Pollak, S.

    2013-09-01

    The problem of estimating the state of thrusting/ballistic endoatmospheric projectiles moving in 3-dimensional (3-D) space using 2-dimensional (2-D) measurements from a single passive sensor is investigated. The location of projectile's launch point (LP) is unavailable and this could significantly affect the performance of the estimation and the IPP. The LP altitude is then an unknown target parameter. The estimability is analyzed based on the Fisher Information Matrix (FIM) of the target parameter vector, comprising the initial launch (azimuth and elevation) angles, drag coefficient, thrust and the LP altitude, which determine the trajectory according to a nonlinear motion equation. The full rank of the FIM ensures that one has an estimable target parameters. The corresponding Craḿer-Rao lower bound (CRLB) quantifies the estimation performance of the estimator that is statistically efficient and can be used for IPP. In view of the inherent nonlinearity of the problem, the maximum likelihood (ML) estimate of the target parameter vector is found by using a mixed (partially grid-based) search approach. For a selected grid in the drag-coefficient-thrust-altitude subspace, the proposed parallelizable approach is shown to have reliable estimation performance and further leads to the final IPP of high accuracy.

  18. Walsh-Hadamard Based 3D Steganography for Protecting Sensitive Information in Point-of-Care.

    PubMed

    Abuadbba, Alsharif; Khalil, Ibrahim

    2016-11-29

    Remote points-of-care has recently had a lot of attention for their advantages such as saving lives and cost reduction. The transmitted streams usually contain (1) normal biomedical signals (e.g. ECG) and (2) highly private information (e.g. patient identity). Despite the obvious advantages, the primary concerns are privacy and authenticity of the transferred data. Therefore, this paper introduces a novel steganographic mechanism that ensures (1) strong privacy preservation of private information by random concealing inside the transferred signals employing a key, and (2) evidence of originality for the biomedical signals. To maximize hiding, Fast Walsh-Hadamard Transform is utilized to transform the signals into a group of coefficients. To ensure the lowest distortion, only less-significant values of coefficients are employed. To strengthen security, the key is utilized in a 3-Dimensional random coefficients' reform to produce a 3D order employed in the concealing process. The resultant distortion has been thoroughly measured in all stages. After extensive experiments on three types of signals, it has been proven that the algorithm has little impact on the genuine signals (< 1 %). The security evaluation also confirms that unlawful retrieval of the hidden information within rational time is mightily improbable.

  19. LIVAS: a 3-D multi-wavelength aerosol/cloud climatology based on CALIPSO and EARLINET

    NASA Astrophysics Data System (ADS)

    Amiridis, V.; Marinou, E.; Tsekeri, A.; Wandinger, U.; Schwarz, A.; Giannakaki, E.; Mamouri, R.; Kokkalis, P.; Binietoglou, I.; Solomos, S.; Herekakis, T.; Kazadzis, S.; Gerasopoulos, E.; Balis, D.; Papayannis, A.; Kontoes, C.; Kourtidis, K.; Papagiannopoulos, N.; Mona, L.; Pappalardo, G.; Le Rille, O.; Ansmann, A.

    2015-01-01

    We present LIVAS, a 3-dimentional multi-wavelength global aerosol and cloud optical climatology, optimized to be used for future space-based lidar end-to-end simulations of realistic atmospheric scenarios as well as retrieval algorithm testing activities. LIVAS database provides averaged profiles of aerosol optical properties for the potential space-borne laser operating wavelengths of 355, 532, 1064, 1570 and 2050 nm and of cloud optical properties at the wavelength of 532 nm. The global climatology is based on CALIPSO observations at 532 and 1064 nm and on aerosol-type-dependent spectral conversion factors for backscatter and extinction, derived from EARLINET ground-based measurements for the UV and scattering calculations for the IR wavelengths, using a combination of input data from AERONET, suitable aerosol models and recent literature. The required spectral conversion factors are calculated for each of the CALIPSO aerosol types and are applied to CALIPSO extinction and backscatter data correspondingly to the aerosol type retrieved by the CALIPSO aerosol classification scheme. A cloud climatology based on CALIPSO measurements at 532 nm is also provided, neglecting wavelength conversion due to approximately neutral scattering behavior of clouds along the spectral range of LIVAS. Averages of particle linear depolarization ratio profiles at 532 nm are provided as well. Finally, vertical distributions for a set of selected scenes of specific atmospheric phenomena (e.g., dust outbreaks, volcanic eruptions, wild fires, polar stratospheric clouds) are analyzed and spectrally converted so as to be used as case studies for space-borne lidar performance assessments. The final global climatology includes 4-year (1 January 2008-31 December 2011) time-averaged CALIPSO data on a uniform grid of 1×1 degree with the original high vertical resolution of CALIPSO in order to ensure realistic simulations of the atmospheric variability in lidar end-to-end simulations.

  20. Status report on the 'Merging' of the Electron-Cloud Code POSINST with the 3-D Accelerator PIC CODE WARP

    SciTech Connect

    Vay, J.-L.; Furman, M.A.; Azevedo, A.W.; Cohen, R.H.; Friedman, A.; Grote, D.P.; Stoltz, P.H.

    2004-04-19

    We have integrated the electron-cloud code POSINST [1] with WARP [2]--a 3-D parallel Particle-In-Cell accelerator code developed for Heavy Ion Inertial Fusion--so that the two can interoperate. Both codes are run in the same process, communicate through a Python interpreter (already used in WARP), and share certain key arrays (so far, particle positions and velocities). Currently, POSINST provides primary and secondary sources of electrons, beam bunch kicks, a particle mover, and diagnostics. WARP provides the field solvers and diagnostics. Secondary emission routines are provided by the Tech-X package CMEE.

  1. 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

  2. 3D Moving-Mesh Simulations of Galactic Center Cloud G2

    NASA Astrophysics Data System (ADS)

    Wilson, Julia; Fragile, P. C.; Anninos, P.; Murray, S. D.

    2013-01-01

    Using three-dimensional, moving-mesh simulations, we investigate the future evolution of the recently discovered gas cloud G2 traveling through the galactic center. We consider the case of a spherical cloud initially in pressure equilibrium with the background. Our suite of simulations explores the following parameters: the equation of state, radial profiles of the background gas, and start times for the evolution. Our primary focus is on how the fate of this cloud will affect the future activity of Sgr A*. From our simulations we expect an average feeding rate in the range of 5 - 19 × 10-8M⊙ yr-1 beginning in 2013 and lasting for at least 7 years (our simulations stop in year 2020). The accretion varies by less than a factor of three on timescales ≤ 1 month, and shows no more than a factor of 10 difference between the maximum and minimum observed rates within any given model. These rates are comparable to the current estimated accretion rate in the immediate vicinity of Sgr A*, although they represent only a small (≤ 5%) increase over the current expected feeding rate at the effective inner boundary of our simulations (r = 750RS ≈ 1015 cm), where RS is the Schwarzschild radius of the black hole. Therefore, the break up of cloud G2 may have only a minimal effect on the brightness and variability of Sgr A* over the next decade. This is because current models of the galactic center predict that most of the gas will be caught up in outflows. However, if the accreted G2 material can remain cold, it may not mix well with the hot, diffuse background gas, and instead accrete efficiently onto Sgr A*. Further observations of G2 will give us an unprecedented opportunity to test this idea. The break up of the cloud itself may also be observable. By tracking the amount of cloud energy that is dissipated during our simulations, we are able to get a rough estimate of the luminosity associated with its tidal disruption; we find values of a few 1036 erg s-1.

  3. Comparison of ZEB1 and Leica C10 Indoor Laser Scanning Point Clouds

    NASA Astrophysics Data System (ADS)

    Sirmacek, Beril; Shen, Yueqian; Lindenbergh, Roderik; Zlatanova, Sisi; Diakite, Abdoulaye

    2016-06-01

    We present a comparison of point cloud generation and quality of data acquired by Zebedee (Zeb1) and Leica C10 devices which are used in the same building interior. Both sensor devices come with different practical and technical advantages. As it could be expected, these advantages come with some drawbacks. Therefore, depending on the requirements of the project, it is important to have a vision about what to expect from different sensors. In this paper, we provide a detailed analysis of the point clouds of the same room interior acquired from Zeb1 and Leica C10 sensors. First, it is visually assessed how different features appear in both the Zeb1 and Leica C10 point clouds. Next, a quantitative analysis is given by comparing local point density, local noise level and stability of local normals. Finally, a simple 3D room plan is extracted from both the Zeb1 and the Leica C10 point clouds and the lengths of constructed line segments connecting corners of the room are compared. The results show that Zeb1 is far superior in ease of data acquisition. No heavy handling, hardly no measurement planning and no point cloud registration is required from the operator. The resulting point cloud has a quality in the order of centimeters, which is fine for generating a 3D interior model of a building. Our results also clearly show that fine details of for example ornaments are invisible in the Zeb1 data. If point clouds with a quality in the order of millimeters are required, still a high-end laser scanner like the Leica C10 is required, in combination with a more sophisticated, time-consuming and elaborative data acquisition and processing approach.

  4. 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.

  5. The 3D Radiative Effects of Clouds in Aerosol Retrieval: Can we Remove Them?

    SciTech Connect

    Kassianov, Evgueni I.; Ovchinnikov, Mikhail; Berg, Larry K.; McFarlane, Sally A.; Flynn, Connor J.; Ferrare, Richard; Hostetler, Chris A.

    2009-09-30

    We outline a new method, called the ratio method, developed to retrieve aerosol optical depth (AOD) under broken cloud conditions and present validation results from sensitivity and case studies. Results of the sensitivity study demonstrate that the ratio method, which exploits ratios of reflectances in the visible spectral range, has the potential for accurate AOD retrievals under different observational conditions and random errors in input data. Also, we examine the performance of the ratio method using aircraft data collected during the Cloud and Land Surface Interaction Campaign (CLASIC) and the Cumulus Humilis Aerosol Processing Study (CHAPS). Results of the case study suggest that the ratio method has the ability to retrieve AOD from multi-spectral aircraft observations of the reflected solar radiation.

  6. Integration of Point Clouds Originated from Laser Scaner and Photogrammetric Images for Visualization of Complex Details of Historical Buildings

    NASA Astrophysics Data System (ADS)

    Altuntas, C.

    2015-02-01

    Three-dimensional (3D) models of historical buildings are created for documentation and virtual realization of them. Laser scanning and photogrammetry are extensively used to perform for these aims. The selection of the method that will be used in threedimensional modelling study depends on the scale and shape of the object, and also applicability of the method. Laser scanners are high cost instruments. However, the cameras are low cost instruments. The off-the-shelf cameras are used for taking the photogrammetric images. The camera is imaging the object details by carrying on hand while the laser scanner makes ground based measurement. Laser scanner collect high density spatial data in a short time from the measurement area. On the other hand, image based 3D (IB3D) measurement uses images to create 3D point cloud data. The image matching and the creation of the point cloud can be done automatically. Historical buildings include more complex details. Thus, all details cannot be measured by terrestrial laser scanner (TLS) due to the blocking the details with each others. Especially, the artefacts which have complex shapes cannot be measured in full details. They cause occlusion on the point cloud model. However it is possible to record photogrammetric images and creation IB3D point cloud for these areas. Thus the occlusion free 3D model is created by the integration of point clouds originated from the TLS and photogrammetric images. In this study, usability of laser scanning in conjunction with image based modelling for creation occlusion free three-dimensional point cloud model of historical building was evaluated. The IB3D point cloud was created in the areas that could not been measured by TLS. Then laser scanning and IB3D point clouds were integrated in the common coordinate system. The registration point clouds were performed with the iterative closest point (ICP) and georeferencing methods. Accuracy of the registration was evaluated by convergency and its

  7. Normal and Feature Approximations from Noisy Point Clouds

    DTIC Science & Technology

    2005-02-01

    Normal and Feature Approximations from Noisy Point Clouds Tamal K. Dey Jian Sun Abstract We consider the problem of approximating normal and...normal and, in partic- ular, feature size approximations for noisy point clouds . In the noise-free case the choice of the Delaunay balls is not an issue...axis from noisy point clouds ex- ists [7]. This algorithm approximates the medial axis with Voronoi faces under a stringent uniform sampling

  8. Distributed Dimensonality-Based Rendering of LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Brédif, M.; Vallet, B.; Ferrand, B.

    2015-08-01

    Mobile Mapping Systems (MMS) are now commonly acquiring lidar scans of urban environments for an increasing number of applications such as 3D reconstruction and mapping, urban planning, urban furniture monitoring, practicability assessment for persons with reduced mobility (PRM)... MMS acquisitions are usually huge enough to incur a usability bottleneck for the increasing number of non-expert user that are not trained to process and visualize these huge datasets through specific softwares. A vast majority of their current need is for a simple 2D visualization that is both legible on screen and printable on a static 2D medium, while still conveying the understanding of the 3D scene and minimizing the disturbance of the lidar acquisition geometry (such as lidar shadows). The users that motivated this research are, by law, bound to precisely georeference underground networks for which they currently have schematics with no or poor absolute georeferencing. A solution that may fit their needs is thus a 2D visualization of the MMS dataset that they could easily interpret and on which they could accurately match features with their user datasets they would like to georeference. Our main contribution is two-fold. First, we propose a 3D point cloud stylization for 2D static visualization that leverages a Principal Component Analysis (PCA)-like local geometry analysis. By skipping the usual and error-prone estimation of a ground elevation, this rendering is thus robust to non-flat areas and has no hard-to-tune parameters such as height thresholds. Second, we implemented the corresponding rendering pipeline so that it can scale up to arbitrary large datasets by leveraging the Spark framework and its Resilient Distributed Dataset (RDD) and Dataframe abstractions.

  9. SU-E-T-490: Independent Three-Dimensional (3D) Dose Verification of VMAT/SBRT Using EPID and Cloud Computing

    SciTech Connect

    Ding, A; Han, B; Bush, K; Wang, L; Xing, L

    2015-06-15

    Purpose: Dosimetric verification of VMAT/SBRT is currently performed on one or two planes in a phantom with either film or array detectors. A robust and easy-to-use 3D dosimetric tool has been sought since the advent of conformal radiation therapy. Here we present such a strategy for independent 3D VMAT/SBRT plan verification system by a combined use of EPID and cloud-based Monte Carlo (MC) dose calculation. Methods: The 3D dosimetric verification proceeds in two steps. First, the plan was delivered with a high resolution portable EPID mounted on the gantry, and the EPID-captured gantry-angle-resolved VMAT/SBRT field images were converted into fluence by using the EPID pixel response function derived from MC simulations. The fluence was resampled and used as the input for an in-house developed Amazon cloud-based MC software to reconstruct the 3D dose distribution. The accuracy of the developed 3D dosimetric tool was assessed using a Delta4 phantom with various field sizes (square, circular, rectangular, and irregular MLC fields) and different patient cases. The method was applied to validate VMAT/SBRT plans using WFF and FFF photon beams (Varian TrueBeam STX). Results: It was found that the proposed method yielded results consistent with the Delta4 measurements. For points on the two detector planes, a good agreement within 1.5% were found for all the testing fields. Patient VMAT/SBRT plan studies revealed similar level of accuracy: an average γ-index passing rate of 99.2± 0.6% (3mm/3%), 97.4± 2.4% (2mm/2%), and 72.6± 8.4 % ( 1mm/1%). Conclusion: A valuable 3D dosimetric verification strategy has been developed for VMAT/SBRT plan validation. The technique provides a viable solution for a number of intractable dosimetry problems, such as small fields and plans with high dose gradient.

  10. The algorithm to generate color point-cloud with the registration between panoramic image and laser point-cloud

    NASA Astrophysics Data System (ADS)

    Zeng, Fanyang; Zhong, Ruofei

    2014-03-01

    Laser point cloud contains only intensity information and it is necessary for visual interpretation to obtain color information from other sensor. Cameras can provide texture, color, and other information of the corresponding object. Points with color information of corresponding pixels in digital images can be used to generate color point-cloud and is conducive to the visualization, classification and modeling of point-cloud. Different types of digital cameras are used in different Mobile Measurement Systems (MMS).the principles and processes for generating color point-cloud in different systems are not the same. The most prominent feature of the panoramic images is the field of 360 degrees view angle in the horizontal direction, to obtain the image information around the camera as much as possible. In this paper, we introduce a method to generate color point-cloud with panoramic image and laser point-cloud, and deduce the equation of the correspondence between points in panoramic images and laser point-clouds. The fusion of panoramic image and laser point-cloud is according to the collinear principle of three points (the center of the omnidirectional multi-camera system, the image point on the sphere, the object point). The experimental results show that the proposed algorithm and formulae in this paper are correct.

  11. Fusion Render Cloud System for 3D Contents Using a Super Computer

    NASA Astrophysics Data System (ADS)

    Choi, E.-Jung; Kim, Seoksoo

    This study develops a SOHO RenderFarm system suitable for a lab environment through data collection and professional education, implements a user environment which is the same as a super computer, analyzes rendering problems that may arise from use of a super computer and then designs a FRC(Fusion Render Cloud) system. Also, clients can access the SOHO RenderFarm system through networks, and the FRC system completed in a test environment can be interlinked with external networks of a super computer.

  12. Point-cloud-to-point-cloud technique on tool calibration for dental implant surgical path tracking

    NASA Astrophysics Data System (ADS)

    Lorsakul, Auranuch; Suthakorn, Jackrit; Sinthanayothin, Chanjira

    2008-03-01

    Dental implant is one of the most popular methods of tooth root replacement used in prosthetic dentistry. Computerize navigation system on a pre-surgical plan is offered to minimize potential risk of damage to critical anatomic structures of patients. Dental tool tip calibrating is basically an important procedure of intraoperative surgery to determine the relation between the hand-piece tool tip and hand-piece's markers. With the transferring coordinates from preoperative CT data to reality, this parameter is a part of components in typical registration problem. It is a part of navigation system which will be developed for further integration. A high accuracy is required, and this relation is arranged by point-cloud-to-point-cloud rigid transformations and singular value decomposition (SVD) for minimizing rigid registration errors. In earlier studies, commercial surgical navigation systems from, such as, BrainLAB and Materialize, have flexibility problem on tool tip calibration. Their systems either require a special tool tip calibration device or are unable to change the different tool. The proposed procedure is to use the pointing device or hand-piece to touch on the pivot and the transformation matrix. This matrix is calculated every time when it moves to the new position while the tool tip stays at the same point. The experiment acquired on the information of tracking device, image acquisition and image processing algorithms. The key success is that point-to-point-cloud requires only 3 post images of tool to be able to converge to the minimum errors 0.77%, and the obtained result is correct in using the tool holder to track the path simulation line displayed in graphic animation.

  13. Cloud GIS and 3d Modelling to Enhance Sardinian Late Gothic Architectural Heritage

    NASA Astrophysics Data System (ADS)

    Pisu, C.; Casu, P.

    2013-07-01

    This work proposes the documentation, virtual reconstruction and spreading of architectural heritage through the use of software packages that operate in cloud computing. Cloud computing makes available a variety of applications and tools which can be effective both for the preparation and for the publication of different kinds of data. We tested the versatil ity and ease of use of such documentation tools in order to study a particular architectural phenomenon. The ultimate aim is to develop a multi-scale and multi-layer information system, oriented to the divulgation of Sardinian late gothic architecture. We tested the applications on portals of late Gothic architecture in Sardinia. The actions of conservation, protection and enhancement of cultural heritage are all founded on the social function that can be reached only through the widest possible fruition by the community. The applications of digital technologies on cultural heritage can contribute to the construction of effective communication models that, relying on sensory and emotional involvement of the viewer, can attract a wider audience to cultural content.

  14. Finding Organized Structures in 3-D LADAR Data

    DTIC Science & Technology

    2004-12-01

    work exists also on how to extract planar and linear objects from scattered 3-D point clouds , see for example [5], [6]. Methods were even proposed to...of structure detection and segmentation from 3-D point clouds collected from a single sensor location or integrated from multiple locations. In [2...primitives to point clouds are difficult to use practically for large data sets containing multiple complex structures, in opposition to multiple planar

  15. The 3-D Tropical Convective Cloud Spectrum in AMIE Radar Observations and Global Climate Simulations

    SciTech Connect

    Schumacher, Courtney

    2015-08-31

    During the three years of this grant performance, the PI and her research group have made a number of significant contributions towards determining properties of tropical deep convective clouds and how models depict and respond to the heating associated with tropical convective systems. The PI has also been an active ARM/ASR science team member, including playing a significant role in AMIE and GoAmazon2014/5. She served on the DOE ASR radar science steering committee and was a joint chair of the Mesoscale Convective Organization group under the Cloud Life Cycle working group. This grant has funded a number of graduate students, many of them women, and the PI and her group have presented their DOE-supported work at various universities and national meetings. The PI and her group participated in the AMIE (2011-12) and GoAmazon2014/5 (2014-15) DOE field deployments that occurred in the tropical Indian Ocean and Brazilian Amazon, respectively. AMIE observational results (DePasquale et al. 2014, Feng et al. 2014, Ahmed and Schumacher 2015) focus on the variation and possible importance of Kelvin waves in various phases of the Madden-Julian Oscillation (MJO), on the synergy of the different wavelength radars deployed on Addu Atoll, and on the importance of humidity thresholds in the tropics on stratiform rain production. Much of the PIs GoAmazon2014/5 results to date relate to overviews of the observations made during the field campaign (Martin et al. 2015, 2016; Fuentes et al. 2016), but also include the introduction of the descending arm and its link to ozone transport from the mid-troposphere to the surface (Gerken et al. 2016). Vertical motion and mass flux profiles from GoAmazon (Giangrande et al. 2016) also show interesting patterns between seasons and provide targets for model simulations. Results from TWP-ICE (Schumacher et al. 2015), which took place in Darwin, Australia in 2006 show that vertical velocity retrievals from the profilers provide structure to

  16. Joint angle variability in 3D bimanual pointing: uncontrolled manifold analysis.

    PubMed

    Domkin, Dmitry; Laczko, Jozsef; Djupsjöbacka, Mats; Jaric, Slobodan; Latash, Mark L

    2005-05-01

    The structure of joint angle variability and its changes with practice were investigated using the uncontrolled manifold (UCM) computational approach. Subjects performed fast and accurate bimanual pointing movements in 3D space, trying to match the tip of a pointer, held in the right hand, with the tip of one of three different targets, held in the left hand during a pre-test, several practice sessions and a post-test. The prediction of the UCM approach about the structuring of joint angle variance for selective stabilization of important task variables was tested with respect to selective stabilization of time series of the vectorial distance between the pointer and aimed target tips (bimanual control hypothesis) and with respect to selective stabilization of the endpoint trajectory of each arm (unimanual control hypothesis). The components of the total joint angle variance not affecting (V(COMP)) and affecting (V(UN)) the value of a selected task variable were computed for each 10% of the normalized movement time. The ratio of these two components R(V)=V(COMP)/V(UN) served as a quantitative index of selective stabilization. Both the bimanual and unimanual control hypotheses were supported, however the R(V) values for the bimanual hypothesis were significantly higher than those for the unimanual hypothesis applied to the left and right arm both prior to and after practice. This suggests that the CNS stabilizes the relative trajectory of one endpoint with respect to the other more than it stabilizes the trajectories of each of the endpoints in the external space. Practice-associated improvement in both movement speed and accuracy was accompanied by counter-intuitive lack of changes in R(V). Both V(COMP) and V(UN) variance components decreased such that their ratio remained constant prior to and after practice. We conclude that the UCM approach offers a unique and under-explored opportunity to track changes in the organization of multi-effector systems with practice

  17. Automated 3D motion tracking using Gabor filter bank, robust point matching, and deformable models.

    PubMed

    Chen, Ting; Wang, Xiaoxu; Chung, Sohae; Metaxas, Dimitris; Axel, Leon

    2010-01-01

    Tagged magnetic resonance imaging (tagged MRI or tMRI) provides a means of directly and noninvasively displaying the internal motion of the myocardium. Reconstruction of the motion field is needed to quantify important clinical information, e.g., the myocardial strain, and detect regional heart functional loss. In this paper, we present a three-step method for this task. First, we use a Gabor filter bank to detect and locate tag intersections in the image frames, based on local phase analysis. Next, we use an improved version of the robust point matching (RPM) method to sparsely track the motion of the myocardium, by establishing a transformation function and a one-to-one correspondence between grid tag intersections in different image frames. In particular, the RPM helps to minimize the impact on the motion tracking result of 1) through-plane motion and 2) relatively large deformation and/or relatively small tag spacing. In the final step, a meshless deformable model is initialized using the transformation function computed by RPM. The model refines the motion tracking and generates a dense displacement map, by deforming under the influence of image information, and is constrained by the displacement magnitude to retain its geometric structure. The 2D displacement maps in short and long axis image planes can be combined to drive a 3D deformable model, using the moving least square method, constrained by the minimization of the residual error at tag intersections. The method has been tested on a numerical phantom, as well as on in vivo heart data from normal volunteers and heart disease patients. The experimental results show that the new method has a good performance on both synthetic and real data. Furthermore, the method has been used in an initial clinical study to assess the differences in myocardial strain distributions between heart disease (left ventricular hypertrophy) patients and the normal control group. The final results show that the proposed method

  18. Establishing point correspondence of 3D faces via sparse facial deformable model.

    PubMed

    Pan, Gang; Zhang, Xiaobo; Wang, Yueming; Hu, Zhenfang; Zheng, Xiaoxiang; Wu, Zhaohui

    2013-11-01

    Establishing a dense vertex-to-vertex anthropometric correspondence between 3D faces is an important and fundamental problem in 3D face research, which can contribute to most applications of 3D faces. This paper proposes a sparse facial deformable model to automatically achieve this task. For an input 3D face, the basic idea is to generate a new 3D face that has the same mesh topology as a reference face and the highly similar shape to the input face, and whose vertices correspond to those of the reference face in an anthropometric sense. Two constraints: 1) the shape constraint and 2) correspondence constraint are modeled in our method to satisfy the three requirements. The shape constraint is solved by a novel face deformation approach in which a normal-ray scheme is integrated to the closest-vertex scheme to keep high-curvature shapes in deformation. The correspondence constraint is based on an assumption that if the vertices on 3D faces are corresponded, their shape signals lie on a manifold and each face signal can be represented sparsely by a few typical items in a dictionary. The dictionary can be well learnt and contains the distribution information of the corresponded vertices. The correspondence information can be conveyed to the sparse representation of the generated 3D face. Thus, a patch-based sparse representation is proposed as the correspondence constraint. By solving the correspondence constraint iteratively, the vertices of the generated face can be adjusted to correspondence positions gradually. At the early iteration steps, smaller sparsity thresholds are set that yield larger representation errors but better globally corresponded vertices. At the later steps, relatively larger sparsity thresholds are used to encode local shapes. By this method, the vertices in the new face approach the right positions progressively until the final global correspondence is reached. Our method is automatic, and the manual work is needed only in training procedure

  19. 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

  20. Point Cloud Storage and Access on a Global Scale

    DTIC Science & Technology

    2015-01-01

    Terabytes of point cloud data resulting in datasets too large for the current state of the art commercial software to Process, Exploit, Disseminate (PED...LiDAR sensors generate Terabytes of point cloud data resulting in datasets too large for the current state of the art commercial software to Process

  1. Satellite and Surface Data Synergy for Developing a 3D Cloud Structure and Properties Characterization Over the ARM SGP. Stage 1: Cloud Amounts, Optical Depths, and Cloud Heights Reconciliation

    NASA Technical Reports Server (NTRS)

    Genkova, I.; Long, C. N.; Heck, P. W.; Minnis, P.

    2003-01-01

    One of the primary Atmospheric Radiation Measurement (ARM) Program objectives is to obtain measurements applicable to the development of models for better understanding of radiative processes in the atmosphere. We address this goal by building a three-dimensional (3D) characterization of the cloud structure and properties over the ARM Southern Great Plains (SGP). We take the approach of juxtaposing the cloud properties as retrieved from independent satellite and ground-based retrievals, and looking at the statistics of the cloud field properties. Once these retrievals are well understood, they will be used to populate the 3D characterization database. As a first step we determine the relationship between surface fractional sky cover and satellite viewing angle dependent cloud fraction (CF). We elaborate on the agreement intercomparing optical depth (OD) datasets from satellite and ground using available retrieval algorithms with relation to the CF, cloud height, multi-layer cloud presence, and solar zenith angle (SZA). For the SGP Central Facility, where output from the active remote sensing cloud layer (ARSCL) valueadded product (VAP) is available, we study the uncertainty of satellite estimated cloud heights and evaluate the impact of this uncertainty for radiative studies.

  2. 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.

  3. 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

  4. Towards automatic indoor reconstruction of cluttered building rooms from point clouds

    NASA Astrophysics Data System (ADS)

    Previtali, M.; Barazzetti, L.; Brumana, R.; Scaioni, M.

    2014-05-01

    Terrestrial laser scanning is increasingly used in architecture and building engineering for as-built modelling of large and medium size civil structures. However, raw point clouds derived from laser scanning survey are generally not directly ready for generation of such models. A manual modelling phase has to be undertaken to edit and complete 3D models, which may cover indoor or outdoor environments. This paper presents an automated procedure to turn raw point clouds into semantically-enriched models of building interiors. The developed method mainly copes with a geometric complexity typical of indoor scenes with prevalence of planar surfaces, such as walls, floors and ceilings. A characteristic aspect of indoor modelling is the large amount of clutter and occlusion that may characterize any point clouds. For this reason the developed reconstruction pipeline was designed to recover and complete missing parts in a plausible way. The accuracy of the presented method was evaluated against traditional manual modelling and showed comparable results.

  5. Description and first results of an explicit electrical scheme in a 3D cloud resolving model

    NASA Astrophysics Data System (ADS)

    Barthe, Christelle; Molinié, Gilles; Pinty, Jean-Pierre

    2005-07-01

    The three-dimensional non-hydrostatic mesoscale model MésoNH of the French community offers the numerical environment to develop a cloud electrification scheme in a consistent way with the original mixed phase microphysical scheme. The charge separation mechanisms are entirely due to non-inductive processes and result from elastic ice-snow, ice-graupel and snow-graupel collisions. The electric charges carried by each of the five hydrometeor categories are transported along the airflow and are exchanged according to the various microphysical mass transfer rates but assuming a power law distribution of the individual charges as a function of the particle size. The electric field is diagnosed at each time step after integrating the electric potential induced by a net charge density in the Poisson equation. Finally, a lightning ash is triggered when the electric field locally steps over a given threshold. It propagates in two opposite directions until the magnitude of the electric field falls below a prescribed value. A fractal branching algorithm is then activated to extend lightning streamers away from the main channel and toward cloudy regions where substantial charge densities are present. Charges are neutralized along the tortuous lightning path with a simple procedure that preserves total charge conservation. The complete electrification scheme tested for an ideal case of vigorous supercellular storm shows an intense electrical activity all along its lifecycle. We show that the model is able to produce a direct tripolar structure of the charges as the result of a temperature charge reversal of - 10 °C and of the different sedimentation rates of the hydrometeors.

  6. Prospects of 3D mapping of the Galactic Centre clouds with X-ray polarimetry

    NASA Astrophysics Data System (ADS)

    Marin, F.; Karas, V.; Kunneriath, D.; Muleri, F.

    2014-07-01

    Despite past panchromatic observations of the innermost part of the Milky Way, the overall structure of the Galactic Centre (GC) remains enigmatic in terms of geometry. In this paper, we aim to show how polarimetry can probe the three-dimensional position of the molecular material in the central ˜100 pc of the GC. We investigate a model where the central supermassive black hole Sgr A* is radiatively coupled to a fragmented circumnuclear disc (CND), an elliptical twisted ring representative of the central molecular zone (CMZ), and the two main, bright molecular clouds Sgr B2 and Sgr C. 8-35 keV integrated polarization mapping reveals that Sgr B2 and Sgr C, situated at the two sides of the CMZ, present the highest polarization degrees (66.5 and 47.8 per cent, respectively), both associated with a polarization position angle ψ = 90° (normal to the scattering plane). The CND shows a lower polarization degree, 1.0 per cent with ψ = -20.5°, tracing the inclination of the CND with respect to the Galactic plane. The CMZ polarization is spatially variable. We also consider a range of spatial locations for Sgr A* and the reprocessing media, and investigate how the modelled three-dimensional geometry influences the resulting GC polarization. The two reflection nebulae are found to always produce high polarization degrees (≫10 per cent). We show that a 500 ks observation with a broad-band polarimeter could constrain the location and the morphology of the scattering material with respect to the emitting source, revealing the past activity of Sgr A*.

  7. Point cloud vs drawing on archaeological site

    NASA Astrophysics Data System (ADS)

    Alby, E.

    2015-08-01

    Archaeology is a discipline closely related to the representation of objects that are at the center of its concerns. At different times of the archaeological method, representation approach takes different forms. It takes place on the archaeological excavation, during the exploration, or in a second time in the warehouse, object after object. It occurs also in different drawing scales. The use of topographical positioning techniques has found its place for decades in the stratigraphic process. Plans and sections are thus readjusted to each other, on the excavation site. These techniques are available to the archaeologist since a long time. The most of the time, a qualified member of the team performs himself these simple topographical operations. The two issues raised in this article are: three-dimensional acquisition techniques can they, first find their place in the same way on the excavation site, and is it conceivable that it could serve to support the representation? The drawing during the excavations is a very time-consuming phase; has it still its place on site? Currently, the drawing is part of the archaeological stratigraphy method. It helps documenting the different layers, which are gradually destroyed during the exploration. Without systematic documentation, any scientific reasoning cannot be done retrospectively and the conclusions would not be any evidence. Is it possible to imagine another way to document these phases without loss compared to the drawing? Laser scanning and photogrammetry are approved as acquisition techniques. What can they bring more to what is already done for archaeologists? Archaeological practice can be seen as divided into two parts: preventive archeology and classical archeology. The first has largely adopted the techniques that provide point clouds to save valuable time on site. Everything that is not destroyed by the archaeological approach will be destroyed by the building construction that triggered the excavations. The

  8. 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

  9. A 3-D numerical study of pinhole diffraction to predict the accuracy of EUV point diffraction interferometry

    SciTech Connect

    Goldberg, K.A. |; Tejnil, E.; Bokor, J. |

    1995-12-01

    A 3-D electromagnetic field simulation is used to model the propagation of extreme ultraviolet (EUV), 13-nm, light through sub-1500 {Angstrom} dia pinholes in a highly absorptive medium. Deviations of the diffracted wavefront phase from an ideal sphere are studied within 0.1 numerical aperture, to predict the accuracy of EUV point diffraction interferometersused in at-wavelength testing of nearly diffraction-limited EUV optical systems. Aberration magnitudes are studied for various 3-D pinhole models, including cylindrical and conical pinhole bores.

  10. Examining In-Cloud Convective Turbulence in Relation to Total Lightning and the 3D Wind Field of Severe Thunderstorms

    NASA Astrophysics Data System (ADS)

    Al-Momar, S. A.; Deierling, W.; Williams, J. K.; Hoffman, E. G.

    2014-12-01

    Convectively induced turbulence (CIT) is commonly listed as a cause or factor in weather-related commercial aviation accidents. In-cloud CIT is generated in part by shears between convective updrafts and downdrafts. Total lightning is also dependent on a robust updraft and the resulting storm electrification. The relationship between total lightning and turbulence could prove useful in operational aviation settings with the use of future measurements from the geostationary lightning mapper (GLM) onboard the GOES-R satellite. Providing nearly hemispheric coverage of total lightning, the GLM could help identify CIT in otherwise data-sparse locations. For a severe thunderstorm case on 7 June 2012 in northeast Colorado, in-cloud eddy dissipation rate estimates from the NCAR/NEXRAD Turbulence Detection Algorithm were compared with cloud electrification data from the Colorado Lightning Mapping Array and radar products from the Denver, Colorado WSR-88D. These comparisons showed that high concentrations of very high frequency (VHF) source densities emitted by lightning occurred near and downstream of the storm's convective core. Severe turbulence was also shown to occur near this area, extending near the melting level of the storm and spreading upward and outward. Additionally, increases/decreases in VHF sources and turbulence volumes occurred within a few minutes of each other; although, light turbulence was shown to increase near one storm's dissipation. This may be due to increased shear from the now downdraft dominate storm. The 3D wind field from this case, obtained by either a dual-Doppler or a Variational Doppler Radar Assimilation System (VDRAS) analysis, will also be examined to further study the relationships between total lightning and thunderstorm kinematics. If these results prove to be robust, lightning may serve as a strong indicator of the location of moderate or greater turbulence.

  11. Accuracy Assessment of Mobile Mapping Point Clouds Using the Existing Environment as Terrestrial Reference

    NASA Astrophysics Data System (ADS)

    Hofmann, S.; Brenner, C.

    2016-06-01

    Mobile mapping data is widely used in various applications, what makes it especially important for data users to get a statistically verified quality statement on the geometric accuracy of the acquired point clouds or its processed products. The accuracy of point clouds can be divided into an absolute and a relative quality, where the absolute quality describes the position of the point cloud in a world coordinate system such as WGS84 or UTM, whereas the relative accuracy describes the accuracy within the point cloud itself. Furthermore, the quality of processed products such as segmented features depends on the global accuracy of the point cloud but mainly on the quality of the processing steps. Several data sources with different characteristics and quality can be thought of as potential reference data, such as cadastral maps, orthophoto, artificial control objects or terrestrial surveys using a total station. In this work a test field in a selected residential area was acquired as reference data in a terrestrial survey using a total station. In order to reach high accuracy the stationing of the total station was based on a newly made geodetic network with a local accuracy of less than 3 mm. The global position of the network was determined using a long time GNSS survey reaching an accuracy of 8 mm. Based on this geodetic network a 3D test field with facades and street profiles was measured with a total station, each point with a two-dimensional position and altitude. In addition, the surface of poles of street lights, traffic signs and trees was acquired using the scanning mode of the total station. Comparing this reference data to the acquired mobile mapping point clouds of several measurement campaigns a detailed quality statement on the accuracy of the point cloud data is made. Additionally, the advantages and disadvantages of the described reference data source concerning availability, cost, accuracy and applicability are discussed.

  12. Scan Line Based Road Marking Extraction from Mobile LiDAR Point Clouds.

    PubMed

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

    2016-06-17

    Mobile Mapping Technology (MMT) is one of the most important 3D spatial data acquisition technologies. The state-of-the-art mobile mapping systems, equipped with laser scanners and named Mobile LiDAR Scanning (MLS) systems, have been widely used in a variety of areas, especially in road mapping and road inventory. With the commercialization of Advanced Driving Assistance Systems (ADASs) and self-driving technology, there will be a great demand for lane-level detailed 3D maps, and MLS is the most promising technology to generate such lane-level detailed 3D maps. Road markings and road edges are necessary information in creating such lane-level detailed 3D maps. This paper proposes a scan line based method to extract road markings from mobile LiDAR point clouds in three steps: (1) preprocessing; (2) road points extraction; (3) road markings extraction and refinement. In preprocessing step, the isolated LiDAR points in the air are removed from the LiDAR point clouds and the point clouds are organized into scan lines. In the road points extraction step, seed road points are first extracted by Height Difference (HD) between trajectory data and road surface, then full road points are extracted from the point clouds by moving least squares line fitting. In the road markings extraction and refinement step, the intensity values of road points in a scan line are first smoothed by a dynamic window median filter to suppress intensity noises, then road markings are extracted by Edge Detection and Edge Constraint (EDEC) method, and the Fake Road Marking Points (FRMPs) are eliminated from the detected road markings by segment and dimensionality feature-based refinement. The performance of the proposed method is evaluated by three data samples and the experiment results indicate that road points are well extracted from MLS data and road markings are well extracted from road points by the applied method. A quantitative study shows that the proposed method achieves an average

  13. A 3D point of view on the habitability of hot, moist atmospheres

    NASA Astrophysics Data System (ADS)

    Leconte, J.; Forget, F.; Wordsworth, R.; Charnay, B.

    2012-12-01

    Because current exoplanets detection methods are biased toward shorter period orbits, most planets discovered to date have a higher equilibrium temperature than the Earth and Venus. If a substantial amount of water is available at the surface, water vapor could become a major constituent of the atmosphere of these planets, and lead to the so-called runaway or moist greenhouse that determines the inner edge of the traditional habitable zone. Modeling the climate of such hot, moist atmospheres is thus mandatory to understand the atmospheric properties of hot transiting terrestrial exoplanets for which observation should soon be available. However, so far, emphasis has been put on 1D radiative convective models, which cannot well predict the impact of clouds, or the non-linear effect of spatial inhomogeneities. In particular, while these single column models can provide reasonable answers for planets with a dense atmosphere or a rapid rotation which limit large scale temperature contrasts, only a tridimensional model can treat properly the case of close in exoplanets for which the rotation rate is synchronized (or pseudo synchronized for eccentric orbits) with the orbital motion. Indeed, this very peculiar radiative forcing can create a strong day-night side temperature contrast and a very efficient cold trap on the night side that cannot be modeled in 1D. To study the processes determining the inner edge of the habitable zone in a wide variety of contexts, we used the new "generic" LMD GCM developed for exoplanet studies and which notably include a versatile radiative transfer code to simulate any atmospheric cocktail of gases, aerosols and clouds for which optical data exists. For the present work, we have implemented a new water cycle scheme with a more robust treatment of the cloud microphysics, precipitations and water vapor continuum opacity. This allows us to model hot atmospheres with an arbitrarily large amount of water vapor. Using this tridimensional model

  14. Reducing and filtering point clouds with enhanced vector quantization.

    PubMed

    Ferrari, Stefano; Ferrigno, Giancarlo; Piuri, Vincenzo; Borghese, N Alberto

    2007-01-01

    Modern scanners are able to deliver huge quantities of three-dimensional (3-D) data points sampled on an object's surface, in a short time. These data have to be filtered and their cardinality reduced to come up with a mesh manageable at interactive rates. We introduce here a novel procedure to accomplish these two tasks, which is based on an optimized version of soft vector quantization (VQ). The resulting technique has been termed enhanced vector quantization (EVQ) since it introduces several improvements with respect to the classical soft VQ approaches. These are based on computationally expensive iterative optimization; local computation is introduced here, by means of an adequate partitioning of the data space called hyperbox (HB), to reduce the computational time so as to be linear in the number of data points N, saving more than 80% of time in real applications. Moreover, the algorithm can be fully parallelized, thus leading to an implementation that is sublinear in N. The voxel side and the other parameters are automatically determined from data distribution on the basis of the Zador's criterion. This makes the algorithm completely automatic. Because the only parameter to be specified is the compression rate, the procedure is suitable even for nontrained users. Results obtained in reconstructing faces of both humans and puppets as well as artifacts from point clouds publicly available on the web are reported and discussed, in comparison with other methods available in the literature. EVQ has been conceived as a general procedure, suited for VQ applications with large data sets whose data space has relatively low dimensionality.

  15. Investigation of lightning flash morphologies along the entire supercell life cycle using a numerical 3D cloud resolving model(CRM).

    NASA Astrophysics Data System (ADS)

    Molinié, G.; Escobar, J.; Gazin, D.

    2008-12-01

    A stochastic lightning flash scheme has been implemented in line in a meso-sca le CRM. It is fully parallelized and vectorized. In this model, a lightning flash is schematized as two single conducting channels (single tracks) propagating in opposite directions from the lightning ignition point. Branch patterns propagate from the single channels. On the base of scale similarities between discharges in dielectrics at centimeter scales and lightning flashes, the stochastic scheme has been designed to compute branch trajec tories. Physical considerations and branch fractal dimensions compel branch trajectories. The charge neutralization operates along the single tracks and branches to threshold the cloud electrical charge. First, an assessment of the scheme will be presented in simple 2D configurations. Second, we will describe comprehensive 3D-thundercloud life-cycle simulations including cloud electrification and lightning discharges. Lightning flash patterns are analyzed through statistics of their effective fractal dimension. It is shown that paradoxically, lightning flashes with quasi-plane branch propagation (fractal dimension close to 2) lead to more steady electrical behavior than those completely filling volumes (fractal dimension close to 3).

  16. Urban Road Detection in Airbone Laser Scanning Point Cloud Using Random Forest Algorithm

    NASA Astrophysics Data System (ADS)

    Kaczałek, B.; Borkowski, A.

    2016-06-01

    The objective of this research is to detect points that describe a road surface in an unclassified point cloud of the airborne laser scanning (ALS). For this purpose we use the Random Forest learning algorithm. The proposed methodology consists of two stages: preparation of features and supervised point cloud classification. In this approach we consider ALS points, representing only the last echo. For these points RGB, intensity, the normal vectors, their mean values and the standard deviations are provided. Moreover, local and global height variations are taken into account as components of a feature vector. The feature vectors are calculated on a basis of the 3D Delaunay triangulation. The proposed methodology was tested on point clouds with the average point density of 12 pts/m2 that represent large urban scene. The significance level of 15% was set up for a decision tree of the learning algorithm. As a result of the Random Forest classification we received two subsets of ALS points. One of those groups represents points belonging to the road network. After the classification evaluation we achieved from 90% of the overall classification accuracy. Finally, the ALS points representing roads were merged and simplified into road network polylines using morphological operations.

  17. 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.

  18. A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Zou, Lejun; Shen, Xiaohua; Ren, Yupeng; Qin, Yi

    2017-02-01

    Conventional manual surveys of rock mass fractures usually require large amounts of time and labor; yet, they provide a relatively small set of data that cannot be considered representative of the study region. Terrestrial laser scanners are increasingly used for fracture surveys because they can efficiently acquire large area, high-resolution, three-dimensional (3D) point clouds from outcrops. However, extracting fractures and other planar surfaces from 3D outcrop point clouds is still a challenging task. No method has been reported that can be used to automatically extract the full extent of every individual fracture from a 3D outcrop point cloud. In this study, we propose a method using a region-growing approach to address this problem; the method also estimates the orientation of each fracture. In this method, criteria based on the local surface normal and curvature of the point cloud are used to initiate and control the growth of the fracture region. In tests using outcrop point cloud data, the proposed method identified and extracted the full extent of individual fractures with high accuracy. Compared with manually acquired field survey data, our method obtained better-quality fracture data, thereby demonstrating the high potential utility of the proposed method.

  19. SG-t optimization and processing technology of the points cloud of the railway tank car (container)

    NASA Astrophysics Data System (ADS)

    Zhang, Zhipeng

    2016-10-01

    Based on 3D laser scanning technology in railway tank car and tank container (the railway tank car (container) for short) application, the point cloud which was incomplete and noise which was found during the scanning process were analyzed. Based on the massive scanning point cloud of railway tank car(container), proposed a fast and effective SG-t point cloud optimization processing method. The SG-t method included sp-H point cloud pre-processing method and Eti-G model reconstruction. The tests showed that the new methods could optimize point cloud which was noise and incomplete in a relatively short time. It could reconstruct model fast and efficient. It could greatly improve the efficiency and precision of scanning. The results which Compared with the results of capacity comparison method showed that measurement uncertainty increased from 4×10-3,k=2 to 3×10-3,k=2.Optimization and processing method of the point cloud of the 3D laser scanning of railway tank car (container) provide a reference to the development of related technologies.

  20. Assessment of Iterative Closest Point Registration Accuracy for Different Phantom Surfaces Captured by an Optical 3D Sensor in Radiotherapy

    PubMed Central

    Walke, Mathias; Gademann, Günther

    2017-01-01

    An optical 3D sensor provides an additional tool for verification of correct patient settlement on a Tomotherapy treatment machine. The patient's position in the actual treatment is compared with the intended position defined in treatment planning. A commercially available optical 3D sensor measures parts of the body surface and estimates the deviation from the desired position without markers. The registration precision of the in-built algorithm and of selected ICP (iterative closest point) algorithms is investigated on surface data of specially designed phantoms captured by the optical 3D sensor for predefined shifts of the treatment table. A rigid body transform is compared with the actual displacement to check registration reliability for predefined limits. The curvature type of investigated phantom bodies has a strong influence on registration result which is more critical for surfaces of low curvature. We investigated the registration accuracy of the optical 3D sensor for the chosen phantoms and compared the results with selected unconstrained ICP algorithms. Safe registration within the clinical limits is only possible for uniquely shaped surface regions, but error metrics based on surface normals improve translational registration. Large registration errors clearly hint at setup deviations, whereas small values do not guarantee correct positioning. PMID:28163773

  1. Non-magnetic photospheric bright points in 3D simulations of the solar atmosphere

    NASA Astrophysics Data System (ADS)

    Calvo, F.; Steiner, O.; Freytag, B.

    2016-11-01

    Context. Small-scale bright features in the photosphere of the Sun, such as faculae or G-band bright points, appear in connection with small-scale magnetic flux concentrations. Aims: Here we report on a new class of photospheric bright points that are free of magnetic fields. So far, these are visible in numerical simulations only. We explore conditions required for their observational detection. Methods: Numerical radiation (magneto-)hydrodynamic simulations of the near-surface layers of the Sun were carried out. The magnetic field-free simulations show tiny bright points, reminiscent of magnetic bright points, only smaller. A simple toy model for these non-magnetic bright points (nMBPs) was established that serves as a base for the development of an algorithm for their automatic detection. Basic physical properties of 357 detected nMBPs were extracted and statistically evaluated. We produced synthetic intensity maps that mimic observations with various solar telescopes to obtain hints on their detectability. Results: The nMBPs of the simulations show a mean bolometric intensity contrast with respect to their intergranular surroundings of approximately 20%, a size of 60-80 km, and the isosurface of optical depth unity is at their location depressed by 80-100 km. They are caused by swirling downdrafts that provide, by means of the centripetal force, the necessary pressure gradient for the formation of a funnel of reduced mass density that reaches from the subsurface layers into the photosphere. Similar, frequently occurring funnels that do not reach into the photosphere, do not produce bright points. Conclusions: Non-magnetic bright points are the observable manifestation of vertically extending vortices (vortex tubes) in the photosphere. The resolving power of 4-m-class telescopes, such as the DKIST, is needed for an unambiguous detection of them. The movie associated to Fig. 1 is available at http://www.aanda.org

  2. 3-D Localization of Virtual Sound Sources: Effects of Visual Environment, Pointing Method, and Training

    PubMed Central

    Majdak, Piotr; Goupell, Matthew J.; Laback, Bernhard

    2010-01-01

    The ability to localize sound sources in three-dimensional space was tested in humans. In experiment 1, naive subjects listened to noises filtered with subject-specific head-related transfer functions. The tested conditions included the pointing method (head or manual pointing) and the visual environment (VE) (darkness or virtual VE). The localization performance was not significantly different between the pointing methods. The virtual VE significantly improved the horizontal precision and reduced the number of front-back confusions. These results show the benefit of using a virtual VE in sound localization tasks. In experiment 2, subjects were provided sound localization training. Over the course of training, the performance improved for all subjects, with the largest improvements occurring during the first 400 trials. The improvements beyond the first 400 trials were smaller. After the training, there was still no significant effect of pointing method, showing that the choice of either head- or manual-pointing method plays a minor role in sound localization performance. The results of experiment 2 reinforce the importance of perceptual training for at least 400 trials in sound localization studies. PMID:20139459

  3. Three-dimensional rendering of computer-generated holograms acquired from point-clouds on light field displays

    NASA Astrophysics Data System (ADS)

    Symeonidou, Athanasia; Blinder, David; Ceulemans, Beerend; Munteanu, Adrian; Schelkens, Peter

    2016-09-01

    Holograms, either optically acquired or simulated numerically from 3D datasets, such as point clouds, have special rendering requirements for display. Evaluating the quality of hologram generation techniques is not straightforward, since high-quality holographic display technologies are still immature, In this paper we present a framework for three-dimensional rendering of colour computer-generated holograms (CGHs) acquired from point-clouds, on high-end light field displays. This allows for the rendering of holographic content with horizontal parallax and wide viewing angle. We deploy prior work, namely a fast CGH method that inherently handles occlusion problems to acquire high quality colour holograms from point clouds. Our experiments showed that rendering holograms with the proposed framework provides 3D effect with depth disparity and horizontal-only with wide viewing angle. Therefore, it allows for the evaluation of CGH techniques regarding functional properties such as depth cues and efficient occlusion handling.

  4. Using DOE-ARM and Space-Based Assets to Assess the Quality of Air Force Weather 3D Cloud Analysis and Forecast Products

    NASA Astrophysics Data System (ADS)

    Nobis, T. E.

    2015-12-01

    Air Force Weather (AFW) has documented requirements for global cloud analysis and forecasting to support DoD missions around the world. To meet these needs, AFW utilizes a number of cloud products. Cloud analyses are constructed using 17 different near real time satellite sources. Products include analysis of the individual satellite transmissions at native satellite resolution and an hourly global merge of all 17 sources on a 24km grid. AFW has also recently started creation of a time delayed global cloud reanalysis to produce a 'best possible' analysis for climatology and verification purposes. Forecasted cloud products include global short-range cloud forecasts created using advection techniques as well as statistically post processed cloud forecast products derived from various global and regional numerical weather forecast models. All of these cloud products cover different spatial and temporal resolutions and are produced on a number of different grid projections. The longer term vision of AFW is to consolidate these various approaches into uniform global numerical weather modeling (NWM) system using advanced cloudy-data assimilation processes to construct the analysis and a licensed version of UKMO's Unified Model to produce the various cloud forecast products. In preparation for this evolution in cloud modeling support, AFW has started to aggressively benchmark the performance of their current capabilities. Cloud information collected from so called 'active' sensors on the ground at the DOE-ARM sites and from space by such instruments as CloudSat, CALIPSO and CATS are being utilized to characterize the performance of AFW products derived largely by passive means. The goal is to understand the performance of the 3D cloud analysis and forecast products of today to help shape the requirements and standards for the future NWM driven system.This presentation will present selected results from these benchmarking efforts and highlight insights and observations

  5. Effects of cyclone diameter on performance of 1D3D cyclones: Cut point and slope

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Cyclones are a commonly used air pollution abatement device for separating particulate matter (PM) from air streams in industrial processes. Several mathematical models have been proposed to predict the cut point of cyclones as cyclone diameter varies. The objective of this research was to determine...

  6. The Cauchy Problem for the 3-D Vlasov-Poisson System with Point Charges

    NASA Astrophysics Data System (ADS)

    Marchioro, Carlo; Miot, Evelyne; Pulvirenti, Mario

    2011-07-01

    In this paper we establish global existence and uniqueness of the solution to the three-dimensional Vlasov-Poisson system in the presence of point charges with repulsive interaction. The present analysis extends an analogous two-dimensional result (Caprino and Marchioro in Kinet. Relat. Models 3(2):241-254, 2010).

  7. Observation of Magnetic Reconnection at a 3D Null Point Associated with a Solar Eruption

    NASA Astrophysics Data System (ADS)

    Sun, J. Q.; Zhang, J.; Yang, K.; Cheng, X.; Ding, M. D.

    2016-10-01

    Magnetic null has long been recognized as a special structure serving as a preferential site for magnetic reconnection (MR). However, the direct observational study of MR at null-points is largely lacking. Here, we show the observations of MR around a magnetic null associated with an eruption that resulted in an M1.7 flare and a coronal mass ejection. The Geostationary Operational Environmental Satellites X-ray profile of the flare exhibited two peaks at ∼02:23 UT and ∼02:40 UT on 2012 November 8, respectively. Based on the imaging observations, we find that the first and also primary X-ray peak was originated from MR in the current sheet (CS) underneath the erupting magnetic flux rope (MFR). On the other hand, the second and also weaker X-ray peak was caused by MR around a null point located above the pre-eruption MFR. The interaction of the null point and the erupting MFR can be described as a two-step process. During the first step, the erupting and fast expanding MFR passed through the null point, resulting in a significant displacement of the magnetic field surrounding the null. During the second step, the displaced magnetic field started to move back, resulting in a converging inflow and subsequently the MR around the null. The null-point reconnection is a different process from the current sheet reconnection in this flare; the latter is the cause of the main peak of the flare, while the former is the cause of the secondary peak of the flare and the conspicuous high-lying cusp structure.

  8. A point cloud based pipeline for depth reconstruction from autostereoscopic sets

    NASA Astrophysics Data System (ADS)

    Niquin, Cédric; Prévost, Stéphanie; Remion, Yannick

    2010-02-01

    This is a three step pipeline to construct a 3D mesh of a scene from a set of N images, destined to be viewed on auto-stereoscopic displays. The first step matches the pixels to create a point cloud using a new algorithm based on graph-cuts. It exploits the data redundancy of the N images to ensure the geometric consistency of the scene and to reduce the graph complexity, in order to speed up the computation. It performs an accurate detection of occlusions and its results can then be used in applications like view synthesis. The second step slightly moves the points along the Z-axis to refine the point cloud. It uses a new cost including both occlusion positions and light variations deduced from the matching. The Z values are selected using a dynamic programming algorithm. This step finally generates a point cloud, which is fine enough for applications like augmented reality. From any of the two previously defined point clouds, the last step creates a colored mesh, which is a convenient data structure to be used in graphics APIs. It also generates N depth maps, allowing a comparison between the results of our method with those of other methods.

  9. Buoyancy effects on the 3D MHD stagnation-point flow of a Newtonian fluid

    NASA Astrophysics Data System (ADS)

    Borrelli, A.; Giantesio, G.; Patria, M. C.; Roşca, N. C.; Roşca, A. V.; Pop, I.

    2017-02-01

    This work examines the steady three-dimensional stagnation-point flow of an electrically conducting Newtonian fluid in the presence of a uniform external magnetic field H0 under the Oberbeck-Boussinesq approximation. We neglect the induced magnetic field and examine the three possible directions of H0 which coincide with the directions of the axes. In all cases it is shown that the governing nonlinear partial differential equations admit similarity solutions. We find that the flow has to satisfy an ordinary differential problem whose solution depends on the Hartmann number M, the buoyancy parameter λ and the Prandtl number Pr. The skin-friction components along the axes are computed and the stagnation-point is classified. The numerical integration shows the existence of dual solutions and the occurrence of the reverse flow for some values of the parameters.

  10. Surface Roughness from Point Clouds - A Multi-Scale Analysis

    NASA Astrophysics Data System (ADS)

    Milenković, Milutin; Ressl, Camillo; Hollaus, Markus; Pfeifer, Norbert

    2013-04-01

    Roughness is a physical parameter of surfaces which should include the surface complexity in geophysical models. In hydrodynamic modeling, e.g., roughness should estimate the resistance caused by the surface on the flow, or in remote sensing, how the signal is scattered. Roughness needs to be estimated as a parameter of the model. This has been identified as main source of the uncertainties in model prediction, mainly due to the errors that follow a traditional roughness estimation, e.g. from surface profiles, or by a visual interpretation and manual delineation from aerial photos. Currently, roughness estimation is shifting towards point clouds of surfaces, which primarily come from laser scanning and image matching techniques. However, those data sets are also not free of errors and may affect roughness estimation. Our study focusses on the estimation of roughness indices from different point clouds, and the uncertainties that follow such a procedure. The analysis is performed on a graveled surface of a river bed in Eastern Austria, using point clouds acquired by a triangulating laser scanner (Minolta Vivid 910), photogrammetry (DSLR camera), and terrestrial laser scanner (Riegl FWF scanner). To enable their comparison, all the point clouds are transformed to a superior coordinate system. Then, different roughness indices are calculated and compared at different scales, including stochastic and features-based indices like RMS of elevation, std.dev., Peak to Valley height, openness. The analysis is additionally supported with the spectral signatures (frequency domain) of the different point clouds. The selected techniques provide point clouds of different resolution (0.1-10cm) and coverage (0.3-10m), which also justifies the multi-scale roughness analysis. By doing this, it becomes possible to differentiate between the measurement errors and the roughness of the object at the resolutions of the point clouds. Parts of this study have been funded by the project

  11. Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds

    NASA Astrophysics Data System (ADS)

    Roynard, X.; Deschaud, J.-E.; Goulette, F.

    2016-06-01

    Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

  12. Robust approximation of the Medial Axis Transform of LiDAR point clouds as a tool for visualisation

    NASA Astrophysics Data System (ADS)

    Peters, Ravi; Ledoux, Hugo

    2016-05-01

    Governments and companies around the world collect point clouds (datasets containing elevation points) because these are useful for many applications, e.g. to reconstruct 3D city models, to understand and predict the impact of floods, and to monitor dikes. We address in this paper the visualisation of point clouds, which is perhaps the most essential instrument a practitioner or a scientist has to analyse and understand such datasets. We argue that it is currently hampered by two main problems: (1) point clouds are often massive (several billion points); (2) the viewer's perception of depth and structure is often lost (because of the sparse and unstructured points). We propose solving both problems by using the Medial Axis Transform (MAT) and its properties. This allows us to (1) smartly simplify a point cloud in a geometry-dependent way (to preserve only significant features), and (2) to render splats whose radii are adaptive to the distribution of points (and thus obtain less "holes" in the surface). Our main contribution is a series of heuristics that allows us to compute the MAT robustly for noisy real-world LiDAR point clouds, and to compute the MAT for point clouds that do not fit into the main memory. We have implemented our algorithms, we report on experiments made with point clouds (of more than one billion points), and we demonstrate that we are able to render scenes with much less points than in the original point cloud (we preserve around 10%) while retaining good depth-perception and a sense of structure at close viewing distances.

  13. Pose-invariant face-head identification using a bank of neural networks and the 3D neck reference point

    NASA Astrophysics Data System (ADS)

    Hild, Michael; Yoshida, Kazunobu; Hashimoto, Motonobu

    2003-03-01

    A method for recognizing faces in relativley unconstrained environments, such as offices, is described. It can recognize faces occurring over an extended range of orientations and distances relative to the camera. As the pattern recognition mechanism, a bank of small neural networks of the multilayer perceptron type is used, where each perceptron has the task of recognizing only a single person's face. The perceptrons are trained with a set of nine face images representing the nine main facial orientations of the person to be identified, and a set face images from various other persons. The center of the neck is determined as the reference point for face position unification. Geometric normalization and reference point determination utilizes 3-D data point measurements obtained with a stereo camera. The system achieves a recognition rate of about 95%.

  14. Well log analysis to assist the interpretation of 3-D seismic data at Milne Point, north slope of Alaska

    USGS Publications Warehouse

    Lee, Myung W.

    2005-01-01

    In order to assess the resource potential of gas hydrate deposits in the North Slope of Alaska, 3-D seismic and well data at Milne Point were obtained from BP Exploration (Alaska), Inc. The well-log analysis has three primary purposes: (1) Estimate gas hydrate or gas saturations from the well logs; (2) predict P-wave velocity where there is no measured P-wave velocity in order to generate synthetic seismograms; and (3) edit P-wave velocities where degraded borehole conditions, such as washouts, affected the P-wave measurement significantly. Edited/predicted P-wave velocities were needed to map the gas-hydrate-bearing horizons in the complexly faulted upper part of 3-D seismic volume. The estimated gas-hydrate/gas saturations from the well logs were used to relate to seismic attributes in order to map regional distribution of gas hydrate inside the 3-D seismic grid. The P-wave velocities were predicted using the modified Biot-Gassmann theory, herein referred to as BGTL, with gas-hydrate saturations estimated from the resistivity logs, porosity, and clay volume content. The effect of gas on velocities was modeled using the classical Biot-Gassman theory (BGT) with parameters estimated from BGTL.

  15. The Neighboring Column Approximation (NCA) - A fast approach for the calculation of 3D thermal heating rates in cloud resolving models

    NASA Astrophysics Data System (ADS)

    Klinger, Carolin; Mayer, Bernhard

    2016-01-01

    Due to computational costs, radiation is usually neglected or solved in plane parallel 1D approximation in today's numerical weather forecast and cloud resolving models. We present a fast and accurate method to calculate 3D heating and cooling rates in the thermal spectral range that can be used in cloud resolving models. The parameterization considers net fluxes across horizontal box boundaries in addition to the top and bottom boundaries. Since the largest heating and cooling rates occur inside the cloud, close to the cloud edge, the method needs in first approximation only the information if a grid box is at the edge of a cloud or not. Therefore, in order to calculate the heating or cooling rates of a specific grid box, only the directly neighboring columns are used. Our so-called Neighboring Column Approximation (NCA) is an analytical consideration of cloud side effects which can be considered a convolution of a 1D radiative transfer result with a kernel or radius of 1 grid-box (5 pt stencil) and which does usually not break the parallelization of a cloud resolving model. The NCA can be easily applied to any cloud resolving model that includes a 1D radiation scheme. Due to the neglect of horizontal transport of radiation further away than one model column, the NCA works best for model resolutions of about 100 m or lager. In this paper we describe the method and show a set of applications of LES cloud field snap shots. Correction terms, gains and restrictions of the NCA are described. Comprehensive comparisons to the 3D Monte Carlo Model MYSTIC and a 1D solution are shown. In realistic cloud fields, the full 3D simulation with MYSTIC shows cooling rates up to -150 K/d (100 m resolution) while the 1D solution shows maximum coolings of only -100 K/d. The NCA is capable of reproducing the larger 3D cooling rates. The spatial distribution of the heating and cooling is improved considerably. Computational costs are only a factor of 1.5-2 higher compared to a 1D

  16. 3D Elastic Solutions for Laterally Loaded Discs: Generalised Brazilian and Point Load Tests

    NASA Astrophysics Data System (ADS)

    Serati, Mehdi; Alehossein, Habib; Williams, David J.

    2014-07-01

    This paper investigates the application of a double Fourier series technique to the construction of an elastic stress field in a cylindrical bar subject to lateral boundary loads. The lateral loads, including the constant load boundary conditions, are represented by two Fourier series: one on the perimeter of the circular section ( r 0, θ) and the other on the longitudinal curved surface parallel to the bar axis ( z). The technique invokes acceptable potential functions of the Papkovich-Neuber displacement field, satisfying the governing partial differential equations, to assign appropriate odd and even trigonometric Fourier terms in cylindrical coordinates ( r, θ, z). The generic solution decomposes the problem of interest to a state of stress caused by two independent boundary conditions along the z axis and θ-polar angle, both superimposed on a solution for which these potentials are the product of the trigonometric terms of the independent variables ( θ, z). Constants appearing in the resultant second-order partial differential equations are determined from the generally mixed (tractions and/or displacements) boundary conditions. While the solutions are satisfied exactly at the ends of an infinite bar, they are satisfied weakly on average, in the light of Saint Venant's approximation at the two ends of a finite bar. The application of the proposed analysis is verified against available elastic solutions for axisymmetric and non-axisymmetric engineering problems such as the indirect Brazilian Tensile Strength and Point Load Strength tests.

  17. Optimized shape semantic graph representation for object understanding and recognition in point clouds

    NASA Astrophysics Data System (ADS)

    Ning, Xiaojuan; Wang, Yinghui; Meng, Weiliang; Zhang, Xiaopeng

    2016-10-01

    To understand and recognize the three-dimensional (3-D) objects represented as point cloud data, we use an optimized shape semantic graph (SSG) to describe 3-D objects. Based on the decomposed components of an object, the boundary surface of different components and the topology of components, the SSG gives a semantic description that is consistent with human vision perception. The similarity measurement of the SSG for different objects is effective for distinguishing the type of object and finding the most similar one. Experiments using a shape database show that the SSG is valuable for capturing the components of the objects and the corresponding relations between them. The SSG is not only suitable for an object without any loops but also appropriate for an object with loops to represent the shape and the topology. Moreover, a two-step progressive similarity measurement strategy is proposed to effectively improve the recognition rate in the shape database containing point-sample data.

  18. 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

  19. 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.

  20. Partial difference operators on weighted graphs for image processing on surfaces and point clouds.

    PubMed

    Lozes, Francois; Elmoataz, Abderrahim; Lezoray, Olivier

    2014-09-01

    Partial difference equations (PDEs) and variational methods for image processing on Euclidean domains spaces are very well established because they permit to solve a large range of real computer vision problems. With the recent advent of many 3D sensors, there is a growing interest in transposing and solving PDEs on surfaces and point clouds. In this paper, we propose a simple method to solve such PDEs using the framework of PDEs on graphs. This latter approach enables us to transcribe, for surfaces and point clouds, many models and algorithms designed for image processing. To illustrate our proposal, three problems are considered: (1) p -Laplacian restoration and inpainting; (2) PDEs mathematical morphology; and (3) active contours segmentation.

  1. Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models

    NASA Astrophysics Data System (ADS)

    Abayowa, Bernard O.; Yilmaz, Alper; Hardie, Russell C.

    2015-08-01

    This paper presents a framework for automatic registration of both the optical and 3D structural information extracted from oblique aerial imagery to a Light Detection and Ranging (LiDAR) point cloud without prior knowledge of an initial alignment. The framework employs a coarse to fine strategy in the estimation of the registration parameters. First, a dense 3D point cloud and the associated relative camera parameters are extracted from the optical aerial imagery using a state-of-the-art 3D reconstruction algorithm. Next, a digital surface model (DSM) is generated from both the LiDAR and the optical imagery-derived point clouds. Coarse registration parameters are then computed from salient features extracted from the LiDAR and optical imagery-derived DSMs. The registration parameters are further refined using the iterative closest point (ICP) algorithm to minimize global error between the registered point clouds. The novelty of the proposed approach is in the computation of salient features from the DSMs, and the selection of matching salient features using geometric invariants coupled with Normalized Cross Correlation (NCC) match validation. The feature extraction and matching process enables the automatic estimation of the coarse registration parameters required for initializing the fine registration process. The registration framework is tested on a simulated scene and aerial datasets acquired in real urban environments. Results demonstrates the robustness of the framework for registering optical and 3D structural information extracted from aerial imagery to a LiDAR point cloud, when co-existing initial registration parameters are unavailable.

  2. Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds

    ERIC Educational Resources Information Center

    Sun, Shaohui

    2013-01-01

    Urban reconstruction, with an emphasis on man-made structure modeling, is an active research area with broad impact on several potential applications. Urban reconstruction combines photogrammetry, remote sensing, computer vision, and computer graphics. Even though there is a huge volume of work that has been done, many problems still remain…

  3. The Direct Registration of LIDAR Point Clouds and High Resolution Image Based on Linear Feature by Introducing AN Unknown Parameter

    NASA Astrophysics Data System (ADS)

    Chunjing, Y.; Guang, G.

    2012-07-01

    The registration between optical images and point clouds is the first task when the combination of these two datasets is concerned. Due to the discrete nature of the point clouds, and the 2D-3D transformation in particular, a tie points based registration strategy which is commonly adopted in image-to-image registration is hard to be used directly in this scenario. A derived collinear equation describing the map relationship between an image point and a ground point is used as the mathematical model for registration, with the point in the LiDAR space expressed by its parametric form. such a map relation can be viewed as the mathematical model which registers the image pixels to point clouds. This model is not only suitable for a single image registration but also applicable to multiple consecutive images. We also studied scale problem in image and point clouds registration, with scale problem is defined by the optimal corresponding between the image resolution and the density of point clouds. Test dataset includes the DMC images and point clouds acquired by the Leica ALS50 II over an area in Henan Prov., China. Main contributions of the paper includes: [1] an derived collinear equation is introduced by which a ground point is expressed by its parametric form, which makes it possible to replace point feature by linear feature, hence avoiding the problem that it is almost impossible to find a point in the point clouds which is accurately corresponds to a point in the image space; [2] least square method is used to calculate the registration transformation parameters and the unknown parameter λ in the same time;[3] scale problem is analyzed semi-quantitatively and to the authors' best knowledge, it is the first time in literature that clearly defines the scale problem and carries out semi-quantitative analysis in the context of LiDAR data processing.

  4. 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.

  5. 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

  6. A quantitative study of 3D-scanning frequency and Δd of tracking points on the tooth surface

    PubMed Central

    Li, Hong; Lyu, Peijun; Sun, Yuchun; Wang, Yong; Liang, Xiaoyue

    2015-01-01

    Micro-movement of human jaws in the resting state might influence the accuracy of direct three-dimensional (3D) measurement. Providing a reference for sampling frequency settings of intraoral scanning systems to overcome this influence is important. In this study, we measured micro-movement, or change in distance (∆d), as the change in position of a single tracking point from one sampling time point to another in five human subjects. ∆d of tracking points on incisors at 7 sampling frequencies was judged against the clinical accuracy requirement to select proper sampling frequency settings. The curve equation was then fit quantitatively between ∆d median and the sampling frequency to predict the trend of ∆d with increasing f. The difference of ∆d among the subjects and the difference between upper and lower incisor feature points of the same subject were analyzed by a non-parametric test (α = 0.05). Significant differences of incisor feature points were noted among different subjects and between upper and lower jaws of the same subject (P < 0.01). Overall, ∆d decreased with increasing frequency. When the frequency was 60 Hz, ∆d nearly reached the clinical accuracy requirement. Frequencies higher than 60 Hz did not significantly decrease Δd further. PMID:26400112

  7. Automatic building extraction and segmentation directly from lidar point clouds

    NASA Astrophysics Data System (ADS)

    Jiang, Jingjue; Ming, Ying

    2006-10-01

    This paper presents an automatic approach for building extraction and segmentation directly from Lidar point clouds without previous rasterization or triangulation. The algorithm works in the following sequential steps. First, a filtering algorithm, which is capable of preserving steep terrain features, is performed on raw Lidar point clouds. Points that belong to the bare earth and those that belong to buildings are separated. Second, the building points which may include some vegetation and other objects due to the disturbance of noise and the distribution of points are segmented further by using a Riemannian Graph. Then building segments are recognized by considering size and roughness. Finally, each segment can be treated as a building roof plane. Experiment results show that the algorithm is very promising.

  8. Fast, Automated, 3D Modeling of Building Interiors

    DTIC Science & Technology

    2012-10-30

    of thermographies with laser scanning point clouds [6]. Given the heterogeneous nature of the two modalities, we propose a feature-based approach...extract 2D lines from thermographies , and 3D lines are extracted through segmentation of the point cloud. Feature- matching and the relative pose between... thermographies and point cloud are obtained from an iterative procedure applied to detect and reject outliers; this includes rotation matrix and

  9. 3D radiative transfer effects in multi-angle/multispectral radio-polarimetric signals from a mixture of clouds and aerosols viewed by a non-imaging sensor

    NASA Astrophysics Data System (ADS)

    Davis, Anthony B.; Garay, Michael J.; Xu, Feng; Qu, Zheng; Emde, Claudia

    2013-09-01

    When observing a spatially complex mix of aerosols and clouds in a single relatively large field-of-view, nature entangles their signals non-linearly through polarized radiation transport processes that unfold in the 3D position and direction spaces. In contrast, any practical forward model in a retrieval algorithm will use only 1D vector radiative transfer (vRT) in a linear mixing technique. We assess the difference between the observed and predicted signals using synthetic data from a high-fidelity 3D vRT model with clouds generated using a Large Eddy Simulation model and an aerosol climatology. We find that this difference is signal—not noise—for the Aerosol Polarimetry Sensor (APS), an instrument developed by NASA. Moreover, the worst case scenario is also the most interesting case, namely, when the aerosol burden is large, hence hase the most impact on the cloud microphysics and dynamics. Based on our findings, we formulate a mitigation strategy for these unresolved cloud adjacency effects assuming that some spatial information is available about the structure of the clouds at higher resolution from "context" cameras, as was planned for NASA's ill-fated Glory mission that was to carry the APS but failed to reach orbit. Application to POLDER (POLarization and Directionality of Earth Reflectances) data from the period when PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) was in the A-train is briefly discussed.

  10. The Iqmulus Urban Showcase: Automatic Tree Classification and Identification in Huge Mobile Mapping Point Clouds

    NASA Astrophysics Data System (ADS)

    Böhm, J.; Bredif, M.; Gierlinger, T.; Krämer, M.; Lindenberg, R.; Liu, K.; Michel, F.; Sirmacek, B.

    2016-06-01

    Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.

  11. Range determination for generating point clouds from airborne small footprint LiDAR waveforms.

    PubMed

    Qin, Yuchu; Vu, Tuong Thuy; Ban, Yifang; Niu, Zheng

    2012-11-05

    This paper presents a range determination approach for generating point clouds from small footprint LiDAR waveforms. Waveform deformation over complex terrain area is simulated using convolution. Drift of the peak center position is analyzed to identify the first echo returned by the illuminated objects in the LiDAR footprint. An approximate start point of peak in the waveform is estimated and adopted as the indicator of range calculation; range correction method is proposed to correct pulse widening over complex terrain surface. The experiment was carried out on small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. The results suggest that the proposed approach generates more points than standard commercial products; based on field measurements, a comparative analysis between the point clouds generated by the proposed approach and the commercial software GeocodeWF indicates that: 1). the proposed approach obtained more accurate tree heights; 2). smooth surface can be achieved with low standard deviation. In summary, the proposed approach provides a satisfactory solution for range determination in estimating 3D coordinate values of point clouds, especially for correcting range information of waveforms containing deformed peaks.

  12. Voronoi-Based Curvature and Feature Estimation from Point Clouds.

    PubMed

    Mérigot, Quentin; Ovsjanikov, Maks; Guibas, Leonidas

    2011-06-01

    We present an efficient and robust method for extracting curvature information, sharp features, and normal directions of a piecewise smooth surface from its point cloud sampling in a unified framework. Our method is integral in nature and uses convolved covariance matrices of Voronoi cells of the point cloud which makes it provably robust in the presence of noise. We show that these matrices contain information related to curvature in the smooth parts of the surface, and information about the directions and angles of sharp edges around the features of a piecewise-smooth surface. Our method is applicable in both two and three dimensions, and can be easily parallelized, making it possible to process arbitrarily large point clouds, which was a challenge for Voronoi-based methods. In addition, we describe a Monte-Carlo version of our method, which is applicable in any dimension. We illustrate the correctness of both principal curvature information and feature extraction in the presence of varying levels of noise and sampling density on a variety of models. As a sample application, we use our feature detection method to segment point cloud samplings of piecewise-smooth surfaces.

  13. From cloud-of-point coordinates to three-dimensional virtual environment: the data conversion system

    NASA Astrophysics Data System (ADS)

    Sitnik, Robert; Kujawinska, Malgorzata

    2002-02-01

    The sequential steps of conversion of data gathered by a full-field 3-D shape measurement optical system into CAD/CAM and multimedia environments are discussed. The complete triangulation algorithm, which automatically creates the triangle mesh from the input cloud of points, is described. Each block of this algorithm is explained in detail with special attention paid to the parameters controlling the quality of the data conversion process. The adaptive process of reducing the number of the triangles based on a second derivative of local curvature of an objects' surface is explained. The error analysis is discussed at each step of the cloud data processing in dependency of the algorithm initial parameters. The three algorithms that process additional color information (R,G,B) into the texture mapped on the triangle mesh is presented. The usefulness of the complete conversion process is proved by the manufacturing of an exemplary object, exporting a human 3-D face to the Internet and an example object into a 3-D virtual environment.

  14. Genomic cloud computing: legal and ethical points to consider

    PubMed Central

    Dove, Edward S; Joly, Yann; Tassé, Anne-Marie; Burton, Paul; Chisholm, Rex; Fortier, Isabel; Goodwin, Pat; Harris, Jennifer; Hveem, Kristian; Kaye, Jane; Kent, Alistair; Knoppers, Bartha Maria; Lindpaintner, Klaus; Little, Julian; Riegman, Peter; Ripatti, Samuli; Stolk, Ronald; Bobrow, Martin; Cambon-Thomsen, Anne; Dressler, Lynn; Joly, Yann; Kato, Kazuto; Knoppers, Bartha Maria; Rodriguez, Laura Lyman; McPherson, Treasa; Nicolás, Pilar; Ouellette, Francis; Romeo-Casabona, Carlos; Sarin, Rajiv; Wallace, Susan; Wiesner, Georgia; Wilson, Julia; Zeps, Nikolajs; Simkevitz, Howard; De Rienzo, Assunta; Knoppers, Bartha M

    2015-01-01

    The biggest challenge in twenty-first century data-intensive genomic science, is developing vast computer infrastructure and advanced software tools to perform comprehensive analyses of genomic data sets for biomedical research and clinical practice. Researchers are increasingly turning to cloud computing both as a solution to integrate data from genomics, systems biology and biomedical data mining and as an approach to analyze data to solve biomedical problems. Although cloud computing provides several benefits such as lower costs and greater efficiency, it also raises legal and ethical issues. In this article, we discuss three key ‘points to consider' (data control; data security, confidentiality and transfer; and accountability) based on a preliminary review of several publicly available cloud service providers' Terms of Service. These ‘points to consider' should be borne in mind by genomic research organizations when negotiating legal arrangements to store genomic data on a large commercial cloud service provider's servers. Diligent genomic cloud computing means leveraging security standards and evaluation processes as a means to protect data and entails many of the same good practices that researchers should always consider in securing their local infrastructure. PMID:25248396

  15. Genomic cloud computing: legal and ethical points to consider.

    PubMed

    Dove, Edward S; Joly, Yann; Tassé, Anne-Marie; Knoppers, Bartha M

    2015-10-01

    The biggest challenge in twenty-first century data-intensive genomic science, is developing vast computer infrastructure and advanced software tools to perform comprehensive analyses of genomic data sets for biomedical research and clinical practice. Researchers are increasingly turning to cloud computing both as a solution to integrate data from genomics, systems biology and biomedical data mining and as an approach to analyze data to solve biomedical problems. Although cloud computing provides several benefits such as lower costs and greater efficiency, it also raises legal and ethical issues. In this article, we discuss three key 'points to consider' (data control; data security, confidentiality and transfer; and accountability) based on a preliminary review of several publicly available cloud service providers' Terms of Service. These 'points to consider' should be borne in mind by genomic research organizations when negotiating legal arrangements to store genomic data on a large commercial cloud service provider's servers. Diligent genomic cloud computing means leveraging security standards and evaluation processes as a means to protect data and entails many of the same good practices that researchers should always consider in securing their local infrastructure.

  16. 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

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

    PubMed

    Arastounia, Mostafa; Oude Elberink, Sander

    2016-12-12

    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.

  18. Atypical late-time singular regimes accurately diagnosed in stagnation-point-type solutions of 3D Euler flows

    NASA Astrophysics Data System (ADS)

    Mulungye, Rachel M.; Lucas, Dan; Bustamante, Miguel D.

    2016-02-01

    We revisit, both numerically and analytically, the finite-time blowup of the infinite-energy solution of 3D Euler equations of stagnation-point-type introduced by Gibbon et al. (1999). By employing the method of mapping to regular systems, presented in Bustamante (2011) and extended to the symmetry-plane case by Mulungye et al. (2015), we establish a curious property of this solution that was not observed in early studies: before but near singularity time, the blowup goes from a fast transient to a slower regime that is well resolved spectrally, even at mid-resolutions of $512^2.$ This late-time regime has an atypical spectrum: it is Gaussian rather than exponential in the wavenumbers. The analyticity-strip width decays to zero in a finite time, albeit so slowly that it remains well above the collocation-point scale for all simulation times $t < T^* - 10^{-9000}$, where $T^*$ is the singularity time. Reaching such a proximity to singularity time is not possible in the original temporal variable, because floating point double precision ($\\approx 10^{-16}$) creates a `machine-epsilon' barrier. Due to this limitation on the \\emph{original} independent variable, the mapped variables now provide an improved assessment of the relevant blowup quantities, crucially with acceptable accuracy at an unprecedented closeness to the singularity time: $T^*- t \\approx 10^{-140}.$

  19. A Coupled fcGCM-GCE Modeling System: A 3D Cloud Resolving Model and a Regional Scale Model

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo

    2005-01-01

    Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and ore sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (21CE, several 31CE), Goddard radiation (including explicity calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generation regional scale model, WRF. In this talk, I will present: (1) A Brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), (3) A discussion on the Goddard WRF version (its developments and applications), and (4) The characteristics of the four-dimensional cloud data

  20. Automatic Extraction and Regularization of Building Outlines from Airborne LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Albers, Bastian; Kada, Martin; Wichmann, Andreas

    2016-06-01

    Building outlines are needed for various applications like urban planning, 3D city modelling and updating cadaster. Their automatic reconstruction, e.g. from airborne laser scanning data, as regularized shapes is therefore of high relevance. Today's airborne laser scanning technology can produce dense 3D point clouds with high accuracy, which makes it an eligible data source to reconstruct 2D building outlines or even 3D building models. In this paper, we propose an automatic building outline extraction and regularization method that implements a trade-off between enforcing strict shape restriction and allowing flexible angles using an energy minimization approach. The proposed procedure can be summarized for each building as follows: (1) an initial building outline is created from a given set of building points with the alpha shape algorithm; (2) a Hough transform is used to determine the main directions of the building and to extract line segments which are oriented accordingly; (3) the alpha shape boundary points are then repositioned to both follow these segments, but also to respect their original location, favoring long line segments and certain angles. The energy function that guides this trade-off is evaluated with the Viterbi algorithm.

  1. Production of Lightning NO(x) and its Vertical Distribution Calculated from 3-D Cloud-scale Chemical Transport Model Simulations

    NASA Technical Reports Server (NTRS)

    Ott, Lesley; Pickering, Kenneth; Stenchikov, Georgiy; Allen, Dale; DeCaria, Alex; Ridley, Brian; Lin, Ruei-Fong; Lang, Steve; Tao, Wei-Kuo

    2009-01-01

    A 3-D cloud scale chemical transport model that includes a parameterized source of lightning NO(x), based on observed flash rates has been used to simulate six midlatitude and subtropical thunderstorms observed during four field projects. Production per intracloud (P(sub IC) and cloud-to-ground (P(sub CG)) flash is estimated by assuming various values of P(sub IC) and P(sub CG) for each storm and determining which production scenario yields NO(x) mixing ratios that compare most favorably with in-cloud aircraft observations. We obtain a mean P(sub CG) value of 500 moles NO (7 kg N) per flash. The results of this analysis also suggest that on average, P(sub IC) may be nearly equal to P(sub CG), which is contrary to the common assumption that intracloud flashes are significantly less productive of NO than are cloud-to-ground flashes. This study also presents vertical profiles of the mass of lightning NO(x), after convection based on 3-D cloud-scale model simulations. The results suggest that following convection, a large percentage of lightning NO(x), remains in the middle and upper troposphere where it originated, while only a small percentage is found near the surface. The results of this work differ from profiles calculated from 2-D cloud-scale model simulations with a simpler lightning parameterization that were peaked near the surface and in the upper troposphere (referred to as a "C-shaped" profile). The new model results (a backward C-shaped profile) suggest that chemical transport models that assume a C-shaped vertical profile of lightning NO(x) mass may place too much mass neat the surface and too little in the middle troposphere.

  2. Correction and Densification of Uas-Based Photogrammetric Thermal Point Cloud

    NASA Astrophysics Data System (ADS)

    Akcay, O.; Erenoglu, R. C.; Erenoglu, O.

    2016-06-01

    Photogrammetric processing algorithms can suffer problems due to either the initial image quality (noise, low radiometric quality, shadows and so on) or to certain surface materials (shiny or textureless objects). This can result in noisy point clouds and/or difficulties in feature extraction. Specifically, dense point clouds which are generated with photogrammetric method using a lightweight thermal camera, are more noisy and sparse than the point clouds of high-resolution digital camera images. In this paper, new method which produces more reliable and dense thermal point cloud using the sparse thermal point cloud and high resolution digital point cloud was considered. Both thermal and digital images were obtained with UAS (Unmanned Aerial System) based lightweight Optris PI 450 and Canon EOS 605D camera images. Thermal and digital point clouds, and orthophotos were produced using photogrammetric methods. Problematic thermal point cloud was transformed to a high density thermal point cloud using image processing methods such as rasterizing, registering, interpolation and filling. The results showed that the obtained thermal point cloud - up to chosen processing parameters - was 87% more densify than the original point cloud. The second improvement was gained at the height accuracy of the thermal point cloud. New densified point cloud has more consistent elevation model while the original thermal point cloud shows serious deviations from the expected surface model.

  3. Analysis of shallow landslides by morphometry parameters derived from terrestrial laser scanning point clouds

    NASA Astrophysics Data System (ADS)

    Mayr, A.; Rutzinger, M.; Bremer, M.; Wiegand, C.; Kringer, K.; Geitner, C.

    2012-04-01

    Erosion by shallow landslides is a widespread and growing phenomenon in mountainous areas. The major consequences are loss of soil and regolith as well as damages on infrastructure and provision of unconsolidated material for secondary processes such as mudflows. In this study we present a concept for extracting morphometry parameters from terrestrial laser scanning (TLS) point clouds in order to investigate the relation between slope surface structure and regolith depth. TLS is used to collect high-resolution point cloud data of an affected slope in the Schmirn Valley (Tyrol, Austria). Regolith depth is considered to be one of the important factors for the development of shallow landslides. However, direct field measurements are labour- and time-consuming. In this study we developed an approach, to investigate the relation between regolith depth and surface morphometry parameters. The reference regolith depth information is derived from lightweight dynamic cone penetrometer tests (DCPT) within the test site. The suggested approach integrates spatial analysis of Geographic Information Systems and point cloud processing algorithms. It will help to enhance the prediction of shallow landslide occurrence by (i) deriving high resolution 3D morphometric parameters and (ii) determining regolith depth with a reasonable effort due to automation. In future we want to be able to contribute with this concept to the detailed modelling of shallow landslide susceptibility on alpine slopes.

  4. Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

    NASA Technical Reports Server (NTRS)

    Hammond, Marcus; Clark, Ashley; Mahajan, Aditya; Sharma, Sumant; Rock, Stephen

    2015-01-01

    An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

  5. An automated method to register airborne and terrestrial laser scanning point clouds

    NASA Astrophysics Data System (ADS)

    Yang, Bisheng; Zang, Yufu; Dong, Zhen; Huang, Ronggang

    2015-11-01

    Laser scanning techniques have been widely used to capture three-dimensional (3D) point clouds of various scenes (e.g. urban scenes). In particular, airborne laser scanning (ALS) and mobile laser scanning (MLS), terrestrial laser scanning (TLS) are effective to capture point clouds from top or side view. Registering the complimentary point clouds captured by ALS and MLS/TLS provides an aligned data source for many purposes (e.g. 3D reconstruction). Among these MLS can be directly geo-referenced to ALS according to the equipped position systems. For small scanning areas or dense building areas, TLS is used instead of MLS. However, registering ALS and TLS datasets suffers from poor automation and robustness because of few overlapping areas and sparse corresponding geometric features. A robust method for the registration of TLS and ALS datasets is proposed, which has four key steps. (1) extracts building outlines from TLS and ALS data sets independently; (2) obtains the potential matching pairs of outlines according to the geometric constraints between building outlines; (3) constructs the Laplacian matrices of the extracted building outlines to model the topology between the geometric features; (4) calculates the correlation coefficients of the extracted geometric features by decomposing the Laplacian matrices into the spectral space, providing correspondences between the extracted features for coarse registration. Finally, the multi-line adjustment strategy is employed for the fine registration. The robustness and accuracy of the proposed method are verified using field data, demonstrating a reliable and stable solution to accurately register ALS and TLS datasets.

  6. Effect of Clouds on Optical Imaging of the Space Shuttle During the Ascent Phase: A Statistical Analysis Based on a 3D Model

    NASA Technical Reports Server (NTRS)

    Short, David A.; Lane, Robert E., Jr.; Winters, Katherine A.; Madura, John T.

    2004-01-01

    Clouds are highly effective in obscuring optical images of the Space Shuttle taken during its ascent by ground-based and airborne tracking cameras. Because the imagery is used for quick-look and post-flight engineering analysis, the Columbia Accident Investigation Board (CAIB) recommended the return-to-flight effort include an upgrade of the imaging system to enable it to obtain at least three useful views of the Shuttle from lift-off to at least solid rocket booster (SRB) separation (NASA 2003). The lifetimes of individual cloud elements capable of obscuring optical views of the Shuttle are typically 20 minutes or less. Therefore, accurately observing and forecasting cloud obscuration over an extended network of cameras poses an unprecedented challenge for the current state of observational and modeling techniques. In addition, even the best numerical simulations based on real observations will never reach "truth." In order to quantify the risk that clouds would obscure optical imagery of the Shuttle, a 3D model to calculate probabilistic risk was developed. The model was used to estimate the ability of a network of optical imaging cameras to obtain at least N simultaneous views of the Shuttle from lift-off to SRB separation in the presence of an idealized, randomized cloud field.

  7. 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

  8. Determining Stand Parameters from Uas-Based Point Clouds

    NASA Astrophysics Data System (ADS)

    Yilmaz, V.; Serifoglu, C.; Gungor, O.

    2016-06-01

    In Turkey, forest management plans are produced by terrestrial surveying techniques for 10 or 20 year periods, which can be considered quite long to maintain the sustainability of forests. For a successful forest management plan, it is necessary to collect accurate information about the stand parameters and store them in dynamic and robust databases. The position, number, height and closure of trees are among the most important stand parameters required for a forest management plan. Determining the position of each single tree is challenging in such an area consisting of too many interlocking trees. Hence, in this study, an object-based tree detection methodology has been developed in MATLAB programming language to determine the position of each tree top in a highly closed area. The developed algorithm uses the Canopy Height Model (CHM), which is computed from the Digital Terrain Model (DTM) and Digital Surface Model (DSM) generated by using the point cloud extracted from the images taken from a UAS (Unmanned Aerial System). The heights of trees have been determined by using the CHM. The closure of the trees has been determined with the written MATLAB script. The results show that the developed tree detection methodology detected more than 70% of the trees successfully. It can also be concluded that the stand parameters may be determined by using the UAS-based point clouds depending on the characteristics of the study area. In addition, determination of the stand parameters by using point clouds reduces the time needed to produce forest management plans.

  9. Micelle Mediated Trace Level Sulfide Quantification through Cloud Point Extraction

    PubMed Central

    Devaramani, Samrat; Malingappa, Pandurangappa

    2012-01-01

    A simple cloud point extraction protocol has been proposed for the quantification of sulfide at trace level. The method is based on the reduction of iron (III) to iron (II) by the sulfide and the subsequent complexation of metal ion with nitroso-R salt in alkaline medium. The resulting green-colored complex was extracted through cloud point formation using cationic surfactant, that is, cetylpyridinium chloride, and the obtained surfactant phase was homogenized by ethanol before its absorbance measurement at 710 nm. The reaction variables like metal ion, ligand, surfactant concentration, and medium pH on the cloud point extraction of the metal-ligand complex have been optimized. The interference effect of the common anions and cations was studied. The proposed method has been successfully applied to quantify the trace level sulfide in the leachate samples of the landfill and water samples from bore wells and ponds. The validity of the proposed method has been studied by spiking the samples with known quantities of sulfide as well as comparing with the results obtained by the standard method. PMID:22619597

  10. The CZMIL manual editor (CME): a new tool for analyzing bathymetric lidar waveforms and editing point clouds

    NASA Astrophysics Data System (ADS)

    Morris, Gary Q.; Depner, Jan; Hilderbrand, Ronnie; Ramnath, Vinod

    2010-04-01

    The University of Southern Mississippi's Center of Higher Learning has developed a Waveform Viewer, Attribute Viewer, and a 3D Editor for use in the CZMIL Point Cloud Manual Editor (CME). The Waveform Viewer displays various channels of CZMIL waveforms within the 2D/3D editor interface of CME. This module provides the user an interactive tool set consisting of a cross sectioning mechanism for the intensity time-bin relationship, waveform file output, and zooming capabilities. The Attribute Viewer provides the data analyst with information to analyze various environmental and spatial parameters that might contribute to errors in the measured points. The 3D Editor offers the benefits of capturing depth outliers; an intuitive visual connectivity with the 2D editor; and the implementation of volumetric directional slice isolation of data outliers.

  11. Numerical 3D analysis of cloud cavitation shedding frequency on a circular leading edge hydrofoil with a barotropic cavitation model

    NASA Astrophysics Data System (ADS)

    Blume, M.; Skoda, R.

    2015-12-01

    A compressible density-based time-explicit low Mach number consistent viscous flow solver is utilised in combination with a barotropic cavitation model for the analysis of cloud cavitation on a circular leading edge (CLE) hydrofoil. For 5° angle of attack, cloud structure and shedding frequency for different cavitation numbers are compared to experimental data. A strong grid sensitivity is found in particular for high cavitation numbers. On a fine grid, a very good agreement with validation data is achieved even without explicit turbulence model. The neglect of viscous effects as well as a two-dimensional set-up lead to a less realistic prediction of cloud structures and frequencies. Comparative simulations with the Sauer-Schnerr cavitation model and modified pre-factors of the mass transfer terms underestimate the measured shedding frequency.

  12. Initial Self-Consistent 3D Electron-Cloud Simulations of the LHC Beam with the Code WARP+POSINST

    SciTech Connect

    Vay, J; Furman, M A; Cohen, R H; Friedman, A; Grote, D P

    2005-10-11

    We present initial results for the self-consistent beam-cloud dynamics simulations for a sample LHC beam, using a newly developed set of modeling capability based on a merge [1] of the three-dimensional parallel Particle-In-Cell (PIC) accelerator code WARP [2] and the electron-cloud code POSINST [3]. Although the storage ring model we use as a test bed to contain the beam is much simpler and shorter than the LHC, its lattice elements are realistically modeled, as is the beam and the electron cloud dynamics. The simulated mechanisms for generation and absorption of the electrons at the walls are based on previously validated models available in POSINST [3, 4].

  13. Factors influencing superimposition error of 3D cephalometric landmarks by plane orientation method using 4 reference points: 4 point superimposition error regression model.

    PubMed

    Hwang, Jae Joon; Kim, Kee-Deog; Park, Hyok; Park, Chang Seo; Jeong, Ho-Gul

    2014-01-01

    Superimposition has been used as a method to evaluate the changes of orthodontic or orthopedic treatment in the dental field. With the introduction of cone beam CT (CBCT), evaluating 3 dimensional changes after treatment became possible by superimposition. 4 point plane orientation is one of the simplest ways to achieve superimposition of 3 dimensional images. To find factors influencing superimposition error of cephalometric landmarks by 4 point plane orientation method and to evaluate the reproducibility of cephalometric landmarks for analyzing superimposition error, 20 patients were analyzed who had normal skeletal and occlusal relationship and took CBCT for diagnosis of temporomandibular disorder. The nasion, sella turcica, basion and midpoint between the left and the right most posterior point of the lesser wing of sphenoidal bone were used to define a three-dimensional (3D) anatomical reference co-ordinate system. Another 15 reference cephalometric points were also determined three times in the same image. Reorientation error of each landmark could be explained substantially (23%) by linear regression model, which consists of 3 factors describing position of each landmark towards reference axes and locating error. 4 point plane orientation system may produce an amount of reorientation error that may vary according to the perpendicular distance between the landmark and the x-axis; the reorientation error also increases as the locating error and shift of reference axes viewed from each landmark increases. Therefore, in order to reduce the reorientation error, accuracy of all landmarks including the reference points is important. Construction of the regression model using reference points of greater precision is required for the clinical application of this model.

  14. Factors Influencing Superimposition Error of 3D Cephalometric Landmarks by Plane Orientation Method Using 4 Reference Points: 4 Point Superimposition Error Regression Model

    PubMed Central

    Hwang, Jae Joon; Kim, Kee-Deog; Park, Hyok; Park, Chang Seo; Jeong, Ho-Gul

    2014-01-01

    Superimposition has been used as a method to evaluate the changes of orthodontic or orthopedic treatment in the dental field. With the introduction of cone beam CT (CBCT), evaluating 3 dimensional changes after treatment became possible by superimposition. 4 point plane orientation is one of the simplest ways to achieve superimposition of 3 dimensional images. To find factors influencing superimposition error of cephalometric landmarks by 4 point plane orientation method and to evaluate the reproducibility of cephalometric landmarks for analyzing superimposition error, 20 patients were analyzed who had normal skeletal and occlusal relationship and took CBCT for diagnosis of temporomandibular disorder. The nasion, sella turcica, basion and midpoint between the left and the right most posterior point of the lesser wing of sphenoidal bone were used to define a three-dimensional (3D) anatomical reference co-ordinate system. Another 15 reference cephalometric points were also determined three times in the same image. Reorientation error of each landmark could be explained substantially (23%) by linear regression model, which consists of 3 factors describing position of each landmark towards reference axes and locating error. 4 point plane orientation system may produce an amount of reorientation error that may vary according to the perpendicular distance between the landmark and the x-axis; the reorientation error also increases as the locating error and shift of reference axes viewed from each landmark increases. Therefore, in order to reduce the reorientation error, accuracy of all landmarks including the reference points is important. Construction of the regression model using reference points of greater precision is required for the clinical application of this model. PMID:25372707

  15. Automatic Single Tree Detection in Plantations using UAV-based Photogrammetric Point clouds

    NASA Astrophysics Data System (ADS)

    Kattenborn, T.; Sperlich, M.; Bataua, K.; Koch, B.

    2014-08-01

    For reasons of documentation, management and certification there is a high interest in efficient inventories of palm plantations on the single plant level. Recent developments in unmanned aerial vehicle (UAV) technology facilitate spatial and temporal flexible acquisition of high resolution 3D data. Common single tree detection approaches are based on Very High Resolution (VHR) satellite or Airborne Laser Scanning (ALS) data. However, VHR data is often limited to clouds and does commonly not allow for height measurements. VHR and in particualar ALS data are characterized by high relatively high acquisition costs. Sperlich et al. (2013) already demonstrated the high potential of UAV-based photogrammetric point clouds for single tree detection using pouring algorithms. This approach was adjusted and improved for an application on palm plantation. The 9.4ha test site on Tarawa, Kiribati, comprised densely scattered growing palms, as well as abundant undergrowth and trees. Using a standard consumer grade camera mounted on an octocopter two flight campaigns at 70m and 100m altitude were performed to evaluate the effect Ground Sampling Distance (GSD) and image overlap. To avoid comission errors and improve the terrain interpolation the point clouds were classified based on the geometric characteristics of the classes, i.e. (1) palm, (2) other vegetation (3) and ground. The mapping accuracy amounts for 86.1 % for the entire study area and 98.2 % for dense growing palm stands. We conclude that this flexible and automatic approach has high capabilities for operational use.

  16. a Data Driven Method for Building Reconstruction from LiDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Sajadian, M.; Arefi, H.

    2014-10-01

    Airborne laser scanning, commonly referred to as LiDAR, is a superior technology for three-dimensional data acquisition from Earth's surface with high speed and density. Building reconstruction is one of the main applications of LiDAR system which is considered in this study. For a 3D reconstruction of the buildings, the buildings points should be first separated from the other points such as; ground and vegetation. In this paper, a multi-agent strategy has been proposed for simultaneous extraction and segmentation of buildings from LiDAR point clouds. Height values, number of returned pulse, length of triangles, direction of normal vectors, and area are five criteria which have been utilized in this step. Next, the building edge points are detected using a new method named "Grid Erosion". A RANSAC based technique has been employed for edge line extraction. Regularization constraints are performed to achieve the final lines. Finally, by modelling of the roofs and walls, 3D building model is reconstructed. The results indicate that the proposed method could successfully extract the building from LiDAR data and generate the building models automatically. A qualitative and quantitative assessment of the proposed method is then provided.

  17. Gridless, pattern-driven point cloud completion and extension

    NASA Astrophysics Data System (ADS)

    Gravey, Mathieu; Mariethoz, Gregoire

    2016-04-01

    While satellites offer Earth observation with a wide coverage, other remote sensing techniques such as terrestrial LiDAR can acquire very high-resolution data on an area that is limited in extension and often discontinuous due to shadow effects. Here we propose a numerical approach to merge these two types of information, thereby reconstructing high-resolution data on a continuous large area. It is based on a pattern matching process that completes the areas where only low-resolution data is available, using bootstrapped high-resolution patterns. Currently, the most common approach to pattern matching is to interpolate the point data on a grid. While this approach is computationally efficient, it presents major drawbacks for point clouds processing because a significant part of the information is lost in the point-to-grid resampling, and that a prohibitive amount of memory is needed to store large grids. To address these issues, we propose a gridless method that compares point clouds subsets without the need to use a grid. On-the-fly interpolation involves a heavy computational load, which is met by using a GPU high-optimized implementation and a hierarchical pattern searching strategy. The method is illustrated using data from the Val d'Arolla, Swiss Alps, where high-resolution terrestrial LiDAR data are fused with lower-resolution Landsat and WorldView-3 acquisitions, such that the density of points is homogeneized (data completion) and that it is extend to a larger area (data extension).

  18. Leveraging LIDAR-derived Point Clouds for Topographic Characterization

    NASA Astrophysics Data System (ADS)

    Velasquez, C.; Glenn, N. F.; Ames, D. P.

    2010-12-01

    Velásquez, C. F., Glenn, N. F., Ames, D. P. Acquisition of accurate x, y and z coordinates of terrain information throughout large geographic areas is possible with Light Detection and Ranging (LiDAR) technology. The mass of points (or point cloud) remotely acquired with airborne LiDAR sensors, for instance, are used for the creation of quality digital models of terrain. Extraction of hydrological features such as channel networks from digital terrain models is a common task. This task is generally conducted by means of implementing software tools that analyze raster structures for representation of terrain surfaces. Exploring the feasibility of directly and effectively extracting land cover features from a point cloud deserves due attention given both the proliferation of LiDAR data availability and the potential for the information to overcome some disadvantages that are intrinsic to the raster structure. Pursuing an alternative mode to leveraging LiDAR data for hydrologic applications is, therefore, the subject motivating the current research. A methodology for extraction of concave-shaped terrain surfaces is proposed. These features, relevant to hydrological applications, are extracted by means of implementing a convolution operation that is conducted on the x, y, and z coordinates of the point cloud and with an adapted construct for a zero-sum kernel function. The mass of points that are topographically lower of what is referred to as a zero-crossing level appear to reasonably well delineate concave-shaped landforms. The proposed method is, therefore, of merit to further investigation.

  19. Studies of 3D-cloud optical depth from small to very large values, and of the radiation and remote sensing impacts of larger-drop clustering

    SciTech Connect

    Wiscombe, Warren; Marshak, Alexander; Knyazikhin, Yuri; Chiu, Christine

    2007-05-04

    We have basically completed all the goals stated in the previous proposal and published or submitted journal papers thereon, the only exception being First-Principles Monte Carlo which has taken more time than expected. We finally finished the comprehensive book on 3D cloud radiative transfer (edited by Marshak and Davis and published by Springer), with many contributions by ARM scientists; this book was highlighted in the 2005 ARM Annual Report. We have also completed (for now) our pioneering work on new models of cloud drop clustering based on ARM aircraft FSSP data, with applications both to radiative transfer and to rainfall. This clustering work was highlighted in the FY07 “Our Changing Planet” (annual report of the US Climate Change Science Program). Our group published 22 papers, one book, and 5 chapters in that book, during this proposal period. All are listed at the end of this section. Below, we give brief highlights of some of those papers.

  20. Simulation estimates of cloud points of polydisperse fluids.

    PubMed

    Buzzacchi, Matteo; Sollich, Peter; Wilding, Nigel B; Müller, Marcus

    2006-04-01

    We describe two distinct approaches to obtaining the cloud-point densities and coexistence properties of polydisperse fluid mixtures by Monte Carlo simulation within the grand-canonical ensemble. The first method determines the chemical potential distribution mu(sigma) (with the polydisperse attribute) under the constraint that the ensemble average of the particle density distribution rho(sigma) match a prescribed parent form. Within the region of phase coexistence (delineated by the cloud curve) this leads to a distribution of the fluctuating overall particle density n, p(n), that necessarily has unequal peak weights in order to satisfy a generalized lever rule. A theoretical analysis shows that as a consequence, finite-size corrections to estimates of coexistence properties are power laws in the system size. The second method assigns mu(sigma) such that an equal-peak-weight criterion is satisfied for p(n) for all points within the coexistence region. However, since equal volumes of the coexisting phases cannot satisfy the lever rule for the prescribed parent, their relative contributions must be weighted appropriately when determining mu(sigma). We show how to ascertain the requisite weight factor operationally. A theoretical analysis of the second method suggests that it leads to finite-size corrections to estimates of coexistence properties which are exponentially small in the system size. The scaling predictions for both methods are tested via Monte Carlo simulations of a polydisperse lattice-gas model near its cloud curve, the results showing excellent quantitative agreement with the theory.

  1. Reconstruction of measurable three-dimensional point cloud model based on large-scene archaeological excavation sites

    NASA Astrophysics Data System (ADS)

    Zhang, Chun-Sen; Zhang, Meng-Meng; Zhang, Wei-Xing

    2017-01-01

    This paper outlines a low-cost, user-friendly photogrammetric technique with nonmetric cameras to obtain excavation site digital sequence images, based on photogrammetry and computer vision. Digital camera calibration, automatic aerial triangulation, image feature extraction, image sequence matching, and dense digital differential rectification are used, combined with a certain number of global control points of the excavation site, to reconstruct the high precision of measured three-dimensional (3-D) models. Using the acrobatic figurines in the Qin Shi Huang mausoleum excavation as an example, our method solves the problems of little base-to-height ratio, high inclination, unstable altitudes, and significant ground elevation changes affecting image matching. Compared to 3-D laser scanning, the 3-D color point cloud obtained by this method can maintain the same visual result and has advantages of low project cost, simple data processing, and high accuracy. Structure-from-motion (SfM) is often used to reconstruct 3-D models of large scenes and has lower accuracy if it is a reconstructed 3-D model of a small scene at close range. Results indicate that this method quickly achieves 3-D reconstruction of large archaeological sites and produces heritage site distribution of orthophotos providing a scientific basis for accurate location of cultural relics, archaeological excavations, investigation, and site protection planning. This proposed method has a comprehensive application value.

  2. Exploring point-cloud features from partial body views for gender classification

    NASA Astrophysics Data System (ADS)

    Fouts, Aaron; McCoppin, Ryan; Rizki, Mateen; Tamburino, Louis; Mendoza-Schrock, Olga

    2012-06-01

    In this paper we extend a previous exploration of histogram features extracted from 3D point cloud images of human subjects for gender discrimination. Feature extraction used a collection of concentric cylinders to define volumes for counting 3D points. The histogram features are characterized by a rotational axis and a selected set of volumes derived from the concentric cylinders. The point cloud images are drawn from the CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Success from our previous investigation was based on extracting features from full body coverage which required integration of multiple camera images. With the full body coverage, the central vertical body axis and orientation are readily obtainable; however, this is not the case with a one camera view providing less than one half body coverage. Assuming that the subjects are upright, we need to determine or estimate the position of the vertical axis and the orientation of the body about this axis relative to the camera. In past experiments the vertical axis was located through the center of mass of torso points projected on the ground plane and the body orientation derived using principle component analysis. In a natural extension of our previous work to partial body views, the absence of rotational invariance about the cylindrical axis greatly increases the difficulty for gender classification. Even the problem of estimating the axis is no longer simple. We describe some simple feasibility experiments that use partial image histograms. Here, the cylindrical axis is assumed to be known. We also discuss experiments with full body images that explore the sensitivity of classification accuracy relative to displacements of the cylindrical axis. Our initial results provide the basis for further

  3. Looking for shapes in two-dimensional cluttered point clouds.

    PubMed

    Srivastava, Anuj; Jermyn, Ian H

    2009-09-01

    We study the problem of identifying shape classes in point clouds. These clouds contain sampled points along contours and are corrupted by clutter and observation noise. Taking an analysis-by-synthesis approach, we simulate high-probability configurations of sampled contours using models learned from training data to evaluate the given test data. To facilitate simulations, we develop statistical models for sources of (nuisance) variability: 1) shape variations within classes, 2) variability in sampling continuous curves, 3) pose and scale variability, 4) observation noise, and 5) points introduced by clutter. The variability in sampling closed curves into finite points is represented by positive diffeomorphisms of a unit circle. We derive probability models on these functions using their square-root forms and the Fisher-Rao metric. Using a Monte Carlo approach, we simulate configurations from a joint prior on the shape-sample space and compare them to the data using a likelihood function. Average likelihoods of simulated configurations lead to estimates of posterior probabilities of different classes and, hence, Bayesian classification.

  4. An OpenGL-based Interface to 3D PowerPoint-like Presentations of OpenGL Projects

    NASA Astrophysics Data System (ADS)

    Mokhov, Serguei A.; Song, Miao

    We present a multimedia 3D interface to powerpoint-like presentations in OpenGL. The presentations of such kind are useful to demonstrate projects or conference talks with the demonstration results of a 3D animation, effects, and others alongside the presentation 'in situ' instead of switching between a regular presentation software to the demo and back - the demo and the presentation can be one and the same, embedded together.

  5. Integration of Point Clouds Dataset from Different Sensors

    NASA Astrophysics Data System (ADS)

    Abdullah, C. K. A. F. Che Ku; Baharuddin, N. Z. S.; Ariff, M. F. M.; Majid, Z.; Lau, C. L.; Yusoff, A. R.; Idris, K. M.; Aspuri, A.

    2017-02-01

    Laser Scanner technology become an option in the process of collecting data nowadays. It is composed of Airborne Laser Scanner (ALS) and Terrestrial Laser Scanner (TLS). ALS like Phoenix AL3-32 can provide accurate information from the viewpoint of rooftop while TLS as Leica C10 can provide complete data for building facade. However if both are integrated, it is able to produce more accurate data. The focus of this study is to integrate both types of data acquisition of ALS and TLS and determine the accuracy of the data obtained. The final results acquired will be used to generate models of three-dimensional (3D) buildings. The scope of this study is focusing on data acquisition of UTM Eco-home through laser scanning methods such as ALS which scanning on the roof and the TLS which scanning on building façade. Both device is used to ensure that no part of the building that are not scanned. In data integration process, both are registered by the selected points among the manmade features which are clearly visible in Cyclone 7.3 software. The accuracy of integrated data is determined based on the accuracy assessment which is carried out using man-made registration methods. The result of integration process can achieve below 0.04m. This integrated data then are used to generate a 3D model of UTM Eco-home building using SketchUp software. In conclusion, the combination of the data acquisition integration between ALS and TLS would produce the accurate integrated data and able to use for generate a 3D model of UTM eco-home. For visualization purposes, the 3D building model which generated is prepared in Level of Detail 3 (LOD3) which recommended by City Geographic Mark-Up Language (CityGML).

  6. a Robust Registration Algorithm for Point Clouds from Uav Images for Change Detection

    NASA Astrophysics Data System (ADS)

    Al-Rawabdeh, A.; Al-Gurrani, H.; Al-Durgham, K.; Detchev, I.; He, F.; El-Sheimy, N.; Habib, A.

    2016-06-01

    Landslides are among the major threats to urban landscape and manmade infrastructure. They often cause economic losses, property damages, and loss of lives. Temporal monitoring data of landslides from different epochs empowers the evaluation of landslide progression. Alignment of overlapping surfaces from two or more epochs is crucial for the proper analysis of landslide dynamics. The traditional methods for point-cloud-based landslide monitoring rely on using a variation of the Iterative Closest Point (ICP) registration procedure to align any reconstructed surfaces from different epochs to a common reference frame. However, sometimes the ICP-based registration can fail or may not provide sufficient accuracy. For example, point clouds from different epochs might fit to local minima due to lack of geometrical variability within the data. Also, manual interaction is required to exclude any non-stable areas from the registration process. In this paper, a robust image-based registration method is introduced for the simultaneous evaluation of all registration parameters. This includes the Interior Orientation Parameters (IOPs) of the camera and the Exterior Orientation Parameters (EOPs) of the involved images from all available observation epochs via a bundle block adjustment with self-calibration. Next, a semi-global dense matching technique is implemented to generate dense 3D point clouds for each epoch using the images captured in a particular epoch separately. The normal distances between any two consecutive point clouds can then be readily computed, because the point clouds are already effectively co-registered. A low-cost DJI Phantom II Unmanned Aerial Vehicle (UAV) was customised and used in this research for temporal data collection over an active soil creep area in Lethbridge, Alberta, Canada. The customisation included adding a GPS logger and a Large-Field-Of-View (LFOV) action camera which facilitated capturing high-resolution geo-tagged images in two epochs

  7. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

    PubMed

    Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

    2017-03-15

    Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

  8. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

    PubMed Central

    Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

    2017-01-01

    Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood. PMID:28294963

  9. Graph-based segmentation of airborne lidar point clouds

    NASA Astrophysics Data System (ADS)

    Vilariño, David L.; Martínez, Jorge; Rivera, Francisco F.; Cabaleiro, José C.; Pena, Tomás. F.

    2016-10-01

    In this paper, a graph-based technique originally intended for image processing has been tailored for the segmentation of airborne LiDAR points, that are irregularly distributed. Every LiDAR point is labeled as a node and interconnected as a graph extended to its neighborhood and defined in a 4D feature space (x, y, z, and the reflection intensity). The interconnections between pairs of neighboring nodes are weighted based on the distance in the feature space. The segmentation consists in an iterative process of classification of nodes into homogeneous groups based on their similarity. This approach is intended to be part of a complete system for classification of structures from LiDAR point clouds in applications needing fast response times. In this sense, a study of the performance/accuracy trade-off has been performed, extracting some conclusions about the benefits of the proposed solution.

  10. Density of point clouds in mobile laser scanning. (Polish Title: Gestosc chmury punktow pochodzacej z mobilnego skanowania laserowego)

    NASA Astrophysics Data System (ADS)

    Warchoł, A.

    2015-12-01

    The LiDAR (Light Detection And Ranging) technology is becoming a more and more popular method to collect spatial information. The acquisition of 3D data by means of one or several laser scanners mounted on a mobile platform (car) could quickly provide large volumes of dense data with centimeter-level accuracy. This is, therefore, the ideal solution to obtain information about objects with elongated shapes (corridors), and their surroundings. Point clouds used by specific applications must fulfill certain quality criteria, such as quantitative and qualitative indicators (i.e. precision, accuracy, density, completeness).Usually, the client fixes some parameter values that must be achieved. In terms of the precision, this parameter is well described, whereas in the case of density point clouds the discussion is still open. Due to the specificities of the MLS (Mobile Laser Scanning), the solution from ALS (Airborne Laser Scanning) cannot be directly applied. Hence, the density of the final point clouds, calculated as the number of points divided by "flat" surface area, is inappropriate. We present in this article three different ways of determining and interpreting point cloud density on three different test fields. The first method divides the number of points by the "flat" area, the second by the "three-dimensional" area, and the last one refers to a voxel approach. The most reliable method seems to be the voxel method, which in addition to the local density values also presents their spatial distribution.

  11. Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification.

    PubMed

    Rutzinger, Martin; Höfle, Bernhard; Hollaus, Markus; Pfeifer, Norbert

    2008-08-04

    Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of points. Higher echo densities (> 20 echoes/m2) and additional classification variables from full-waveform (FWF) ALS data, namely echo amplitude, echo width and information on multiple echoes from one shot, offer new possibilities in classifying the ALS point cloud. Currently FWF sensor information is hardly used for classification purposes. This contribution presents an object-based point cloud analysis (OBPA) approach, combining segmentation and classification of the 3D FWF ALS points designed to detect tall vegetation in urban environments. The definition tall vegetation includes trees and shrubs, but excludes grassland and herbage. In the applied procedure FWF ALS echoes are segmented by a seeded region growing procedure. All echoes sorted descending by their surface roughness are used as seed points. Segments are grown based on echo width homogeneity. Next, segment statistics (mean, standard deviation, and coefficient of variation) are calculated by aggregating echo features such as amplitude and surface roughness. For classification a rule base is derived automatically from a training area using a statistical classification tree. To demonstrate our method we present data of three sites with around 500,000 echoes each. The accuracy of the classified vegetation segments is evaluated for two independent validation sites. In a point-wise error assessment, where the classification is compared with manually classified 3D points, completeness and correctness better than 90% are reached for the validation sites. In comparison to many other algorithms the proposed 3D point classification works on the original measurements

  12. Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification

    PubMed Central

    Rutzinger, Martin; Höfle, Bernhard; Hollaus, Markus; Pfeifer, Norbert

    2008-01-01

    Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of points. Higher echo densities (>20 echoes/m2) and additional classification variables from full-waveform (FWF) ALS data, namely echo amplitude, echo width and information on multiple echoes from one shot, offer new possibilities in classifying the ALS point cloud. Currently FWF sensor information is hardly used for classification purposes. This contribution presents an object-based point cloud analysis (OBPA) approach, combining segmentation and classification of the 3D FWF ALS points designed to detect tall vegetation in urban environments. The definition tall vegetation includes trees and shrubs, but excludes grassland and herbage. In the applied procedure FWF ALS echoes are segmented by a seeded region growing procedure. All echoes sorted descending by their surface roughness are used as seed points. Segments are grown based on echo width homogeneity. Next, segment statistics (mean, standard deviation, and coefficient of variation) are calculated by aggregating echo features such as amplitude and surface roughness. For classification a rule base is derived automatically from a training area using a statistical classification tree. To demonstrate our method we present data of three sites with around 500,000 echoes each. The accuracy of the classified vegetation segments is evaluated for two independent validation sites. In a point-wise error assessment, where the classification is compared with manually classified 3D points, completeness and correctness better than 90% are reached for the validation sites. In comparison to many other algorithms the proposed 3D point classification works on the original measurements

  13. A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds

    PubMed Central

    Poreba, Martyna; Goulette, François

    2015-01-01

    With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-based method to perform accurate registration of point clouds, either in 2D or 3D. A two-step fast algorithm called Robust Line Matching and Registration (RLMR), which combines coarse and fine registration, was developed. The initial estimate is found from a triplet of conjugate line pairs, selected by a RANSAC algorithm. Then, this transformation is refined using an iterative optimization algorithm. Conjugates of linear features are identified with respect to a similarity metric representing a line-to-line distance. The efficiency and robustness to noise of the proposed method are evaluated and discussed. The algorithm is valid and ensures valuable results when pre-aligned point clouds with the same scale are used. The studies show that the matching accuracy is at least 99.5%. The transformation parameters are also estimated correctly. The error in rotation is better than 2.8% full scale, while the translation error is less than 12.7%. PMID:25594589

  14. A robust linear feature-based procedure for automated registration of point clouds.

    PubMed

    Poreba, Martyna; Goulette, François

    2015-01-14

    With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-based method to perform accurate registration of point clouds, either in 2D or 3D. A two-step fast algorithm called Robust Line Matching and Registration (RLMR), which combines coarse and fine registration, was developed. The initial estimate is found from a triplet of conjugate line pairs, selected by a RANSAC algorithm. Then, this transformation is refined using an iterative optimization algorithm. Conjugates of linear features are identified with respect to a similarity metric representing a line-to-line distance. The efficiency and robustness to noise of the proposed method are evaluated and discussed. The algorithm is valid and ensures valuable results when pre-aligned point clouds with the same scale are used. The studies show that the matching accuracy is at least 99.5%. The transformation parameters are also estimated correctly. The error in rotation is better than 2.8% full scale, while the translation error is less than 12.7%.

  15. Visualizing nonmanifold and singular implicit surfaces with point clouds.

    PubMed

    Balsys, Ron J; Harbinson, Dirk J; Suffern, Kevin G

    2012-02-01

    We use octree spatial subdivision to generate point clouds on complex nonmanifold implicit surfaces in order to visualize them. The new spatial subdivision scheme only uses point sampling and an interval exclusion test. The algorithm includes a test for pruning the resulting plotting nodes so that only points in the closest nodes to the surface are used in rendering. This algorithm results in improved image quality compared to the naive use of intervals or affine arithmetic when rendering implicit surfaces, particularly in regions of high curvature. We discuss and compare CPU and GPU versions of the algorithm. We can now render nonmanifold features such as rays, ray-like tubes, cusps, ridges, thin sections that are at arbitrary angles to the octree node edges, and singular points located within plot nodes, all without artifacts. Our previous algorithm could not render these without severe aliasing. The algorithm can render the self-intersection curves of implicit surfaces by exploiting the fact that surfaces are singular where they self-intersect. It can also render the intersection curves of two implicit surfaces. We present new image space and object space algorithms for rendering these intersection curves as contours on one of the surfaces. These algorithms are better at rendering high curvature contours than our previous algorithms. To demonstrate the robustness of the node pruning algorithm we render a number of complex implicit surfaces such as high order polynomial surfaces and Gaussian curvature surfaces. We also compare the algorithm with ray casting interms of speed and image quality. For the surfaces presented here, the point clouds can be computed in seconds to minutes on atypical Intel based PC. Once this is done, the surfaces can be rendered at much higher frame rates to allow some degree of interactive visualization.

  16. Automated feature extraction for 3-dimensional point clouds

    NASA Astrophysics Data System (ADS)

    Magruder, Lori A.; Leigh, Holly W.; Soderlund, Alexander; Clymer, Bradley; Baer, Jessica; Neuenschwander, Amy L.

    2016-05-01

    Light detection and ranging (LIDAR) technology offers the capability to rapidly capture high-resolution, 3-dimensional surface data with centimeter-level accuracy for a large variety of applications. Due to the foliage-penetrating properties of LIDAR systems, these geospatial data sets can detect ground surfaces beneath trees, enabling the production of highfidelity bare earth elevation models. Precise characterization of the ground surface allows for identification of terrain and non-terrain points within the point cloud, and facilitates further discernment between natural and man-made objects based solely on structural aspects and relative neighboring parameterizations. A framework is presented here for automated extraction of natural and man-made features that does not rely on coincident ortho-imagery or point RGB attributes. The TEXAS (Terrain EXtraction And Segmentation) algorithm is used first to generate a bare earth surface from a lidar survey, which is then used to classify points as terrain or non-terrain. Further classifications are assigned at the point level by leveraging local spatial information. Similarly classed points are then clustered together into regions to identify individual features. Descriptions of the spatial attributes of each region are generated, resulting in the identification of individual tree locations, forest extents, building footprints, and 3-dimensional building shapes, among others. Results of the fully-automated feature extraction algorithm are then compared to ground truth to assess completeness and accuracy of the methodology.

  17. Hierarchical Higher Order Crf for the Classification of Airborne LIDAR Point Clouds in Urban Areas

    NASA Astrophysics Data System (ADS)

    Niemeyer, J.; Rottensteiner, F.; Soergel, U.; Heipke, C.

    2016-06-01

    We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

  18. Hierarchical object parsing from structured noisy point clouds.

    PubMed

    Barbu, Adrian

    2013-07-01

    Object parsing and segmentation from point clouds are challenging tasks because the relevant data is available only as thin structures along object boundaries or other features, and is corrupted by large amounts of noise. To handle this kind of data, flexible shape models are desired that can accurately follow the object boundaries. Popular models such as active shape and active appearance models (AAMs) lack the necessary flexibility for this task, while recent approaches such as the recursive compositional models make model simplifications to obtain computational guarantees. This paper investigates a hierarchical Bayesian model of shape and appearance in a generative setting. The input data is explained by an object parsing layer which is a deformation of a hidden principal component analysis (PCA) shape model with Gaussian prior. The paper also introduces a novel efficient inference algorithm that uses informed data-driven proposals to initialize local searches for the hidden variables. Applied to the problem of object parsing from structured point clouds such as edge detection images, the proposed approach obtains state-of-the-art parsing errors on two standard datasets without using any intensity information.

  19. 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

  20. Towards Object Driven Floor Plan Extraction from Laser Point Cloud

    NASA Astrophysics Data System (ADS)

    Babacan, K.; Jung, J.; Wichmann, A.; Jahromi, B. A.; Shahbazi, M.; Sohn, G.; Kada, M.

    2016-06-01

    During the last years, the demand for indoor models has increased for various purposes. As a provisional step to proceed towards higher dimensional indoor models, powerful and flexible floor plans can be utilised. Therefore, several methods have been proposed that provide automatically generated floor plans from laser point clouds. The prevailing methodology seeks to attain semantic enhancement of a model (e.g. the identification and labelling of its components) built upon already reconstructed (a priori) geometry. In contrast, this paper demonstrates preliminary research on the possibility to directly incorporate semantic knowledge, which is itself derived from the raw data during the extraction, into the geometric modelling process. In this regard, we propose a new method to automatically extract floor plans from raw point clouds. It is based on a hierarchical space partitioning of the data, integrated with primitive selection actuated by object detection. First, planar primitives corresponding to vertical architectural structures are extracted using M-estimator SAmple and Consensus (MSAC). The set of the resulting line segments are refined by a selection process through a novel door detection algorithm, considering optimization of prior information and fitness to the data. The selected lines are used as hyperlines to partition the space into enclosed areas. Finally, a floor plan is extracted from these partitions by Minimum Description Length (MDL) hypothesis ranking. The algorithm is applied on a real mobile laser scanner dataset and the results are evaluated both in terms of door detection and consecutive floor plan extraction.

  1. Bayesian Multiscale Modeling of Closed Curves in Point Clouds.

    PubMed

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

    2014-10-01

    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.

  2. Voronoi poles-based saliency feature detection from point clouds

    NASA Astrophysics Data System (ADS)

    Xu, Tingting; Wei, Ning; Dong, Fangmin; Yang, Yuanqin

    2016-12-01

    In this paper, we represent a novel algorithm for point cloud feature detection. Firstly, the algorithm estimates the local feature for each sample point by computing the ratio of the distance from the inner voronoi pole and the outer voronoi pole to the surface. Then the surface global saliency feature is detected by adding the results of the difference of Gaussian for local feature under different scales. Compared with the state of the art methods, our algorithm has higher computing efficiency and more accurate feature detection for sharp edge. The detected saliency features are applied as the weights for surface mesh simplification. The numerical results for mesh simplification show that our method keeps the more details of key features than the traditional methods.

  3. Simple computation of reaction-diffusion processes on point clouds.

    PubMed

    Macdonald, Colin B; Merriman, Barry; Ruuth, Steven J

    2013-06-04

    The study of reaction-diffusion processes is much more complicated on general curved surfaces than on standard Cartesian coordinate spaces. Here we show how to formulate and solve systems of reaction-diffusion equations on surfaces in an extremely simple way, using only the standard Cartesian form of differential operators, and a discrete unorganized point set to represent the surface. Our method decouples surface geometry from the underlying differential operators. As a consequence, it becomes possible to formulate and solve rather general reaction-diffusion equations on general surfaces without having to consider the complexities of differential geometry or sophisticated numerical analysis. To illustrate the generality of the method, computations for surface diffusion, pattern formation, excitable media, and bulk-surface coupling are provided for a variety of complex point cloud surfaces.

  4. Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

    Automated and efficient algorithms to perform segmentation of terrestrial LiDAR data is critical for exploitation of 3D point clouds, where the ultimate goal is CAD modeling of the segmented data. In this work, a novel segmentation technique is proposed, starting with octree decomposition to recursively divide the scene into octants or voxels, followed by a novel split and merge framework that uses graph theory and a series of connectivity analyses to intelligently merge components into larger connected components. The connectivity analysis, based on a combination of proximity, orientation, and curvature connectivity criteria, is designed for the segmentation of pipes, vessels, and walls from terrestrial LiDAR data of piping systems at industrial sites, such as oil refineries, chemical plants, and steel mills. The proposed segmentation method is exercised on two terrestrial LiDAR datasets of a steel mill and a chemical plant, demonstrating its ability to correctly reassemble and segregate features of interest.

  5. Evaluating the Potential of Imaging Rover for Automatic Point Cloud Generation

    NASA Astrophysics Data System (ADS)

    Cera, V.; Campi, M.

    2017-02-01

    The paper presents a phase of an on-going interdisciplinary research concerning the medieval site of Casertavecchia (Italy). The project aims to develop a multi-technique approach for the semantic - enriched 3D modeling starting from the automatic acquisition of several data. In particular, the paper reports the results of the first stage about the Cathedral square of the medieval village. The work is focused on evaluating the potential of an imaging rover for automatic point cloud generation. Each of survey techniques has its own advantages and disadvantages so the ideal approach is an integrated methodology in order to maximize single instrument performance. The experimentation was conducted on the Cathedral square of the ancient site of Casertavecchia, in Campania, Italy.

  6. D Surveying and Geometric Assessment of a Gothic Nave Vaulting from Point Clouds

    NASA Astrophysics Data System (ADS)

    Costa-Jover, A.; Ginovart, J. Lluis i.; Coll-Pla, S.; López Piquer, M.; Samper-Sosa, A.; Moreno García, D.; Solís Lorenzo, A. M.

    2017-02-01

    The development of massive data captures techniques (MDC) in recent years, such as the Terrestrial laser Scanner (TLS), raises the possibility of developing new assessment procedures for architectural heritage. The 3D models that it is able to obtain is a great potential tool, both for conservation purposes and for historical and architectural studies. The paper proposes a simple, non-invasive methodology for the assessment of masonry vaults from point clouds which makes it possible to obtain relevant data about the formal anomalies. The methodology is tested in Tortosa's Gothic Cathedral's vaults, where the geometrical differences between vaults, a priori equal, are identified and related with the partially known construction phases. The procedure can be easily used on any other vaulted construction of any kind, but is especially useful to deal with the complex geometry of Gothic masonry vaults.

  7. Automatic method for building indoor boundary models from dense point clouds collected by laser scanners.

    PubMed

    Valero, Enrique; Adán, Antonio; Cerrada, Carlos

    2012-11-22

    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.

  8. Visualization of Buffer Capacity with 3-D "Topo" Surfaces: Buffer Ridges, Equivalence Point Canyons and Dilution Ramps

    ERIC Educational Resources Information Center

    Smith, Garon C.; Hossain, Md Mainul

    2016-01-01

    BufCap TOPOS is free software that generates 3-D topographical surfaces ("topos") for acid-base equilibrium studies. It portrays pH and buffer capacity behavior during titration and dilution procedures. Topo surfaces are created by plotting computed pH and buffer capacity values above a composition grid with volume of NaOH as the x axis…

  9. 3-D Surface Visualization of pH Titration "Topos": Equivalence Point Cliffs, Dilution Ramps, and Buffer Plateaus

    ERIC Educational Resources Information Center

    Smith, Garon C.; Hossain, Md Mainul; MacCarthy, Patrick

    2014-01-01

    3-D topographic surfaces ("topos") can be generated to visualize how pH behaves during titration and dilution procedures. The surfaces are constructed by plotting computed pH values above a composition grid with volume of base added in one direction and overall system dilution on the other. What emerge are surface features that…

  10. 3D change detection - Approaches and applications

    NASA Astrophysics Data System (ADS)

    Qin, Rongjun; Tian, Jiaojiao; Reinartz, Peter

    2016-12-01

    Due to the unprecedented technology development of sensors, platforms and algorithms for 3D data acquisition and generation, 3D spaceborne, airborne and close-range data, in the form of image based, Light Detection and Ranging (LiDAR) based point clouds, Digital Elevation Models (DEM) and 3D city models, become more accessible than ever before. Change detection (CD) or time-series data analysis in 3D has gained great attention due to its capability of providing volumetric dynamics to facilitate more applications and provide more accurate results. The state-of-the-art CD reviews aim to provide a comprehensive synthesis and to simplify the taxonomy of the traditional remote sensing CD techniques, which mainly sit within the boundary of 2D image/spectrum analysis, largely ignoring the particularities of 3D aspects of the data. The inclusion of 3D data for change detection (termed 3D CD), not only provides a source with different modality for analysis, but also transcends the border of traditional top-view 2D pixel/object-based analysis to highly detailed, oblique view or voxel-based geometric analysis. This paper reviews the recent developments and applications of 3D CD using remote sensing and close-range data, in support of both academia and industry researchers who seek for solutions in detecting and analyzing 3D dynamics of various objects of interest. We first describe the general considerations of 3D CD problems in different processing stages and identify CD types based on the information used, being the geometric comparison and geometric-spectral analysis. We then summarize relevant works and practices in urban, environment, ecology and civil applications, etc. Given the broad spectrum of applications and different types of 3D data, we discuss important issues in 3D CD methods. Finally, we present concluding remarks in algorithmic aspects of 3D CD.

  11. Sharing Clouds: Showing, Distributing, and Sharing Large Point Datasets

    NASA Astrophysics Data System (ADS)

    Grigsby, S.

    2012-12-01

    Sharing large data sets with colleagues and the general public presents a unique technological challenge for scientists. In addition to large data volumes, there are significant challenges in representing data that is often irregular, multidimensional and spatial in nature. For derived data products, additional challenges exist in displaying and providing provenance data. For this presentation, several open source technologies are demonstrated for the remote display and access of large irregular point data sets. These technologies and techniques include the remote viewing of point data using HTML5 and OpenGL, which provides a highly accessible preview of the data sets for a range of audiences. Intermediate levels of accessibility and high levels of interactivity are accomplished with technologies such as wevDAV, which allows collaborators to run analysis on local clients, using data stored and administered on remote servers. Remote processing and analysis, including provenance tracking, will be discussed at the workgroup level. The data sets used for this presentation include data acquired from the NSF funded National Center for Airborne Laser Mapping (NCALM), and data acquired for research and instructional use in NASA's Student Airborne Research Program (SARP). These datasets include Light Ranging And Detection (LiDAR) point clouds ranging in size from several hundred thousand to several hundred million data points; the techniques and technologies discussed are applicable to other forms of irregular point data.

  12. Post-Earthquake Geology in the ERA of Ubiquitous Point Clouds

    NASA Astrophysics Data System (ADS)

    Oskin, M. E.; Arrowsmith, R.; Nissen, E.; Morelan, A. E., III; Trexler, C. C.; Gold, P. O.; Elliott, A. J.; Crosby, C. J.; Kellogg, L. H.

    2015-12-01

    High-precision 3D imaging with lidar and structure-from-motion photogrammetry is revolutionizing the collection of post-earthquake displacement information. Massive point-cloud datasets, and their differences epoch to epoch, provide valuable information for scientific, engineering, and emergency response, and also pose challenges to process, handle, analyze, share, and visualize. In the physical world, earthquake surface ruptures and secondary deformation features are ephemeral, subject to natural degradation by erosion, or to repair of the built environment. Post-earthquake 3D imaging overcomes this limitation by virtually archiving the primary surface expression of deformation. This allows geologists make precise, repeatable measurements, and to assess subtle, distributed deformation often missed by traditional field methods. Generally, the more local and inexpensive the technique, the quicker that a response can be organized: ground and drone-based SfM (hours), terrestrial laser scanning (days), to airborne lidar (weeks). With the growth of high-resolution topography along fault zones and for other mapping purposes, it is increasingly likely that a large earthquake will coincide with an existing data set. Such an event beholds the exciting promise of point cloud differencing to develop a high-resolution, fully three dimensional displacement and rotation field. Existing paired airborne lidar data sets from Japan, New Zealand, Mexico, and California reveal new and informative features of earthquake-induced near-field deformation, but also illustrate that significant challenges impede the separation a tectonic signal from noise and uncertainty within lidar data. In a future earthquakes, there will be great opportunities, and soon enough, an imperative, to measure deformation at sub-meter resolution over entire cities, and along faults hundreds of kilometers in length. As a community, we stand at a threshold, watching this oncoming deluge of repeat and ubiquitous

  13. Successful gas hydrate prospecting using 3D seismic - A case study for the Mt. Elbert prospect, Milne Point, North Slope Alaska

    USGS Publications Warehouse

    Inks, T.L.; Agena, W.F.

    2008-01-01

    In February 2007, the Mt. Elbert Prospect stratigraphic test well, Milne Point, North Slope Alaska encountered thick methane gas hydrate intervals, as predicted by 3D seismic interpretation and modeling. Methane gas hydrate-saturated sediment was found in two intervals, totaling more than 100 ft., identified and mapped based on seismic character and wavelet modeling.

  14. Detection of Slope Movement by Comparing Point Clouds Created by SFM Software

    NASA Astrophysics Data System (ADS)

    Oda, Kazuo; Hattori, Satoko; Takayama, Toko

    2016-06-01

    This paper proposes movement detection method between point clouds created by SFM software, without setting any onsite georeferenced points. SfM software, like Smart3DCaputure, PhotoScan, and Pix4D, are convenient for non-professional operator of photogrammetry, because these systems require simply specification of sequence of photos and output point clouds with colour index which corresponds to the colour of original image pixel where the point is projected. SfM software can execute aerial triangulation and create dense point clouds fully automatically. This is useful when monitoring motion of unstable slopes, or loos rocks in slopes along roads or railroads. Most of existing method, however, uses mesh-based DSM for comparing point clouds before/after movement and it cannot be applied in such cases that part of slopes forms overhangs. And in some cases movement is smaller than precision of ground control points and registering two point clouds with GCP is not appropriate. Change detection method in this paper adopts CCICP (Classification and Combined ICP) algorithm for registering point clouds before / after movement. The CCICP algorithm is a type of ICP (Iterative Closest Points) which minimizes point-to-plane, and point-to-point distances, simultaneously, and also reject incorrect correspondences based on point classification by PCA (Principle Component Analysis). Precision test shows that CCICP method can register two point clouds up to the 1 pixel size order in original images. Ground control points set in site are useful for initial setting of two point clouds. If there are no GCPs in site of slopes, initial setting is achieved by measuring feature points as ground control points in the point clouds before movement, and creating point clouds after movement with these ground control points. When the motion is rigid transformation, in case that a loose Rock is moving in slope, motion including rotation can be analysed by executing CCICP for a loose rock and

  15. Joint detection of anatomical points on surface meshes and color images for visual registration of 3D dental models

    NASA Astrophysics Data System (ADS)

    Destrez, Raphaël.; Albouy-Kissi, Benjamin; Treuillet, Sylvie; Lucas, Yves

    2015-04-01

    Computer aided planning for orthodontic treatment requires knowing occlusion of separately scanned dental casts. A visual guided registration is conducted starting by extracting corresponding features in both photographs and 3D scans. To achieve this, dental neck and occlusion surface are firstly extracted by image segmentation and 3D curvature analysis. Then, an iterative registration process is conducted during which feature positions are refined, guided by previously found anatomic edges. The occlusal edge image detection is improved by an original algorithm which follows Canny's poorly detected edges using a priori knowledge of tooth shapes. Finally, the influence of feature extraction and position optimization is evaluated in terms of the quality of the induced registration. Best combination of feature detection and optimization leads to a positioning average error of 1.10 mm and 2.03°.

  16. 3D surface reconstruction based on image stitching from gastric endoscopic video sequence

    NASA Astrophysics Data System (ADS)

    Duan, Mengyao; Xu, Rong; Ohya, Jun

    2013-09-01

    This paper proposes a method for reconstructing 3D detailed structures of internal organs such as gastric wall from endoscopic video sequences. The proposed method consists of the four major steps: Feature-point-based 3D reconstruction, 3D point cloud stitching, dense point cloud creation and Poisson surface reconstruction. Before the first step, we partition one video sequence into groups, where each group consists of two successive frames (image pairs), and each pair in each group contains one overlapping part, which is used as a stitching region. Fist, the 3D point cloud of each group is reconstructed by utilizing structure from motion (SFM). Secondly, a scheme based on SIFT features registers and stitches the obtained 3D point clouds, by estimating the transformation matrix of the overlapping part between different groups with high accuracy and efficiency. Thirdly, we select the most robust SIFT feature points as the seed points, and then obtain the dense point cloud from sparse point cloud via a depth testing method presented by Furukawa. Finally, by utilizing Poisson surface reconstruction, polygonal patches for the internal organs are obtained. Experimental results demonstrate that the proposed method achieves a high accuracy and efficiency for 3D reconstruction of gastric surface from an endoscopic video sequence.

  17. 3D Adaptive Mesh Refinement Simulations of the Gas Cloud G2 Born within the Disks of Young Stars in the Galactic Center

    NASA Astrophysics Data System (ADS)

    Schartmann, M.; Ballone, A.; Burkert, A.; Gillessen, S.; Genzel, R.; Pfuhl, O.; Eisenhauer, F.; Plewa, P. M.; Ott, T.; George, E. M.; Habibi, M.

    2015-10-01

    The dusty, ionized gas cloud G2 is currently passing the massive black hole in the Galactic Center at a distance of roughly 2400 Schwarzschild radii. We explore the possibility of a starting point of the cloud within the disks of young stars. We make use of the large amount of new observations in order to put constraints on G2's origin. Interpreting the observations as a diffuse cloud of gas, we employ three-dimensional hydrodynamical adaptive mesh refinement (AMR) simulations with the PLUTO code and do a detailed comparison with observational data. The simulations presented in this work update our previously obtained results in multiple ways: (1) high resolution three-dimensional hydrodynamical AMR simulations are used, (2) the cloud follows the updated orbit based on the Brackett-γ data, (3) a detailed comparison to the observed high-quality position-velocity (PV) diagrams and the evolution of the total Brackett-γ luminosity is done. We concentrate on two unsolved problems of the diffuse cloud scenario: the unphysical formation epoch only shortly before the first detection and the too steep Brackett-γ light curve obtained in simulations, whereas the observations indicate a constant Brackett-γ luminosity between 2004 and 2013. For a given atmosphere and cloud mass, we find a consistent model that can explain both, the observed Brackett-γ light curve and the PV diagrams of all epochs. Assuming initial pressure equilibrium with the atmosphere, this can be reached for a starting date earlier than roughly 1900, which is close to apo-center and well within the disks of young stars.

  18. 3D ADAPTIVE MESH REFINEMENT SIMULATIONS OF THE GAS CLOUD G2 BORN WITHIN THE DISKS OF YOUNG STARS IN THE GALACTIC CENTER

    SciTech Connect

    Schartmann, M.; Ballone, A.; Burkert, A.; Gillessen, S.; Genzel, R.; Pfuhl, O.; Eisenhauer, F.; Plewa, P. M.; Ott, T.; George, E. M.; Habibi, M.

    2015-10-01

    The dusty, ionized gas cloud G2 is currently passing the massive black hole in the Galactic Center at a distance of roughly 2400 Schwarzschild radii. We explore the possibility of a starting point of the cloud within the disks of young stars. We make use of the large amount of new observations in order to put constraints on G2's origin. Interpreting the observations as a diffuse cloud of gas, we employ three-dimensional hydrodynamical adaptive mesh refinement (AMR) simulations with the PLUTO code and do a detailed comparison with observational data. The simulations presented in this work update our previously obtained results in multiple ways: (1) high resolution three-dimensional hydrodynamical AMR simulations are used, (2) the cloud follows the updated orbit based on the Brackett-γ data, (3) a detailed comparison to the observed high-quality position–velocity (PV) diagrams and the evolution of the total Brackett-γ luminosity is done. We concentrate on two unsolved problems of the diffuse cloud scenario: the unphysical formation epoch only shortly before the first detection and the too steep Brackett-γ light curve obtained in simulations, whereas the observations indicate a constant Brackett-γ luminosity between 2004 and 2013. For a given atmosphere and cloud mass, we find a consistent model that can explain both, the observed Brackett-γ light curve and the PV diagrams of all epochs. Assuming initial pressure equilibrium with the atmosphere, this can be reached for a starting date earlier than roughly 1900, which is close to apo-center and well within the disks of young stars.

  19. Point Cloud Mapping Methods for Documenting Cultural Landscape Features at the Wormsloe State Historic Site, Savannah, Georgia, USA

    NASA Astrophysics Data System (ADS)

    Jordana, T. R.; Goetcheus, C. L.; Madden, M.

    2016-06-01

    Documentation of the three-dimensional (3D) cultural landscape has traditionally been conducted during site visits using conventional photographs, standard ground surveys and manual measurements. In recent years, there have been rapid developments in technologies that produce highly accurate 3D point clouds, including aerial LiDAR, terrestrial laser scanning, and photogrammetric data reduction from unmanned aerial systems (UAS) images and hand held photographs using Structure from Motion (SfM) methods. These 3D point clouds can be precisely scaled and used to conduct measurements of features even after the site visit has ended. As a consequence, it is becoming increasingly possible to collect non-destructive data for a wide variety of cultural site features, including landscapes, buildings, vegetation, artefacts and gardens. As part of a project for the U.S. National Park Service, a variety of data sets have been collected for the Wormsloe State Historic Site, near Savannah, Georgia, USA. In an effort to demonstrate the utility and versatility of these methods at a range of scales, comparisons of the features mapped with different techniques will be discussed with regards to accuracy, data set completeness, cost and ease-of-use.

  20. Cloud point extraction of Δ9-tetrahydrocannabinol from cannabis resin.

    PubMed

    Ameur, S; Haddou, B; Derriche, Z; Canselier, J P; Gourdon, C

    2013-04-01

    A cloud point extraction coupled with high performance liquid chromatography (HPLC/UV) method was developed for the determination of Δ(9)-tetrahydrocannabinol (THC) in micellar phase. The nonionic surfactant "Dowfax 20B102" was used to extract and pre-concentrate THC from cannabis resin, prior to its determination with a HPLC-UV system (diode array detector) with isocratic elution. The parameters and variables affecting the extraction were investigated. Under optimum conditions (1 wt.% Dowfax 20B102, 1 wt.% Na2SO4, T = 318 K, t = 30 min), this method yielded a quite satisfactory recovery rate (~81 %). The limit of detection was 0.04 μg mL(-1), and the relative standard deviation was less than 2 %. Compared with conventional solid-liquid extraction, this new method avoids the use of volatile organic solvents, therefore is environmentally safer.

  1. Cloud Point Extraction for Electroanalysis: Anodic Stripping Voltammetry of Cadmium.

    PubMed

    Rusinek, Cory A; Bange, Adam; Papautsky, Ian; Heineman, William R

    2015-06-16

    Cloud point extraction (CPE) is a well-established technique for the preconcentration of hydrophobic species from water without the use of organic solvents. Subsequent analysis is then typically performed via atomic absorption spectroscopy (AAS), UV-vis spectroscopy, or high performance liquid chromatography (HPLC). However, the suitability of CPE for electroanalytical methods such as stripping voltammetry has not been reported. We demonstrate the use of CPE for electroanalysis using the determination of cadmium (Cd(2+)) by anodic stripping voltammetry (ASV). Rather than using the chelating agents which are commonly used in CPE to form a hydrophobic, extractable metal complex, we used iodide and sulfuric acid to neutralize the charge on Cd(2+) to form an extractable ion pair. This offers good selectivity for Cd(2+) as no interferences were observed from other heavy metal ions. Triton X-114 was chosen as the surfactant for the extraction because its cloud point temperature is near room temperature (22-25 °C). Bare glassy carbon (GC), bismuth-coated glassy carbon (Bi-GC), and mercury-coated glassy carbon (Hg-GC) electrodes were compared for the CPE-ASV. A detection limit for Cd(2+) of 1.7 nM (0.2 ppb) was obtained with the Hg-GC electrode. ASV with CPE gave a 20x decrease (4.0 ppb) in the detection limit compared to ASV without CPE. The suitability of this procedure for the analysis of tap and river water samples was demonstrated. This simple, versatile, environmentally friendly, and cost-effective extraction method is potentially applicable to a wide variety of transition metals and organic compounds that are amenable to detection by electroanalytical methods.

  2. Spatially explicit spectral analysis of point clouds and geospatial data

    USGS Publications Warehouse

    Buscombe, Daniel D.

    2015-01-01

    The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software packagePySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python, it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is

  3. Cloud Point Extraction for Electroanalysis: Anodic Stripping Voltammetry of Cadmium

    PubMed Central

    Rusinek, Cory A.; Bange, Adam; Papautsky, Ian; Heineman, William R.

    2016-01-01

    Cloud point extraction (CPE) is a well-established technique for the pre-concentration of hydrophobic species from water without the use of organic solvents. Subsequent analysis is then typically performed via atomic absorption spectroscopy (AAS), UV-Vis spectroscopy, or high performance liquid chromatography (HPLC). However, the suitability of CPE for electroanalytical methods such as stripping voltammetry has not been reported. We demonstrate the use of CPE for electroanalysis using the determination of cadmium (Cd2+) by anodic stripping voltammetry (ASV) as a representative example. Rather than using the chelating agents which are commonly used in CPE to form a hydrophobic, extractable metal complex, we used iodide and sulfuric acid to neutralize the charge on Cd2+ to form an extractable ion pair. Triton X-114 was chosen as the surfactant for the extraction because its cloud point temperature is near room temperature (22–25° C). Bare glassy carbon (GC), bismuth-coated glassy carbon (Bi-GC), and mercury-coated glassy carbon (Hg-GC) electrodes were compared for the CPE-ASV. A detection limit for Cd2+ of 1.7 nM (0.2 ppb) was obtained with the Hg-GC electrode. Comparison of ASV analysis without CPE was also investigated and a 20x decrease (4.0 ppb) in the detection limit was observed. The suitability of this procedure for the analysis of tap and river water samples was also demonstrated. This simple, versatile, environmentally friendly and cost-effective extraction method is potentially applicable to a wide variety of transition metals and organic compounds that are amenable to detection by electroanalytical methods. PMID:25996561

  4. Spatially explicit spectral analysis of point clouds and geospatial data

    NASA Astrophysics Data System (ADS)

    Buscombe, Daniel

    2016-01-01

    The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software package PySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python, it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is described

  5. Evaluation of Vertical Lacunarity Profiles in Forested Areas Using Airborne Laser Scanning Point Clouds

    NASA Astrophysics Data System (ADS)

    Székely, B.; Kania, A.; Standovár, T.; Heilmeier, H.

    2016-06-01

    The horizontal variation and vertical layering of the vegetation are important properties of the canopy structure determining the habitat; three-dimensional (3D) distribution of objects (shrub layers, understory vegetation, etc.) is related to the environmental factors (e.g., illumination, visibility). It has been shown that gaps in forests, mosaic-like structures are essential to biodiversity; various methods have been introduced to quantify this property. As the distribution of gaps in the vegetation is a multi-scale phenomenon, in order to capture it in its entirety, scale-independent methods are preferred; one of these is the calculation of lacunarity. We used Airborne Laser Scanning point clouds measured over a forest plantation situated in a former floodplain. The flat topographic relief ensured that the tree growth is independent of the topographic effects. The tree pattern in the plantation crops provided various quasi-regular and irregular patterns, as well as various ages of the stands. The point clouds were voxelized and layers of voxels were considered as images for two-dimensional input. These images calculated for a certain vicinity of reference points were taken as images for the computation of lacunarity curves, providing a stack of lacunarity curves for each reference points. These sets of curves have been compared to reveal spatial changes of this property. As the dynamic range of the lacunarity values is very large, the natural logarithms of the values were considered. Logarithms of lacunarity functions show canopy-related variations, we analysed these variations along transects. The spatial variation can be related to forest properties and ecology-specific aspects.

  6. 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

  7. 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.

  8. Analysis of Performance and Optimization of Point Cloud Conversion in Spatial Databases

    NASA Astrophysics Data System (ADS)

    Chrόszcz, Aleksandra; Łukasik, Piotr; Lupa, Michał

    2016-10-01

    This article compares popular relational database management systems and one nonrelational database in the context of storage of cloud points from LIDAR. The authors examine the efficient storage of cloud points in the database and optimization of query execution time. Additionally, there is also a comparison of the impact of SSD and traditional HDD technology on the time taken to perform operations on a point cloud.

  9. Three-Dimensional Point Cloud Recognition via Distributions of Geometric Distances

    DTIC Science & Technology

    2008-05-01

    A geometric framework for the recognition of three-dimensional objects represented by point clouds is introduced in this paper. The proposed approach...representing the point clouds . The first signature we introduce is the histogram of pairwise diffusion distances between all points on the shape

  10. Formation Dirac point and the topological surface states for HgCdTe-QW and mixed 3D HgCdTe TI

    NASA Astrophysics Data System (ADS)

    Marchewka, Michał

    2016-12-01

    In this paper the results of numerical calculations based on the finite difference method (FDM) for the 2D and 3D TI with and without uniaxial tensile strain for mixed Hg1-xCdxTe structures are presented. The numerical calculations were made using the 8×8 model for x from 0 up to 0.155 and for the wide range for the thickness from a few nm for 2D up to 150 nm for 3D TI as well as for different mismatch of the lattice constant and different barrier potential in the case of the QW. For the investigated region of the Cd composition (x value) the negative energy gap (Eg=Γ8-Γ6) in the Hg1-xCdxTe is smaller than in the case of pure HgTe which, as it turns out, has a significant influence on the topological surface states (TSS) and the position of the Dirac point for QW as well as for 3D TI. The results show that the strained gap and the position of the Dirac point against the Γ8 is a function of the x-Cd compounds in the case of the 3D TI as well as the critical width of the mixed Hg1-xCdxTe QW.

  11. Indoor A* Pathfinding Through an Octree Representation of a Point Cloud

    NASA Astrophysics Data System (ADS)

    Rodenberg, O. B. P. M.; Verbree, E.; Zlatanova, S.

    2016-10-01

    There is a growing demand of 3D indoor pathfinding applications. Researched in the field of robotics during the last decades of the 20th century, these methods focussed on 2D navigation. Nowadays we would like to have the ability to help people navigate inside buildings or send a drone inside a building when this is too dangerous for people. What these examples have in common is that an object with a certain geometry needs to find an optimal collision free path between a start and goal point. This paper presents a new workflow for pathfinding through an octree representation of a point cloud. We applied the following steps: 1) the point cloud is processed so it fits best in an octree; 2) during the octree generation the interior empty nodes are filtered and further processed; 3) for each interior empty node the distance to the closest occupied node directly under it is computed; 4) a network graph is computed for all empty nodes; 5) the A* pathfinding algorithm is conducted. This workflow takes into account the connectivity for each node to all possible neighbours (face, edge and vertex and all sizes). Besides, a collision avoidance system is pre-processed in two steps: first, the clearance of each empty node is computed, and then the maximal crossing value between two empty neighbouring nodes is computed. The clearance is used to select interior empty nodes of appropriate size and the maximal crossing value is used to filter the network graph. Finally, both these datasets are used in A* pathfinding.

  12. Detection of Geometric Keypoints and its Application to Point Cloud Coarse Registration

    NASA Astrophysics Data System (ADS)

    Bueno, M.; Martínez-Sánchez, J.; González-Jorge, H.; Lorenzo, H.

    2016-06-01

    Acquisition of large scale scenes, frequently, involves the storage of large amount of data, and also, the placement of several scan positions to obtain a complete object. This leads to a situation with a different coordinate system in each scan position. Thus, a preprocessing of it to obtain a common reference frame is usually needed before analysing it. Automatic point cloud registration without locating artificial markers is a challenging field of study. The registration of millions or billions of points is a demanding task. Subsampling the original data usually solves the situation, at the cost of reducing the precision of the final registration. In this work, a study of the subsampling via the detection of keypoints and its capability to apply in coarse alignment is performed. The keypoints obtained are based on geometric features of each individual point, and are extracted using the Difference of Gaussians approach over 3D data. The descriptors include features as eigenentropy, change of curvature and planarity. Experiments demonstrate that the coarse alignment, obtained through these keypoints outperforms the coarse registration root mean squared error of an operator by 3 - 5 cm. The applicability of these keypoints is tested and verified in five different case studies.

  13. 3d-3d correspondence revisited

    DOE PAGES

    Chung, Hee -Joong; Dimofte, Tudor; Gukov, Sergei; ...

    2016-04-21

    In fivebrane compactifications on 3-manifolds, we point out the importance of all flat connections in the proper definition of the effective 3d N = 2 theory. The Lagrangians of some theories with the desired properties can be constructed with the help of homological knot invariants that categorify colored Jones polynomials. Higgsing the full 3d theories constructed this way recovers theories found previously by Dimofte-Gaiotto-Gukov. As a result, we also consider the cutting and gluing of 3-manifolds along smooth boundaries and the role played by all flat connections in this operation.

  14. 2-D and 3-D Heliospheric Imaging from LEO, L1 and L5: Instruments, Vantage Points, and Applications

    NASA Astrophysics Data System (ADS)

    DeForest, C. E.

    2015-12-01

    Heliospheric imaging has come of age scientifically, and multiple heliospheric imagers are either operating or being built to operate on scientific missions. Much study and effort has been put into the advantages of solar wind imaging for space weather prediction. For example, CME tracking (either in 3-D with polarization, or in an image plane from a vantage far from Earth) has the potential to greatly increase arrival time predictions. Likewise, higher spatial and temporal resolution could provide critical clues about the important N/S component of the entrained magnetic field, by connecting signed surface magnetograms of the Sun to particular structures observed in the corona and, later, in the ICME. I will discuss the current state of understanding of polarized and/or high resolution heliospheric imaging as it relates to space weather forecasting, the relative advantages of an instrument at LEO, L1, or L5, and desiderata to exploit currently-validated and under-consideration techniques in an operational, prototype, or scientific next-generation solar wind imaging experiment.

  15. 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.

  16. Automatic derivation of natural and artificial lineaments from ALS point clouds in floodplains

    NASA Astrophysics Data System (ADS)

    Mandlburger, G.; Briese, C.

    2009-04-01

    Water flow is one of the most important driving forces in geomorphology and river systems have ever since formed our landscapes. With increasing urbanisation fertile flood plains were more and more cultivated and the defence of valuable settlement areas by dikes and dams became an important issue. Today, we are dealing with landscapes built up by natural as well as man-made artificial forces. In either case the general shape of the terrain can be portrayed by lineaments representing discontinuities of the terrain slope. Our contribution, therefore, presents an automatic method for delineating natural and artificial structure lines based on randomly distributed point data with high density of more than one point/m2. Preferably, the last echoes of airborne laser scanning (ALS) point clouds are used, since the laser signal is able to penetrate vegetation through small gaps in the foliage. Alternatively, point clouds from (multi) image matching can be employed, but poor ground point coverage in vegetated areas is often the limiting factor. Our approach is divided into three main steps: First, potential 2D start segments are detected by analyzing the surface curvature in the vicinity of each data point, second, the detailed 3D progression of each structure line is modelled patch-wise by intersecting surface pairs (e.g. planar patch pairs) based on the detected start segments and by performing line growing and, finally, post-processing like line cleaning, smoothing and networking is carried out in a last step. For the initial detection of start segments a best fitting two dimensional polynomial surface (quadric) is computed in each data point based on a set of neighbouring points, from which the minimum and maximum curvature is derived. Patches showing high maximum and low minimum curvatures indicate linear discontinuities in the surface slope and serve as start segments for the subsequent 3D modelling. Based on the 2D location and orientation of the start segments

  17. Irreducible 3D Radiative Transfer Effects in Multi-angle/Multi-spectral Radio-Polarimetric Signals (Not Noise!) from a Mixture of Clouds and Aerosol in a Single Large-Footprint Pixel

    NASA Astrophysics Data System (ADS)

    Davis, A. B.; Qu, Z.; Emde, C.; Xu, F.; Marshak, A.

    2013-12-01

    Although the Glory satellite mission failed at launch, the atmospheric observation strategy implemented in its Aerosol Polarization Sensor (APS) is alive and well since it is at least possible that another one will be built and launched. This strategy is based on APS's along-track scanning spectro-polarimetric measurement system that captures the three main Stokes vector elements (I,Q,U) at a large number (>200) viewing directions for 9 wavelengths emanating from a single pixel that is ~7 km in diameter at nadir and stretches into a ~7 x 20 km^2 ellipse at the most oblique views to be considered (~70 degrees). Two cloud cameras (CCs) were also onboard Glory to provide spatial context. If the relatively large APS footprint is cloud-free or fully-cloudy, then a 1D vector radiative transfer (RT) model is adequate for predicting the APS signals and, upon iteration over its input parameters, aerosol and cloud property retrievals are expected to be of high quality. And this level of accuracy is indeed required to make a real breakthrough in climate modeling where the radiative properties of aerosols and clouds remain one of the main sources of uncertainty. However, the CCs will often show that the APS's field-of-view is a spatially complex cloud scene, but where we are mostly interested in the ambient aerosols. Moreover, it is precisely these aerosols in contact with clouds that will influence their microphysical and optical properties, leading to the manifold indirect aerosol effects on the climate system that need to be far better understood in order to improve their representation in climate models. Therefore, the research presented here addresses the challenge of characterizing simultaneously aerosols and clouds in a single APS observation. Access to polarization can, at least in principle, be used to separate clouds and aerosols using the cloud-bow directions that will often be sampled by APS. In practice, however, we need to assess the extent of 3D polarized RT

  18. Correlation of Point B and Lymph Node Dose in 3D-Planned High-Dose-Rate Cervical Cancer Brachytherapy

    SciTech Connect

    Lee, Larissa J.; Sadow, Cheryl A.; Russell, Anthony; Viswanathan, Akila N.

    2009-11-01

    Purpose: To compare high dose rate (HDR) point B to pelvic lymph node dose using three-dimensional-planned brachytherapy for cervical cancer. Methods and Materials: Patients with FIGO Stage IB-IIIB cervical cancer received 70 tandem HDR applications using CT-based treatment planning. The obturator, external, and internal iliac lymph nodes (LN) were contoured. Per fraction (PF) and combined fraction (CF) right (R), left (L), and bilateral (Bil) nodal doses were analyzed. Point B dose was compared with LN dose-volume histogram (DVH) parameters by paired t test and Pearson correlation coefficients. Results: Mean PF and CF doses to point B were R 1.40 Gy +- 0.14 (CF: 7 Gy), L 1.43 +- 0.15 (CF: 7.15 Gy), and Bil 1.41 +- 0.15 (CF: 7.05 Gy). The correlation coefficients between point B and the D100, D90, D50, D2cc, D1cc, and D0.1cc LN were all less than 0.7. Only the D2cc to the obturator and the D0.1cc to the external iliac nodes were not significantly different from the point B dose. Significant differences between R and L nodal DVHs were seen, likely related to tandem deviation from irregular tumor anatomy. Conclusions: With HDR brachytherapy for cervical cancer, per fraction nodal dose approximates a dose equivalent to teletherapy. Point B is a poor surrogate for dose to specific nodal groups. Three-dimensional defined nodal contours during brachytherapy provide a more accurate reflection of delivered dose and should be part of comprehensive planning of the total dose to the pelvic nodes, particularly when there is evidence of pathologic involvement.

  19. General principles of describing second- and higher-order null points of a potential magnetic field in 3D

    NASA Astrophysics Data System (ADS)

    Lukashenko, A. T.; Veselovsky, I. S.

    2015-12-01

    General principles of describing secondand higher-order null points of a potential magnetic field are formulated. The potential near a second-order null of the general form can be specified by a linear combination of four basic functions, the list of which is presented. Near secondand higher-order null points, field line equations often cannot be integrated analytically; however, in some cases, it is possible to present a qualitative description of the geometry of null vicinities with consideration of the behavior of field lines near rays outgoing from null, at which the field is radial or equals zero.

  20. Facial plastic surgery area acquisition method based on point cloud mathematical model solution.

    PubMed

    Li, Xuwu; Liu, Fei

    2013-09-01

    It is one of the hot research problems nowadays to find a quick and accurate method of acquiring the facial plastic surgery area to provide sufficient but irredundant autologous or in vitro skin source for covering extensive wound, trauma, and burnt area. At present, the acquisition of facial plastic surgery area mainly includes model laser scanning, point cloud data acquisition, pretreatment of point cloud data, three-dimensional model reconstruction, and computation of area. By using this method, the area can be computed accurately, but it is hard to control the random error, and it requires a comparatively longer computation period. In this article, a facial plastic surgery area acquisition method based on point cloud mathematical model solution is proposed. This method applies symmetric treatment to the point cloud based on the pretreatment of point cloud data, through which the comparison diagram color difference map of point cloud error before and after symmetry is obtained. The slicing mathematical model of facial plastic area is got through color difference map diagram. By solving the point cloud data in this area directly, the facial plastic area is acquired. The point cloud data are directly operated in this method, which can accurately and efficiently complete the surgery area computation. The result of the comparative analysis shows the method is effective in facial plastic surgery area.

  1. CASTLE3D - A Computer Aided System for Labelling Archaeological Excavations in 3D

    NASA Astrophysics Data System (ADS)

    Houshiar, H.; Borrmann, D.; Elseberg, J.; Nüchter, A.; Näth, F.; Winkler, S.

    2015-08-01

    Documentation of archaeological excavation sites with conventional methods and tools such as hand drawings, measuring tape and archaeological notes is time consuming. This process is prone to human errors and the quality of the documentation depends on the qualification of the archaeologist on site. Use of modern technology and methods in 3D surveying and 3D robotics facilitate and improve this process. Computer-aided systems and databases improve the documentation quality and increase the speed of data acquisition. 3D laser scanning is the state of the art in modelling archaeological excavation sites, historical sites and even entire cities or landscapes. Modern laser scanners are capable of data acquisition of up to 1 million points per second. This provides a very detailed 3D point cloud of the environment. 3D point clouds and 3D models of an excavation site provide a better representation of the environment for the archaeologist and for documentation. The point cloud can be used both for further studies on the excavation and for the presentation of results. This paper introduces a Computer aided system for labelling archaeological excavations in 3D (CASTLE3D). Consisting of a set of tools for recording and georeferencing the 3D data from an excavation site, CASTLE3D is a novel documentation approach in industrial archaeology. It provides a 2D and 3D visualisation of the data and an easy-to-use interface that enables the archaeologist to select regions of interest and to interact with the data in both representations. The 2D visualisation and a 3D orthogonal view of the data provide cuts of the environment that resemble the traditional hand drawings. The 3D perspective view gives a realistic view of the environment. CASTLE3D is designed as an easy-to-use on-site semantic mapping tool for archaeologists. Each project contains a predefined set of semantic information that can be used to label findings in the data. Multiple regions of interest can be joined under

  2. Apparent spatial blurring and displacement of a point optical source due to cloud scattering

    SciTech Connect

    Brower, K.L.

    1997-09-01

    A Monte Carlo algorithm is used to determine the apparent spatial blurring of a terrestrial 1.07 micron optical point source due to cloud scattering as seen from space. The virtual image of a point source over a virtual source plane area 22.4 x 22.4 square kilometers arising from cloud scattering was determined for stratus clouds (NASA cloud number 5) and altostratus clouds optical source arises from photon scattering by cloud water droplets. Displacement of the virtual source is due to the apparent illumination of the cloud top region directly about the actual source which when viewed at a nonzero look angle gives a projected displacement of the apparent source relative to the actual source. These features are quantified by an analysis of the Monte Carlo computational results.

  3. A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds

    NASA Astrophysics Data System (ADS)

    Xiong, B.; Oude Elberink, S.; Vosselman, G.

    2014-07-01

    In the task of 3D building model reconstruction from point clouds we face the problem of recovering a roof topology graph in the presence of noise, small roof faces and low point densities. Errors in roof topology graphs will seriously affect the final modelling results. The aim of this research is to automatically correct these errors. We define the graph correction as a graph-to-graph problem, similar to the spelling correction problem (also called the string-to-string problem). The graph correction is more complex than string correction, as the graphs are 2D while strings are only 1D. We design a strategy based on a dictionary of graph edit operations to automatically identify and correct the errors in the input graph. For each type of error the graph edit dictionary stores a representative erroneous subgraph as well as the corrected version. As an erroneous roof topology graph may contain several errors, a heuristic search is applied to find the optimum sequence of graph edits to correct the errors one by one. The graph edit dictionary can be expanded to include entries needed to cope with errors that were previously not encountered. Experiments show that the dictionary with only fifteen entries already properly corrects one quarter of erroneous graphs in about 4500 buildings, and even half of the erroneous graphs in one test area, achieving as high as a 95% acceptance rate of the reconstructed models.

  4. An orientation inference framework for surface reconstruction from unorganized point clouds.

    PubMed

    Chen, Yi-Ling; Lai, Shang-Hong

    2011-03-01

    In this paper, we present an orientation inference framework for reconstructing implicit surfaces from unoriented point clouds. The proposed method starts from building a surface approximation hierarchy comprising of a set of unoriented local surfaces, which are represented as a weighted combination of radial basis functions. We formulate the determination of the globally consistent orientation as a graph optimization problem by treating the local implicit patches as nodes. An energy function is defined to penalize inconsistent orientation changes by checking the sign consistency between neighboring local surfaces. An optimal labeling of the graph nodes indicating the orientation of each local surface can, thus, be obtained by minimizing the total energy defined on the graph. The local inference results are propagated over the model in a front-propagation fashion to obtain the global solution. The reconstructed surfaces are consolidated by a simple and effective inspection procedure to locate the erroneously fitted local surfaces. A progressive reconstruction algorithm that iteratively includes more oriented points to improve the fitting accuracy and efficiently updates the RBF coefficients is proposed. We demonstrate the performance of the proposed method by showing the surface reconstruction results on some real-world 3-D data sets with comparison to those by using the previous methods.

  5. 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.

  6. SU-C-18A-04: 3D Markerless Registration of Lung Based On Coherent Point Drift: Application in Image Guided Radiotherapy

    SciTech Connect

    Nasehi Tehrani, J; Wang, J; Guo, X; Yang, Y

    2014-06-01

    Purpose: This study evaluated a new probabilistic non-rigid registration method called coherent point drift for real time 3D markerless registration of the lung motion during radiotherapy. Method: 4DCT image datasets Dir-lab (www.dir-lab.com) have been used for creating 3D boundary element model of the lungs. For the first step, the 3D surface of the lungs in respiration phases T0 and T50 were segmented and divided into a finite number of linear triangular elements. Each triangle is a two dimensional object which has three vertices (each vertex has three degree of freedom). One of the main features of the lungs motion is velocity coherence so the vertices that creating the mesh of the lungs should also have features and degree of freedom of lung structure. This means that the vertices close to each other tend to move coherently. In the next step, we implemented a probabilistic non-rigid registration method called coherent point drift to calculate nonlinear displacement of vertices between different expiratory phases. Results: The method has been applied to images of 10-patients in Dir-lab dataset. The normal distribution of vertices to the origin for each expiratory stage were calculated. The results shows that the maximum error of registration between different expiratory phases is less than 0.4 mm (0.38 SI, 0.33 mm AP, 0.29 mm RL direction). This method is a reliable method for calculating the vector of displacement, and the degrees of freedom (DOFs) of lung structure in radiotherapy. Conclusions: We evaluated a new 3D registration method for distribution set of vertices inside lungs mesh. In this technique, lungs motion considering velocity coherence are inserted as a penalty in regularization function. The results indicate that high registration accuracy is achievable with CPD. This method is helpful for cal