Science.gov

Sample records for 3-d point cloud

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. Dense 3d Point Cloud Generation from Uav Images from Image Matching and Global Optimazation

    NASA Astrophysics Data System (ADS)

    Rhee, S.; Kim, T.

    2016-06-01

    3D spatial information from unmanned aerial vehicles (UAV) images is usually provided in the form of 3D point clouds. For various UAV applications, it is important to generate dense 3D point clouds automatically from over the entire extent of UAV images. In this paper, we aim to apply image matching for generation of local point clouds over a pair or group of images and global optimization to combine local point clouds over the whole region of interest. We tried to apply two types of image matching, an object space-based matching technique and an image space-based matching technique, and to compare the performance of the two techniques. The object space-based matching used here sets a list of candidate height values for a fixed horizontal position in the object space. For each height, its corresponding image point is calculated and similarity is measured by grey-level correlation. The image space-based matching used here is a modified relaxation matching. We devised a global optimization scheme for finding optimal pairs (or groups) to apply image matching, defining local match region in image- or object- space, and merging local point clouds into a global one. For optimal pair selection, tiepoints among images were extracted and stereo coverage network was defined by forming a maximum spanning tree using the tiepoints. From experiments, we confirmed that through image matching and global optimization, 3D point clouds were generated successfully. However, results also revealed some limitations. In case of image-based matching results, we observed some blanks in 3D point clouds. In case of object space-based matching results, we observed more blunders than image-based matching ones and noisy local height variations. We suspect these might be due to inaccurate orientation parameters. The work in this paper is still ongoing. We will further test our approach with more precise orientation parameters.

  14. 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

  15. Dense point-cloud creation using superresolution for a monocular 3D reconstruction system

    NASA Astrophysics Data System (ADS)

    Diskin, Yakov; Asari, Vijayan K.

    2012-05-01

    We present an enhanced 3D reconstruction algorithm designed to support an autonomously navigated unmanned aerial system (UAS). The algorithm presented focuses on the 3D reconstruction of a scene using only a single moving camera. In this way, the system can be used to construct a point cloud model of its unknown surroundings. The original reconstruction process, resulting with a point cloud was computed based on feature matching and depth triangulation analysis. Although dense, this original model was hindered due to its low disparity resolution. As feature points were matched from frame to frame, the resolution of the input images and the discrete nature of disparities limited the depth computations within a scene. With the recent addition of the preprocessing steps of nonlinear super resolution, the accuracy of the point cloud which relies on precise disparity measurement has significantly increased. Using a pixel by pixel approach, the super resolution technique computes the phase congruency of each pixel's neighborhood and produces nonlinearly interpolated high resolution input frames. Thus, a feature point travels a more precise discrete disparity. Also, the quantity of points within the 3D point cloud model is significantly increased since the number of features is directly proportional to the resolution and high frequencies of the input image. The contribution of the newly added preprocessing steps is measured by evaluating the density and accuracy of the reconstructed point cloud for autonomous navigation and mapping tasks within unknown environments.

  16. 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.

  17. Graph-Based Compression of Dynamic 3D Point Cloud Sequences.

    PubMed

    Thanou, Dorina; Chou, Philip A; Frossard, Pascal

    2016-04-01

    This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames share some similarities, motion estimation is key to effective compression of these sequences. It, however, remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the point clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature-matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring a significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.

  18. 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.

  19. 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

  20. 3D campus modeling using LiDAR point cloud data

    NASA Astrophysics Data System (ADS)

    Kawata, Yoshiyuki; Yoshii, Satoshi; Funatsu, Yukihiro; Takemata, Kazuya

    2012-10-01

    The importance of having a 3D urban city model is recognized in many applications, such as management offices of risk and disaster, the offices for city planning and developing and others. As an example of urban model, we reconstructed 3D KIT campus manually in this study, by utilizing airborne LiDAR point cloud data. The automatic extraction of building shapes was left in future work.

  1. Adaptive noise suppression technique for dense 3D point cloud reconstructions from monocular vision

    NASA Astrophysics Data System (ADS)

    Diskin, Yakov; Asari, Vijayan K.

    2012-10-01

    Mobile vision-based autonomous vehicles use video frames from multiple angles to construct a 3D model of their environment. In this paper, we present a post-processing adaptive noise suppression technique to enhance the quality of the computed 3D model. Our near real-time reconstruction algorithm uses each pair of frames to compute the disparities of tracked feature points to translate the distance a feature has traveled within the frame in pixels into real world depth values. As a result these tracked feature points are plotted to form a dense and colorful point cloud. Due to the inevitable small vibrations in the camera and the mismatches within the feature tracking algorithm, the point cloud model contains a significant amount of misplaced points appearing as noise. The proposed noise suppression technique utilizes the spatial information of each point to unify points of similar texture and color into objects while simultaneously removing noise dissociated with any nearby objects. The noise filter combines all the points of similar depth into 2D layers throughout the point cloud model. By applying erosion and dilation techniques we are able to eliminate the unwanted floating points while retaining points of larger objects. To reverse the compression process, we transform the 2D layer back into the 3D model allowing points to return to their original position without the attached noise components. We evaluate the resulting noiseless point cloud by utilizing an unmanned ground vehicle to perform obstacle avoidance tasks. The contribution of the noise suppression technique is measured by evaluating the accuracy of the 3D reconstruction.

  2. 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

  3. 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

  4. 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.

  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. 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.

  7. Efficient Structure-Aware Selection Techniques for 3D Point Cloud Visualizations with 2DOF Input.

    PubMed

    Yu, Lingyun; Efstathiou, K; Isenberg, P; Isenberg, T

    2012-12-01

    Data selection is a fundamental task in visualization because it serves as a pre-requisite to many follow-up interactions. Efficient spatial selection in 3D point cloud datasets consisting of thousands or millions of particles can be particularly challenging. We present two new techniques, TeddySelection and CloudLasso, that support the selection of subsets in large particle 3D datasets in an interactive and visually intuitive manner. Specifically, we describe how to spatially select a subset of a 3D particle cloud by simply encircling the target particles on screen using either the mouse or direct-touch input. Based on the drawn lasso, our techniques automatically determine a bounding selection surface around the encircled particles based on their density. This kind of selection technique can be applied to particle datasets in several application domains. TeddySelection and CloudLasso reduce, and in some cases even eliminate, the need for complex multi-step selection processes involving Boolean operations. This was confirmed in a formal, controlled user study in which we compared the more flexible CloudLasso technique to the standard cylinder-based selection technique. This study showed that the former is consistently more efficient than the latter - in several cases the CloudLasso selection time was half that of the corresponding cylinder-based selection.

  8. 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.

  9. 3D change detection at street level using mobile laser scanning point clouds and terrestrial images

    NASA Astrophysics Data System (ADS)

    Qin, Rongjun; Gruen, Armin

    2014-04-01

    Automatic change detection and geo-database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images, but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-databases include 3D street-level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection, but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference, and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost-efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi-automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and re-projected between each other to check the geometrical

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  11. Sloped terrain segmentation for autonomous drive using sparse 3D point cloud.

    PubMed

    Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Jeong, Young-Sik; Um, Kyhyun; 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.

  12. 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

  13. Sloped terrain segmentation for autonomous drive using sparse 3D point cloud.

    PubMed

    Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Jeong, Young-Sik; Um, Kyhyun; 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

  14. Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare.

    NASA Astrophysics Data System (ADS)

    Rajendra, Y. D.; Mehrotra, S. C.; Kale, K. V.; Manza, R. R.; Dhumal, R. K.; Nagne, A. D.; Vibhute, A. D.

    2014-11-01

    Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object's surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior façade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.

  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. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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

    2015-01-01

    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. A method of 3D object recognition and localization in a cloud of points

    NASA Astrophysics Data System (ADS)

    Bielicki, Jerzy; Sitnik, Robert

    2013-12-01

    The proposed method given in this article is prepared for analysis of data in the form of cloud of points directly from 3D measurements. It is designed for use in the end-user applications that can directly be integrated with 3D scanning software. The method utilizes locally calculated feature vectors (FVs) in point cloud data. Recognition is based on comparison of the analyzed scene with reference object library. A global descriptor in the form of a set of spatially distributed FVs is created for each reference model. During the detection process, correlation of subsets of reference FVs with FVs calculated in the scene is computed. Features utilized in the algorithm are based on parameters, which qualitatively estimate mean and Gaussian curvatures. Replacement of differentiation with averaging in the curvatures estimation makes the algorithm more resistant to discontinuities and poor quality of the input data. Utilization of the FV subsets allows to detect partially occluded and cluttered objects in the scene, while additional spatial information maintains false positive rate at a reasonably low level.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

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

    PubMed

    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.

  13. 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

  14. 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

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

    NASA Astrophysics Data System (ADS)

    Sun, Shaohui

    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 unsolved. Automation is one of the key focus areas in this research. In this work, a fast, completely automated method to create 3D watertight building models from airborne LiDAR (Light Detection and Ranging) point clouds is presented. The developed method analyzes the scene content and produces multi-layer rooftops, with complex rigorous boundaries and vertical walls, that connect rooftops to the ground. The graph cuts algorithm is used to separate vegetative elements from the rest of the scene content, which is based on the local analysis about the properties of the local implicit surface patch. The ground terrain and building rooftop footprints are then extracted, utilizing the developed strategy, a two-step hierarchical Euclidean clustering. The method presented here adopts a "divide-and-conquer" scheme. Once the building footprints are segmented from the terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential points on the rooftop. For each individual building region, significant features on the rooftop are further detected using a specifically designed region-growing algorithm with surface smoothness constraints. The principal orientation of each building rooftop feature is calculated using a minimum bounding box fitting technique, and is used to guide the refinement of shapes and boundaries of the rooftop parts. Boundaries for all of these features are refined for the purpose of producing strict description. Once the description of the rooftops is achieved, polygonal mesh models are generated by creating surface patches with outlines defined by detected

  16. 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

  17. 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.

  18. 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.

  19. Extraction and refinement of building faces in 3D point clouds

    NASA Astrophysics Data System (ADS)

    Pohl, Melanie; Meidow, Jochen; Bulatov, Dimitri

    2013-10-01

    In this paper, we present an approach to generate a 3D model of an urban scene out of sensor data. The first milestone on that way is to classify the sensor data into the main parts of a scene, such as ground, vegetation, buildings and their outlines. This has already been accomplished within our previous work. Now, we propose a four-step algorithm to model the building structure, which is assumed to consist of several dominant planes. First, we extract small elevated objects, like chimneys, using a hot-spot detector and handle the detected regions separately. In order to model the variety of roof structures precisely, we split up complex building blocks into parts. Two different approaches are used: To act on the assumption of underlying 2D ground polygons, we use geometric methods to divide them into sub-polygons. Without polygons, we use morphological operations and segmentation methods. In the third step, extraction of dominant planes takes place, by using either RANSAC or J-linkage algorithm. They operate on point clouds of sufficient confidence within the previously separated building parts and give robust results even with noisy, outlier-rich data. Last, we refine the previously determined plane parameters using geometric relations of the building faces. Due to noise, these expected properties of roofs and walls are not fulfilled. Hence, we enforce them as hard constraints and use the previously extracted plane parameters as initial values for an optimization method. To test the proposed workflow, we use both several data sets, including noisy data from depth maps and data computed by laser scanning.

  20. 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.

  1. 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.

  2. Road Signs Detection and Recognition Utilizing Images and 3d Point Cloud Acquired by Mobile Mapping System

    NASA Astrophysics Data System (ADS)

    Li, Y. H.; Shinohara, T.; Satoh, T.; Tachibana, K.

    2016-06-01

    High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

  3. Geometric and topological feature extraction of linear segments from 2D cross-section data of 3D point clouds

    NASA Astrophysics Data System (ADS)

    Ramamurthy, Rajesh; Harding, Kevin; Du, Xiaoming; Lucas, Vincent; Liao, Yi; Paul, Ratnadeep; Jia, Tao

    2015-05-01

    Optical measurement techniques are often employed to digitally capture three dimensional shapes of components. The digital data density output from these probes range from a few discrete points to exceeding millions of points in the point cloud. The point cloud taken as a whole represents a discretized measurement of the actual 3D shape of the surface of the component inspected to the measurement resolution of the sensor. Embedded within the measurement are the various features of the part that make up its overall shape. Part designers are often interested in the feature information since those relate directly to part function and to the analytical models used to develop the part design. Furthermore, tolerances are added to these dimensional features, making their extraction a requirement for the manufacturing quality plan of the product. The task of "extracting" these design features from the point cloud is a post processing task. Due to measurement repeatability and cycle time requirements often automated feature extraction from measurement data is required. The presence of non-ideal features such as high frequency optical noise and surface roughness can significantly complicate this feature extraction process. This research describes a robust process for extracting linear and arc segments from general 2D point clouds, to a prescribed tolerance. The feature extraction process generates the topology, specifically the number of linear and arc segments, and the geometry equations of the linear and arc segments automatically from the input 2D point clouds. This general feature extraction methodology has been employed as an integral part of the automated post processing algorithms of 3D data of fine features.

  4. 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

  5. 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.

  6. D Geological Outcrop Characterization: Automatic Detection of 3d Planes (azimuth and Dip) Using LiDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Anders, K.; Hämmerle, M.; Miernik, G.; Drews, T.; Escalona, A.; Townsend, C.; Höfle, B.

    2016-06-01

    Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estima-tion. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Fur-thermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

  7. 3D polygonal representation of dense point clouds by triangulation, segmentation, and texture projection

    NASA Astrophysics Data System (ADS)

    Tajbakhsh, Touraj

    2010-02-01

    A basic concern of computer graphic is the modeling and realistic representation of three-dimensional objects. In this paper we present our reconstruction framework which determines a polygonal surface from a set of dense points such those typically obtained from laser scanners. We deploy the concept of adaptive blobs to achieve a first volumetric representation of the object. In the next step we estimate a coarse surface using the marching cubes method. We propose to deploy a depth-first search segmentation algorithm traversing a graph representation of the obtained polygonal mesh in order to identify all connected components. A so called supervised triangulation maps the coarse surfaces onto the dense point cloud. We optimize the mesh topology using edge exchange operations. For photo-realistic visualization of objects we finally synthesize optimal low-loss textures from available scene captures of different projections. We evaluate our framework on artificial data as well as real sensed data.

  8. 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.

  9. Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

    Point clouds generated from airborne oblique images have become a suitable source for detailed building damage assessment after a disaster event, since they provide the essential geometric and radiometric features of both roof and façades of the building. However, they often contain gaps that result either from physical damage or from a range of image artefacts or data acquisition conditions. A clear understanding of those reasons, and accurate classification of gap-type, are critical for 3D geometry-based damage assessment. In this study, a methodology was developed to delineate buildings from a point cloud and classify the present gaps. The building delineation process was carried out by identifying and merging the roof segments of single buildings from the pre-segmented 3D point cloud. This approach detected 96% of the buildings from a point cloud generated using airborne oblique images. The gap detection and classification methods were tested using two other data sets obtained with Unmanned Aerial Vehicle (UAV) images with a ground resolution of around 1-2 cm. The methods detected all significant gaps and correctly identified the gaps due to damage. The gaps due to damage were identified based on the surrounding damage pattern, applying Gabor wavelets and a histogram of gradient orientation features. Two learning algorithms - SVM and Random Forests were tested for mapping the damaged regions based on radiometric descriptors. The learning model based on Gabor features with Random Forests performed best, identifying 95% of the damaged regions. The generalization performance of the supervised model, however, was less successful: quality measures decreased by around 15-30%.

  10. 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).

  11. 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

  12. 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

    2014-01-01

    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

  13. 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.

  14. The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design

    NASA Astrophysics Data System (ADS)

    Borrmann, Dorit; Elseberg, Jan; Lingemann, Kai; Nüchter, Andreas

    2011-03-01

    The Hough Transform is a well-known method for detecting parameterized objects. It is the de facto standard for detecting lines and circles in 2-dimensional data sets. For 3D it has attained little attention so far. Even for the 2D case high computational costs have lead to the development of numerous variations for the Hough Transform. In this article we evaluate different variants of the Hough Transform with respect to their applicability to detect planes in 3D point clouds reliably. Apart from computational costs, the main problem is the representation of the accumulator. Usual implementations favor geometrical objects with certain parameters due to uneven sampling of the parameter space. We present a novel approach to design the accumulator focusing on achieving the same size for each cell and compare it to existing designs. [Figure not available: see fulltext.

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. Semi-automatic characterization of fractured rock masses using 3D point clouds: discontinuity orientation, spacing and SMR geomechanical classification

    NASA Astrophysics Data System (ADS)

    Riquelme, Adrian; Tomas, Roberto; Abellan, Antonio; Cano, Miguel; Jaboyedoff, Michel

    2015-04-01

    Investigation of fractured rock masses for different geological applications (e.g. fractured reservoir exploitation, rock slope instability, rock engineering, etc.) requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in 3D data acquisition using photogrammetric and/or LiDAR techniques currently allow a quick and an accurate characterization of rock mass discontinuities. This contribution presents a methodology for: (a) use of 3D point clouds for the identification and analysis of planar surfaces outcropping in a rocky slope; (b) calculation of the spacing between different discontinuity sets; (c) semi-automatic calculation of the parameters that play a capital role in the Slope Mass Rating geomechanical classification. As for the part a) (discontinuity orientation), our proposal identifies and defines the algebraic equations of the different discontinuity sets of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test. Additionally, the procedure finds principal orientations by Kernel Density Estimation and identifies clusters (Riquelme et al., 2014). As a result of this analysis, each point is classified with a discontinuity set and with an outcrop plane (cluster). Regarding the part b) (discontinuity spacing) our proposal utilises the previously classified point cloud to investigate how different outcropping planes are linked in space. Discontinuity spacing is calculated for each pair of linked clusters within the same discontinuity set, and then spacing values are analysed calculating their statistic values. Finally, as for the part c) the previous results are used to calculate parameters F_1, F2 and F3 of the Slope Mass Rating geomechanical classification. This analysis is carried out for each discontinuity set using their respective orientation extracted in part a). The open access tool SMRTool (Riquelme et al., 2014) is then used to calculate F1 to F3 correction

  3. 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.

  4. 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.

  5. Automatic reconstruction of 3D urban landscape by computing connected regions and assigning them an average altitude from LiDAR point cloud image

    NASA Astrophysics Data System (ADS)

    Kawata, Yoshiyuki; Koizumi, Kohei

    2014-10-01

    The demand of 3D city modeling has been increasing in many applications such as urban planing, computer gaming with realistic city environment, car navigation system with showing 3D city map, virtual city tourism inviting future visitors to a virtual city walkthrough and others. We proposed a simple method for reconstructing a 3D urban landscape from airborne LiDAR point cloud data. The automatic reconstruction method of a 3D urban landscape was implemented by the integration of all connected regions, which were extracted and extruded from the altitude mask images. These mask images were generated from the gray scale LiDAR image by the altitude threshold ranges. In this study we demonstrated successfully in the case of Kanazawa city center scene by applying the proposed method to the airborne LiDAR point cloud data.

  6. 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).

  7. [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.

  8. 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

  9. A new methodology in fast and accurate matching of the 2D and 3D point clouds extracted by laser scanner systems

    NASA Astrophysics Data System (ADS)

    Torabi, M.; Mousavi G., S. M.; Younesian, D.

    2015-03-01

    Registration of the point clouds is a conventional challenge in computer vision related applications. As an application, matching of train wheel profiles extracted from two viewpoints is studied in this paper. The registration problem is formulated into an optimization problem. An error minimization function for registration of the two partially overlapping point clouds is presented. The error function is defined as the sum of the squared distance between the source points and their corresponding pairs which should be minimized. The corresponding pairs are obtained thorough Iterative Closest Point (ICP) variants. Here, a point-to-plane ICP variant is employed. Principal Component Analysis (PCA) is used to obtain tangent planes. Thus it is shown that minimization of the proposed objective function diminishes point-to-plane ICP variant. We utilized this algorithm to register point clouds of two partially overlapping profiles of wheel train extracted from two viewpoints in 2D. Also, a number of synthetic point clouds and a number of real point clouds in 3D are studied to evaluate the reliability and rate of convergence in our method compared with other registration methods.

  10. Uav-Based Acquisition of 3d Point Cloud - a Comparison of a Low-Cost Laser Scanner and Sfm-Tools

    NASA Astrophysics Data System (ADS)

    Mader, D.; Blaskow, R.; Westfeld, P.; Maas, H.-G.

    2015-08-01

    The Project ADFEX (Adaptive Federative 3D Exploration of Multi Robot System) pursues the goal to develop a time- and cost-efficient system for exploration and monitoring task of unknown areas or buildings. A fleet of unmanned aerial vehicles equipped with appropriate sensors (laser scanner, RGB camera, near infrared camera, thermal camera) were designed and built. A typical operational scenario may include the exploration of the object or area of investigation by an UAV equipped with a laser scanning range finder to generate a rough point cloud in real time to provide an overview of the object on a ground station as well as an obstacle map. The data about the object enables the path planning for the robot fleet. Subsequently, the object will be captured by a RGB camera mounted on the second flying robot for the generation of a dense and accurate 3D point cloud by using of structure from motion techniques. In addition, the detailed image data serves as basis for a visual damage detection on the investigated building. This paper focuses on our experience with use of a low-cost light-weight Hokuyo laser scanner onboard an UAV. The hardware components for laser scanner based 3D point cloud acquisition are discussed, problems are demonstrated and analyzed, and a quantitative analysis of the accuracy potential is shown as well as in comparison with structure from motion-tools presented.

  11. 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.

  12. 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

  13. 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.

  14. NIF Ignition Target 3D Point Design

    SciTech Connect

    Jones, O; Marinak, M; Milovich, J; Callahan, D

    2008-11-05

    We have developed an input file for running 3D NIF hohlraums that is optimized such that it can be run in 1-2 days on parallel computers. We have incorporated increasing levels of automation into the 3D input file: (1) Configuration controlled input files; (2) Common file for 2D and 3D, different types of capsules (symcap, etc.); and (3) Can obtain target dimensions, laser pulse, and diagnostics settings automatically from NIF Campaign Management Tool. Using 3D Hydra calculations to investigate different problems: (1) Intrinsic 3D asymmetry; (2) Tolerance to nonideal 3D effects (e.g. laser power balance, pointing errors); and (3) Synthetic diagnostics.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    Our understanding of the evolution of hard rock coastlines is limited due to the episodic nature and ';slow' rate at which changes occur. High-resolution surveying techniques, such as Terrestrial Laser Scanning (TLS), have just begun to be adopted as a method of obtaining detailed point cloud data to monitor topographical changes over short periods of time (weeks to months). However, the difficulties involved in comparing consecutive point cloud data sets in a complex three-dimensional plane, such as occlusion due to surface roughness and positioning of data capture point as a result of a consistently changing environment (a beach profile), mean that comparing data sets can lead to errors in the region of 10 - 20 cm. Meshing techniques are often used for point cloud data analysis for simple surfaces, but in surfaces such as rocky cliff faces, this technique has been found to be ineffective. Recession rates of hard rock coastlines in the UK are typically determined using aerial photography or airborne LiDAR data, yet the detail of the important changes occurring to the cliff face and toe are missed using such techniques. In this study we apply an algorithm (M3C2 - Multiscale Model to Model Cloud Comparison), initially developed for analysing fluvial morphological change, that directly compares point to point cloud data using surface normals that are consistent with surface roughness and measure the change that occurs along the normal direction (Lague et al., 2013). The surfaces changes are analysed using a set of user defined scales based on surface roughness and registration error. Once the correct parameters are defined, the volumetric cliff face changes are calculated by integrating the mean distance between the point clouds. The analysis has been undertaken at two hard rock sites identified for their active erosion located on the UK's south west peninsular at Porthleven in south west Cornwall and Godrevy in north Cornwall. Alongside TLS point cloud data, in

  16. 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.

  17. 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.

  18. Cloud Property Retrieval and 3D Radiative Transfer

    NASA Technical Reports Server (NTRS)

    Cahalan, Robert F.

    2003-01-01

    Cloud thickness and photon mean-free-path together determine the scale of "radiative smoothing" of cloud fluxes and radiances. This scale is observed as a change in the spatial spectrum of cloud radiances, and also as the "halo size" seen by off beam lidar such as THOR and WAIL. Such of beam lidar returns are now being used to retrieve cloud layer thickness and vertical scattering extinction profile. We illustrate with recent measurements taken at the Oklahoma ARM site, comparing these to the-dependent 3D simulations. These and other measurements sensitive to 3D transfer in clouds, coupled with Monte Carlo and other 3D transfer methods, are providing a better understanding of the dependence of radiation on cloud inhomogeneity, and to suggest new retrieval algorithms appropriate for inhomogeneous clouds. The international "Intercomparison of 3D Radiation Codes" or I3RC, program is coordinating and evaluating the variety of 3D radiative transfer methods now available, and to make them more widely available. Information is on the Web at: http://i3rc.gsfc.nasa.gov/. Input consists of selected cloud fields derived from data sources such as radar, microwave and satellite, and from models involved in the GEWEX Cloud Systems Studies. Output is selected radiative quantities that characterize the large-scale properties of the fields of radiative fluxes and heating. Several example cloud fields will be used to illustrate. I3RC is currently implementing an "open source" 3d code capable of solving the baseline cases. Maintenance of this effort is one of the goals of a new 3DRT Working Group under the International Radiation Commission. It is hoped that the 3DRT WG will include active participation by land and ocean modelers as well, such as 3D vegetation modelers participating in RAMI.

  19. 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.

  20. 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

  1. 3D Viewer Platform of Cloud Clustering Management System: Google Map 3D

    NASA Astrophysics Data System (ADS)

    Choi, Sung-Ja; Lee, Gang-Soo

    The new management system of framework for cloud envrionemnt is needed by the platfrom of convergence according to computing environments of changes. A ISV and small business model is hard to adapt management system of platform which is offered from super business. This article suggest the clustering management system of cloud computing envirionments for ISV and a man of enterprise in small business model. It applies the 3D viewer adapt from map3D & earth of google. It is called 3DV_CCMS as expand the CCMS[1].

  2. 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

  3. 3D Modeling By Consolidation Of Independent Geometries Extracted From Point Clouds - The Case Of The Modeling Of The Turckheim's Chapel (Alsace, France)

    NASA Astrophysics Data System (ADS)

    Koehl, M.; Fabre, Ph.; Schlussel, B.

    2014-06-01

    Turckheim is a small town located in Alsace, north-east of France. In the heart of the Alsatian vineyard, this city has many historical monuments including its old church. To understand the effectiveness of the project described in this paper, it is important to have a look at the history of this church. Indeed there are many historical events that explain its renovation and even its partial reconstruction. The first mention of a christian sanctuary in Turckheim dates back to 898. It will be replaced in the 12th century by a roman church (chapel), which subsists today as the bell tower. Touched by a lightning in 1661, the tower then was enhanced. In 1736, it was repaired following damage sustained in a tornado. In 1791, the town installs an organ to the church. Last milestone, the church is destroyed by fire in 1978. The organ, like the heart of the church will then have to be again restored (1983) with a simplified architecture. From this heavy and rich past, it unfortunately and as it is often the case, remains only very few documents and information available apart from facts stated in some sporadic writings. And with regard to the geometry, the positioning, the physical characteristics of the initial building, there are very little indication. Some assumptions of positions and right-of-way were well issued by different historians or archaeologists. The acquisition and 3D modeling project must therefore provide the current state of the edifice to serve as the basis of new investigations and for the generation of new hypotheses on the locations and historical shapes of this church and its original chapel (Fig. 1)

  4. Dust density measurements in 3D dust clouds by tomography

    NASA Astrophysics Data System (ADS)

    Melzer, Andre

    2014-10-01

    Dusty plasmas usually consist of (micron-sized) dust particles trapped in a gaseous discharge plasma. Volume-filling dust clouds can be generated in the laboratory by thermophoretic levitation of the particles against gravity or under the microgravity conditions of parabolic flights. In these discharges, the dust density is typically so high that together with the high charge on the particles, the dust charge density can compete with the ion and electron (charge) density indicating a regime of charge depletion. Here, we present a technique that allows to measure the spatially resolved 3D dust density in such dusty discharges. For that purpose, the dust cloud is transilluminated by a homogeneous light source and the transilluminated cloud is measured under different angles in a tomographic-like manner. This allows to reconstruct the full 3D dust density within the discharge volume and further to deduce the force balance for the dust component. Supported by DLR 50 WM 1138.

  5. Registration of 3D spectral OCT volumes using 3D SIFT feature point matching

    NASA Astrophysics Data System (ADS)

    Niemeijer, Meindert; Garvin, Mona K.; Lee, Kyungmoo; van Ginneken, Bram; Abràmoff, Michael D.; Sonka, Milan

    2009-02-01

    The recent introduction of next generation spectral OCT scanners has enabled routine acquisition of high resolution, 3D cross-sectional volumetric images of the retina. 3D OCT is used in the detection and management of serious eye diseases such as glaucoma and age-related macular degeneration. For follow-up studies, image registration is a vital tool to enable more precise, quantitative comparison of disease states. This work presents a registration method based on a recently introduced extension of the 2D Scale-Invariant Feature Transform (SIFT) framework1 to 3D.2 The SIFT feature extractor locates minima and maxima in the difference of Gaussian scale space to find salient feature points. It then uses histograms of the local gradient directions around each found extremum in 3D to characterize them in a 4096 element feature vector. Matching points are found by comparing the distance between feature vectors. We apply this method to the rigid registration of optic nerve head- (ONH) and macula-centered 3D OCT scans of the same patient that have only limited overlap. Three OCT data set pairs with known deformation were used for quantitative assessment of the method's robustness and accuracy when deformations of rotation and scaling were considered. Three-dimensional registration accuracy of 2.0+/-3.3 voxels was observed. The accuracy was assessed as average voxel distance error in N=1572 matched locations. The registration method was applied to 12 3D OCT scans (200 x 200 x 1024 voxels) of 6 normal eyes imaged in vivo to demonstrate the clinical utility and robustness of the method in a real-world environment.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. Evaluation of terrestrial photogrammetric point clouds derived from thermal imagery

    NASA Astrophysics Data System (ADS)

    Metcalf, Jeremy P.; Olsen, Richard C.

    2016-05-01

    Computer vision and photogrammetric techniques have been widely applied to digital imagery producing high density 3D point clouds. Using thermal imagery as input, the same techniques can be applied to infrared data to produce point clouds in 3D space, providing surface temperature information. The work presented here is an evaluation of the accuracy of 3D reconstruction of point clouds produced using thermal imagery. An urban scene was imaged over an area at the Naval Postgraduate School, Monterey, CA, viewing from above as with an airborne system. Terrestrial thermal and RGB imagery were collected from a rooftop overlooking the site using a FLIR SC8200 MWIR camera and a Canon T1i DSLR. In order to spatially align each dataset, ground control points were placed throughout the study area using Trimble R10 GNSS receivers operating in RTK mode. Each image dataset is processed to produce a dense point cloud for 3D evaluation.

  13. Particle Acceleration at Reconnecting 3D Null Points

    NASA Astrophysics Data System (ADS)

    Stanier, A.; Browning, P.; Gordovskyy, M.; Dalla, S.

    2012-12-01

    Hard X-ray observations from the RHESSI spacecraft indicate that a significant fraction of solar flare energy release is in non-thermal energetic particles. A plausible acceleration mechanism for these are the strong electric fields associated with reconnection, a process that can be particularly efficient when particles become unmagnetised near to null points. This mechanism has been well studied in 2D, at X-points within reconnecting current sheets; however, 3D reconnection models show significant qualitative differences and it is not known whether these new models are efficient for particle acceleration. We place test particles in analytic model fields (eg. Craig and Fabling 1996) and numerical solutions to the the resistive magnetohydrodynamic (MHD) equations near reconnecting 3D nulls. We compare the behaviour of these test particles with previous results for test particle acceleration in ideal MHD models (Dalla and Browning 2005). We find that the fan model is very efficient due to an increasing "guide field" that stabilises particles against ejection from the current sheet. However, the spine model, which was the most promising in the ideal case, gives weak acceleration as the reconnection electric field is localised to a narrow cylinder about the spine axis.

  14. 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.

  15. 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.

  16. 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.

  17. Point Cloud Server (pcs) : Point Clouds In-Base Management and Processing

    NASA Astrophysics Data System (ADS)

    Cura, R.; Perret, J.; Paparoditis, N.

    2015-08-01

    In addition to the traditional Geographic Information System (GIS) data such as images and vectors, point cloud data has become more available. It is appreciated for its precision and true three-Dimensional (3D) nature. However, managing the point cloud can be difficult due to scaling problems and specificities of this data type. Several methods exist but are usually fairly specialised and solve only one aspect of the management problem. In this work, we propose a complete and efficient point cloud management system based on a database server that works on groups of points rather than individual points. This system is specifically designed to solve all the needs of point cloud users: fast loading, compressed storage, powerful filtering, easy data access and exporting, and integrated processing. Moreover, the system fully integrates metadata (like sensor position) and can conjointly use point clouds with images, vectors, and other point clouds. The system also offers in-base processing for easy prototyping and parallel processing and can scale well. Lastly, the system is built on open source technologies; therefore it can be easily extended and customised. We test the system will several billion points of point clouds from Lidar (aerial and terrestrial ) and stereo-vision. We demonstrate ~ 400 million pts/h loading speed, user-transparent greater than 2 to 4:1 compression ratio, filtering in the approximately 50 ms range, and output of about a million pts/s, along with classical processing, such as object detection.

  18. 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.

  19. Registration of point cloud data for HDD stamped base inspection

    NASA Astrophysics Data System (ADS)

    Suh, Sungho; Cho, Hansang

    2015-09-01

    As a part of the HDD manufacturing process, HDD stamped base, an exterior container, is one of the most essential components in which various parts become assembled to compose a hard disk drive (HDD). Height errors that are caused by pressing, breaking or cracking can occur on the base, because it is designed by a stamping method. In order to detect the height errors, the inspection process is essential in the production fields. In the current industry, CMM (Coordinate Measurement Machine) is one of the representative machines that inspect certain regions on the product. The machine probes a designated point by an operator and judges the defect by comparing the height of the point to the originally designed height. However, the method takes much time to inspect each designated point resulting in a total of 17 minutes. In order to reduce the total inspection time, we propose an inspection method using 3D point cloud data acquired from a holographic sensor. To compare the height from acquired 3D point cloud data with the one from the originally designed CAD data, the exact point cloud registration is important. There are differences between 2D image registration and 3D point cloud registration, such as translation on each plane, rotation, tilt, and nonlinear transformations. The relationship between the acquired 3D point cloud data and the originally designed CAD data can be obtained by projective transformation. If the projective transformation matrix between the two is obtained, 3D point cloud data registration can be performed. In order to calculate 3D projective transformation matrix, corresponding points between 3D point cloud data and CAD data are required. To find the corresponding points, we use the height map which is projected from 3D point cloud data onto XY plane. The height map has pixel intensity from the height value of each point. If the height maps from 3D point cloud data and CAD data are matched, corresponding points can be estimated. As one of the

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Reconstructing 3D coastal cliffs from airborne oblique photographs without ground control points

    NASA Astrophysics Data System (ADS)

    Dewez, T. J. B.

    2014-05-01

    Coastal cliff collapse hazard assessment requires measuring cliff face topography at regular intervals. Terrestrial laser scanner techniques have proven useful so far but are expensive to use either through purchasing the equipment or through survey subcontracting. In addition, terrestrial laser surveys take time which is sometimes incompatible with the time during with the beach is accessible at low-tide. By comparison, structure from motion techniques (SFM) are much less costly to implement, and if airborne, acquisition of several kilometers of coastline can be done in a matter of minutes. In this paper, the potential of GPS-tagged oblique airborne photographs and SFM techniques is examined to reconstruct chalk cliff dense 3D point clouds without Ground Control Points (GCP). The focus is put on comparing the relative 3D point of views reconstructed by Visual SFM with their synchronous Solmeta Geotagger Pro2 GPS locations using robust estimators. With a set of 568 oblique photos, shot from the open door of an airplane with a triplet of synchronized Nikon D7000, GPS and SFM-determined view point coordinates converge to X: ±31.5 m; Y: ±39.7 m; Z: ±13.0 m (LE66). Uncertainty in GPS position affects the model scale, angular attitude of the reference frame (the shoreline ends up tilted by 2°) and absolute positioning. Ground Control Points cannot be avoided to orient such models.

  8. Automatic Classification of Trees from Laser Scanning Point Clouds

    NASA Astrophysics Data System (ADS)

    Sirmacek, B.; Lindenbergh, R.

    2015-08-01

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

  9. Robotic Online Path Planning on Point Cloud.

    PubMed

    Liu, Ming

    2016-05-01

    This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.

  10. Robotic Online Path Planning on Point Cloud.

    PubMed

    Liu, Ming

    2016-05-01

    This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance. PMID:26011876

  11. Hole-ness of point clouds

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  12. Equation predicts diesel cloud points

    SciTech Connect

    Tsang, C.Y.; Ker, V.S.F.; Miranda, R.D.; Wesch, J.C.

    1988-03-28

    Diesel fuel cloud points can be predicted by an empirical equation developed by NOCA/Husky Research Corp. The equation can accurately predict cloud points from feedstock and product data readily available in the refinery. The applicability of the equation to a full range of summer, winter, and arctic diesel blends was proven by studies conducted on data from four Canadian refineries that process a wide variety of conventional crude oils and synthetic crude from bitumen. Results of the studies show that the variance between equation predicted and measured cloud point values are within acceptable reproducibility of measured data. Considerable time can be saved in the refinery when the equation is used for optimizing diesel fuel blend formulations. Applicability ranges from daily blending calculations, to use in linear programs for long-term planning for distillate utilization.

  13. 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.

  14. 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.

  15. PhotoCloud: Interactive remote exploration of joint 2D and 3D datasets.

    PubMed

    Brivio, Paolo; Benedetti, Luca; Tarini, Marco; Ponchio, Federico; Cignoni, Paolo; Scopigno, Roberto

    2013-01-01

    PhotoCloud is a real-time client-server system for interactive visualization and exploration of large datasets comprising thousands of calibrated 2D photographs of a scene and a complex 3D description of the scene. The system isn't tailored to any specific data acquisition process; it aims at generality and flexibility. PhotoCloud achieves scalability through a multiresolution dynamic hierarchical representation of the data, which is remotely stored and accessed by the client through an efficient cache system. The system includes a compact image browser and a multiresolution model renderer. PhotoCloud employs iconic visualization of the images in the 3D space and projects images onto the 3D scene on the fly. Users can navigate the 2D and 3D spaces with smooth, integrated, seamless transitions between them. A study with differently skilled users confirms PhotoCloud's effectiveness and communication power. The Web extras at http://www.youtube.com/playlist?list=PLHJB2bhmgB7cmYD0ST9CEDMRv1JlX4xPH are videos demonstrating PhotoCloud, a real-time client-server system for interactive exploration of large datasets comprising 2D photos and 3D models.

  16. 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…

  17. 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

  18. 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.

  19. A generic scheme for progressive point cloud coding.

    PubMed

    Huang, Yan; Peng, Jingliang; Kuo, C-C Jay; Gopi, M

    2008-01-01

    In this paper, we propose a generic point cloud encoder that provides a unified framework for compressing different attributes of point samples corresponding to 3D objects with arbitrary topology. In the proposed scheme, the coding process is led by an iterative octree cell subdivision of the object space. At each level of subdivision, positions of point samples are approximated by the geometry centers of all tree-front cells while normals and colors are approximated by their statistical average within each of tree-front cells. With this framework, we employ attribute-dependent encoding techniques to exploit different characteristics of various attributes. All of these have led to significant improvement in the rate-distortion (R-D) performance and a computational advantage over the state of the art. Furthermore, given sufficient levels of octree expansion, normal space partitioning and resolution of color quantization, the proposed point cloud encoder can be potentially used for lossless coding of 3D point clouds.

  20. An Emprical Point Error Model for Tls Derived Point Clouds

    NASA Astrophysics Data System (ADS)

    Ozendi, Mustafa; Akca, Devrim; Topan, Hüseyin

    2016-06-01

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

  1. 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.

  2. A 3D view of the outflow in the Orion Molecular Cloud 1 (OMC-1)

    NASA Astrophysics Data System (ADS)

    Nissen, H. D.; Cunningham, N. J.; Gustafsson, M.; Bally, J.; Lemaire, J.-L.; Favre, C.; Field, D.

    2012-04-01

    Context. Stars whose mass is an order of magnitude greater than the Sun play a prominent role in the evolution of galaxies, exploding as supernovae, triggering bursts of star formation and spreading heavy elements about their host galaxies. A fundamental aspect of star formation is the creation of an outflow. The fast outflow emerging from a region associated with massive star formation in the Orion Molecular Cloud 1 (OMC-1), located behind the Orion Nebula, appears to have been set in motion by an explosive event. Aims: We study the structure and dynamics of outflows in OMC-1. We combine radial velocity and proper motion data for near-IR emission of molecular hydrogen to obtain the first 3-dimensional (3D) structure of the OMC-1 outflow. Our work illustrates a new diagnostic tool for studies of star formation that will be exploited in the near future with the advent of high spatial resolution spectro-imaging in particular with data from the Atacama Large Millimeter Array (ALMA). Methods: We used published radial and proper motion velocities obtained from the shock-excited vibrational emission in the H2 v = 1-0 S(1) line at 2.122 μm obtained with the GriF instrument on the Canada-France-Hawaii Telescope, the Apache Point Observatory, the Anglo-Australian Observatory, and the Subaru Telescope. Results: These data give the 3D velocity of ejecta yielding a 3D reconstruction of the outflows. This allows one to view the material from different vantage points in space giving considerable insight into the geometry. Our analysis indicates that the ejection occurred ≲720 years ago from a distorted ring-like structure of ~15″ (6000 AU) in diameter centered on the proposed point of close encounter of the stars BN, source I and maybe also source n. We propose a simple model involving curvature of shock trajectories in magnetic fields through which the origin of the explosion and the center defined by extrapolated proper motions of BN, I and n may be brought into spatial

  3. 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.

  4. 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.

  5. 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.

  6. Precipitation Processes Developed During ARM (1997), TOGA COARE (1992) GATE (1974), SCSMEX (1998), and KWAJEX (1999): Consistent 3D, Semi-3D and 3D Cloud Resolving Model Simulations

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.; Hou, A.; Atlas, R.; Starr, D.; Sud, Y.

    2003-01-01

    Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D) have been used to study the response of clouds to large-scale forcing. IN these 3D simulators, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical clouds systems with large horizontal domains at the National Center of Atmospheric Research (NCAR) and at NASA Goddard Space Center. At Goddard, a 3D cumulus Ensemble (GCE) model was used to simulate periods during TOGA COARE, GATE, SCSMEX, ARM, and KWAJEX using a 512 by 512 km domain (with 2-km resolution). The result indicate that surface precipitation and latent heating profiles are very similar between the 2D and 3D GCE model simulation. The major objective of this paper are: (1) to assess the performance of the super-parametrization technique, (2) calculate and examine the surface energy (especially radiation) and water budget, and (3) identify the differences and similarities in the organization and entrainment rates of convection between simulated 2D and 3D cloud systems.

  7. Estimation of 3-D Cloud Effects on TOMS Satellite Retrieval of Surface UV Irradiance

    NASA Technical Reports Server (NTRS)

    Krotkov, Nickolay A.; Geogdzhayev, I.; Herman, J. R.

    1998-01-01

    To improve surface UV irradiance retrieval from the Total Ozone Mapping Spectrometer (TOMS) we simulate errors of the TOMS cloud correction algorithm for summertime broken cloud conditions. Cloud scenes (50 km by 50 km) are modeled by a normal random (Gaussian) field with a fixed lower boundary and conservative scattering. The model relates stochastic field characteristics with the cloud amount, mean cloud diameter and aspect ratio. Clouds are embedded into Rayleigh atmosphere with standard ozone profile. Radiative transfer calculations of the radiance at the top of the atmosphere and irradiance at the surface were performed using 3-D Monte Carlo (MC) code. The results are averaged over the satellite field of view on the surface (50 km by 50 km) and compared with TOMS predicted surface irradiance for the same scene reflectance. The TOMS algorithm assumes horizontally homogeneous Cl-type cloud between 3 km and 5.5 km. The effective optical depth is determined by fitting observed (MC) radiance at 380 nm. Having the same radiance at the satellite the homogeneous and broken cloud models predict different average irradiances at the surface. This is due to the differences in Bidirectional Reflection Distribution Function (BRDF) for homogeneous and broken cloud scenes with the same hemispherical albedo. For typical TOMS observational geometry at mid-latitudes the simulated single pixels errors may be as large as +/- 20%. Qualitatively these errors are due to the dominance of the non-horizontal cloud surfaces, which are not accounted for in the homogeneous cloud model. However, due to high variability of the real cloud shapes and types it is unclear how these single pixel errors would affect TOMS time-integrated UV exposure over extended periods (weeks to months) for different regions.

  8. 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

  9. Small-scale effects of underwater bubble clouds on ocean reflectance: 3-D modeling results.

    PubMed

    Piskozub, Jacek; Stramski, Dariusz; Terrill, Eric; Melville, W Kendall

    2009-07-01

    We examined the effect of individual bubble clouds on remote-sensing reflectance of the ocean with a 3-D Monte Carlo model of radiative transfer. The concentrations and size distribution of bubbles were defined based on acoustical measurements of bubbles in the surface ocean. The light scattering properties of bubbles for various void fractions were calculated using Mie scattering theory. We show how the spatial pattern, magnitude, and spectral behavior of remote-sensing reflectance produced by modeled bubble clouds change due to variations in their geometric and optical properties as well as the background optical properties of the ambient water. We also determined that for realistic sizes of bubble clouds, a plane-parallel horizontally homogeneous geometry (1-D radiative transfer model) is inadequate for modeling water-leaving radiance above the cloud.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Equisolid Fisheye Stereovision Calibration and Point Cloud Computation

    NASA Astrophysics Data System (ADS)

    Moreau, J.; Ambellouis, A.; Ruichek, Y.

    2013-10-01

    This paper deals with dense 3D point cloud computation of urban environments around a vehicle. The idea is to use two fisheye views to get 3D coordinates of the surrounding scene's points. The first contribution of this paper is the adaptation of an omnidirectional stereovision self-calibration algorithm to an equisolid fisheye projection model. The second contribution is the description of a new epipolar matching based on a scan-circle principle and a dynamic programming technique adapted for fisheye images. The method is validated using both synthetic images for which ground truth is available and real images of an urban scene.

  15. 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.

  16. 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.

  17. A Multiscale Constraints Method Localization of 3D Facial Feature Points

    PubMed Central

    Li, Hong-an; Zhang, Yongxin; Li, Zhanli; Li, Huilin

    2015-01-01

    It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms. PMID:26539244

  18. Deepstar 902 cloud point round robin

    SciTech Connect

    Williams, T.M.; Santamaria, M.M.

    1995-12-01

    Wax deposition is a problem in many oil fields and wells. Since wax precipitates and may cause deposition problems when the oil is cooled below its cloud point, an accurate method to determine an oil`s cloud point is needed. Samples of oil from three wells were collected and distributed to ten laboratories. The results indicate that cloud point temperature measurement is not as well defined as expected. The results of this study are discussed in this paper.

  19. Mobile viewer system for virtual 3D space using infrared LED point markers and camera

    NASA Astrophysics Data System (ADS)

    Sakamoto, Kunio; Taneji, Shoto

    2006-09-01

    The authors have developed a 3D workspace system using collaborative imaging devices. A stereoscopic display enables this system to project 3D information. In this paper, we describe the position detecting system for a see-through 3D viewer. A 3D display system is useful technology for virtual reality, mixed reality and augmented reality. We have researched spatial imaging and interaction system. We have ever proposed 3D displays using the slit as a parallax barrier, the lenticular screen and the holographic optical elements(HOEs) for displaying active image 1)2)3)4). The purpose of this paper is to propose the interactive system using these 3D imaging technologies. The observer can view virtual images in the real world when the user watches the screen of a see-through 3D viewer. The goal of our research is to build the display system as follows; when users see the real world through the mobile viewer, the display system gives users virtual 3D images, which is floating in the air, and the observers can touch these floating images and interact them such that kids can make play clay. The key technologies of this system are the position recognition system and the spatial imaging display. The 3D images are presented by the improved parallax barrier 3D display. Here the authors discuss the measuring method of the mobile viewer using infrared LED point markers and a camera in the 3D workspace (augmented reality world). The authors show the geometric analysis of the proposed measuring method, which is the simplest method using a single camera not the stereo camera, and the results of our viewer system.

  20. 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.

  1. 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.

  2. 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.

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

    NASA Astrophysics Data System (ADS)

    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.

    2016-08-01

    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.

  4. 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

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

    PubMed

    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.

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

    PubMed

    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

  7. 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.

  8. 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).

  9. 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.

  10. Melting points and chemical bonding properties of 3d transition metal elements

    NASA Astrophysics Data System (ADS)

    Takahara, Wataru

    2014-08-01

    The melting points of 3d transition metal elements show an unusual local minimal peak at manganese across Period 4 in the periodic table. The chemical bonding properties of scandium, titanium, vanadium, chromium, manganese, iron, cobalt, nickel and copper are investigated by the DV-Xα cluster method. The melting points are found to correlate with the bond overlap populations. The chemical bonding nature therefore appears to be the primary factor governing the melting points.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. Visibility analysis of point cloud in close range photogrammetry

    NASA Astrophysics Data System (ADS)

    Alsadik, B.; Gerke, M.; Vosselman, G.

    2014-05-01

    The ongoing development of advanced techniques in photogrammetry, computer vision (CV), robotics and laser scanning to efficiently acquire three dimensional geometric data offer new possibilities for many applications. The output of these techniques in the digital form is often a sparse or dense point cloud describing the 3D shape of an object. Viewing these point clouds in a computerized digital environment holds a difficulty in displaying the visible points of the object from a given viewpoint rather than the hidden points. This visibility problem is a major computer graphics topic and has been solved previously by using different mathematical techniques. However, to our knowledge, there is no study of presenting the different visibility analysis methods of point clouds from a photogrammetric viewpoint. The visibility approaches, which are surface based or voxel based, and the hidden point removal (HPR) will be presented. Three different problems in close range photogrammetry are presented: camera network design, guidance with synthetic images and the gap detection in a point cloud. The latter one introduces also a new concept of gap classification. Every problem utilizes a different visibility technique to show the valuable effect of visibility analysis on the final solution.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Interactive Cosmetic Makeup of a 3D Point-Based Face Model

    NASA Astrophysics Data System (ADS)

    Kim, Jeong-Sik; Choi, Soo-Mi

    We present an interactive system for cosmetic makeup of a point-based face model acquired by 3D scanners. We first enhance the texture of a face model in 3D space using low-pass Gaussian filtering, median filtering, and histogram equalization. The user is provided with a stereoscopic display and haptic feedback, and can perform simulated makeup tasks including the application of foundation, color makeup, and lip gloss. Fast rendering is achieved by processing surfels using the GPU, and we use a BSP tree data structure and a dynamic local refinement of the facial surface to provide interactive haptics. We have implemented a prototype system and evaluated its performance.

  1. 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

  2. Precipitation processes developed during TOGA COARE (1992), GATE (1974), SCSMEX (1998), and KWAJEX (1999): 3D Cloud Resolving Model Simulation

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.

    2006-01-01

    Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D). A few 3D CRMs have been used to study the response of clouds to large-scale forcing. In these 3D simulations, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical cloud systems with large horizontal domains at the National Center for Atmospheric Research (NCAR), NOAA GFDL, the U.K. Met. Office, Colorado State University and NASA Goddard Space Flight Center. An improved 3D Goddard Cumulus Ensemble (GCE) model was recently used to simulate periods during TOGA COARE (December 19-27, 1992), GATE (september 1-7, 1974), SCSMEX (May 18-26, June 2-11, 1998) and KWAJEX (August 7-13, August 18-21, and August 29-September 12, 1999) using a 512 by 512 km domain and 41 vertical layers. The major objectives of this paper are: (1) to identify the differences and similarities in the simulated precipitation processes and their associated surface and water energy budgets in TOGA COARE, GATE, KWAJEX, and SCSMEX, and (2) to asses the impact of microphysics, radiation budget and surface fluxes on the organization of convection in tropics.

  3. A method of improving structured light scanning point cloud using stereo image processing

    NASA Astrophysics Data System (ADS)

    Shi, Ruoming; Yu, Yong; Zhu, Ling

    2011-09-01

    Main defect of the structured light scanning is that the edge part is lost in the point clouds of scanned object. This research tried to combine the image processing method to a structured light system in order to improve the quality of the point cloud. The technique approaches are present, and the results are given as below: after overlying the edge part of the 3D model to the original point cloud from the structured light system, their hiatus can be restored and the resolution of the original point cloud can be improved.

  4. Pre-Processing of Point-Data from Contact and Optical 3D Digitization Sensors

    PubMed Central

    Budak, Igor; Vukelić, Djordje; Bračun, Drago; Hodolič, Janko; Soković, Mirko

    2012-01-01

    Contemporary 3D digitization systems employed by reverse engineering (RE) feature ever-growing scanning speeds with the ability to generate large quantity of points in a unit of time. Although advantageous for the quality and efficiency of RE modelling, the huge number of point datas can turn into a serious practical problem, later on, when the CAD model is generated. In addition, 3D digitization processes are very often plagued by measuring errors, which can be attributed to the very nature of measuring systems, various characteristics of the digitized objects and subjective errors by the operator, which also contribute to problems in the CAD model generation process. This paper presents an integral system for the pre-processing of point data, i.e., filtering, smoothing and reduction, based on a cross-sectional RE approach. In the course of the proposed system development, major emphasis was placed on the module for point data reduction, which was designed according to a novel approach with integrated deviation analysis and fuzzy logic reasoning. The developed system was verified through its application on three case studies, on point data from objects of versatile geometries obtained by contact and laser 3D digitization systems. The obtained results demonstrate the effectiveness of the system. PMID:22368513

  5. LiDAR Segmentation using Suitable Seed Points for 3D Building Extraction

    NASA Astrophysics Data System (ADS)

    Abdullah, S. M.; Awrangjeb, M.; Lu, G.

    2014-08-01

    Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof plane extraction. Firstly, the LiDAR point cloud is separated into "ground" and "non-ground" points based on the analysis of DEM with a height threshold. Each of the non-ground point is marked as coplanar or non-coplanar based on a coplanarity analysis. Commencing from the maximum LiDAR point height towards the minimum, all the LiDAR points on each height level are extracted and separated into several groups based on 2D distance. From each group, lines are extracted and a coplanar point which is the nearest to the midpoint of each line is considered as a seed point. This seed point and its neighbouring points are utilised to generate the plane equation. The plane is grown in a region growing fashion until no new points can be added. A robust rule-based tree removal method is applied subsequently to remove planar segments on trees. Four different rules are applied in this method. Finally, the boundary of each object is extracted from the segmented LiDAR point cloud. The method is evaluated with six different data sets consisting hilly and densely vegetated areas. The experimental results indicate that the proposed method offers a high building detection and roof plane extraction rates while compared to a recently proposed method.

  6. 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.

  7. Approximate registration of point clouds with large scale differences

    NASA Astrophysics Data System (ADS)

    Novak, D.; Schindler, K.

    2013-10-01

    3D reconstruction of objects is a basic task in many fields, including surveying, engineering, entertainment and cultural heritage. The task is nowadays often accomplished with a laser scanner, which produces dense point clouds, but lacks accurate colour information, and lacks per-point accuracy measures. An obvious solution is to combine laser scanning with photogrammetric recording. In that context, the problem arises to register the two datasets, which feature large scale, translation and rotation differences. The absence of approximate registration parameters (3D translation, 3D rotation and scale) precludes the use of fine-registration methods such as ICP. Here, we present a method to register realistic photogrammetric and laser point clouds in a fully automated fashion. The proposed method decomposes the registration into a sequence of simpler steps: first, two rotation angles are determined by finding dominant surface normal directions, then the remaining parameters are found with RANSAC followed by ICP and scale refinement. These two steps are carried out at low resolution, before computing a precise final registration at higher resolution.

  8. Precipitation Processes developed during ARM (1997), TOGA COARE (1992), GATE (1974), SCSMEX (1998), and KWAJEX (1999), Consistent 2D, semi-3D and 3D Cloud Resolving Model Simulations

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo; Hou, A.; Atlas, R.; Starr, D.; Sud, Y.

    2003-01-01

    Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D). A few 3D CRMs have been used to study the response of clouds to large-scale forcing. In these 3D simulations, the model domain was small, and the integration time was 6 hours. The major objectives of this paper are: (1) to assess the performance of the super-parameterization technique (i.e. is 2D or semi-3D CRM appropriate for the super-parameterization?); (2) calculate and examine the surface energy (especially radiation) and water budgets; (3) identify the differences and similarities in the organization and entrainment rates of convection between simulated 2D and 3D cloud systems.

  9. 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.

  10. 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

  11. SDTP: a robust method for interest point detection on 3D range images

    NASA Astrophysics Data System (ADS)

    Wang, Shandong; Gong, Lujin; Zhang, Hui; Zhang, Yongjie; Ren, Haibing; Rhee, Seon-Min; Lee, Hyong-Euk

    2013-12-01

    In fields of intelligent robots and computer vision, the capability to select a few points representing salient structures has always been focused and investigated. In this paper, we present a novel interest point detector for 3D range images, which can be used with good results in applications of surface registration and object recognition. A local shape description around each point in the range image is firstly constructed based on the distribution map of the signed distances to the tangent plane in its local support region. Using this shape description, the interest value is computed for indicating the probability of a point being the interest point. Lastly a Non-Maxima Suppression procedure is performed to select stable interest points on positions that have large surface variation in the vicinity. Our method is robust to noise, occlusion and clutter, which can be seen from the higher repeatability values compared with the state-of-the-art 3D interest point detectors in experiments. In addition, the method can be implemented easily and requires low computation time.

  12. Classification of Big Point Cloud Data Using Cloud Computing

    NASA Astrophysics Data System (ADS)

    Liu, K.; Boehm, J.

    2015-08-01

    Point cloud data plays an significant role in various geospatial applications as it conveys plentiful information which can be used for different types of analysis. Semantic analysis, which is an important one of them, aims to label points as different categories. In machine learning, the problem is called classification. In addition, processing point data is becoming more and more challenging due to the growing data volume. In this paper, we address point data classification in a big data context. The popular cluster computing framework Apache Spark is used through the experiments and the promising results suggests a great potential of Apache Spark for large-scale point data processing.

  13. 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.

  14. 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.

  15. 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.

  16. 3-D Numerical Modeling Perspectives on Lightning Generation in Volcanic Eruption Clouds

    NASA Astrophysics Data System (ADS)

    Van Eaton, A. R.; Behnke, S. A.; Herzog, M.

    2014-12-01

    Although numerous charging mechanisms have been implicated in the formation of volcanic lightning, recent insights from lightning mapping arrays indicate that vent charging (produced at or near the volcanic source) creates electrical discharges that are distinct from lightning initiated in the airborne plume during transport away from the vent. Previous work has suggested that turbulent structure and formation of hydrometeors, including rain, graupel and ash aggregates, are likely to play important roles in the plume charging process. We examine these phenomena with 3D large-eddy simulations of volcanic plume development that include cloud microphysics, using the Active Tracer High-resolution Atmospheric Model (ATHAM). Three relatively recent eruptions are targeted, each with different plume heights, degrees of wind interaction, and amounts of surface water interaction. We have compared the simulated evolution of turbulence and precipitation formation with data from lightning mapping arrays to address the following question - what can lightning tell us about the initiation and development of a volcanic plume in near-real time?

  17. 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.

  18. Methods for obtaining 3D training images for multiple-point statistics simulations: a comparative study

    NASA Astrophysics Data System (ADS)

    Jha, S. K.; Comunian, A.; Mariethoz, G.; Kelly, B. F.

    2013-12-01

    In recent years, multiple-point statistics (MPS) has been used in several studies for characterizing facies heterogeneity in geological formations. MPS uses a conceptual representation of the expected facies distribution, called a Training image (TI), to generate patterns of facies heterogeneity. In two-dimensional (2D) simulations the TI can be a hand-drawn image, an analogue outcrop image, or derived from geological reconstructions using a combination of geological analogues and geophysical data. However, obtaining suitable TI in three-dimensions (3D) from geological analogues or geophysical data is harder and has limited the use of MPS for simulating facies heterogeneity in 3D. There have been attempts to generate 3D training images using object-based simulation (OBS). However, determining suitable values for the large number of parameters required by OBS is often challenging. In this study, we compare two approaches for generating three-dimensional training images to model a valley filling sequence deposited by meandering rivers. The first approach is based on deriving statistical information from two-dimensional TIs. The 3D domain is simulated with a sequence of 2D MPS simulation steps, performed along different directions on slices of the 3D domain. At each 2D simulation step, the facies simulated at the previous steps that lie on the current 2D slice are used as conditioning data. The second approach uses hand-drawn two-dimensional TIs and produces complex patterns resembling the geological structures by applying rotation and affinity transformations in the facies simulation. The two techniques are compared using transition probabilities, facies proportions, and connectivity metrics. In the presentation we discuss the benefits of each approach for generating three-dimensional facies models.

  19. 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.

  20. 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

  1. Cloud-point determination for crude oils

    SciTech Connect

    Kruka, V.R.; Cadena, E.R.; Long, T.E.

    1995-08-01

    The cloud point represents the temperature at which wax or paraffin begins to precipitate from a hydrocarbon solution. Conventional American Soc. for Testing and Materials (ASTM) procedures for cloud-point determination are not applicable to dark crude oils and also do not account for potential subcooling of the wax. A review of possible methods and testing with several crude oils indicate that a reliable method consists of determining the temperature at which wax deposits begin to form on a cooled surface exposed to warm, flowing oil. A concurrent thermal analysis of the waxy hydrocarbon can indicate the presence of possible multiple wax-precipitation temperature regions in the solution.

  2. 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.

  3. Diesel cloud point determination needs uniformity

    SciTech Connect

    Tsang, C.Y.; Liew, V.; Miranda, R.

    1986-10-20

    Cloud point values for diesel fuels, measured by the existing ASTM method, can vary by as much as 8/sup 0/C. depending on the apparatus and technique used. These variations have an important effect on diesel-blending formulations. Research conducted at NOVA/Husky reveals cloud point discrepancies in results obtained by different laboratories testing the same fuel, and the impact of the discrepancies on blend ratios of distillates which, in turn, affect refinery economics. The major causes for the discrepancies, and recommended changes in test equipment specifications and test procedures to test the discrepancies, are presented in this article.

  4. 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

  5. Points based reconstruction and rendering of 3D shapes from large volume dataset

    NASA Astrophysics Data System (ADS)

    Zhao, Mingchang; Tian, Jie; He, Huiguang; Li, Guangming

    2003-05-01

    In the field of medical imaging, researchers often need visualize lots of 3D datasets to get the informaiton contained in these datasets. But the huge data genreated by modern medical imaging device challenge the real time processing and rendering algorithms at all the time. Spurring by the great achievement of Points Based Rendering (PBR) in the fields of computer graphics to render very large meshes, we propose a new algorithm to use the points as basic primitive of surface reconstruction and rendering to interactively reconstruct and render very large volume dataset. By utilizing the special characteristics of medical image datasets, we obtain a fast and efficient points-based reconstruction and rendering algorithm in common PC. The experimental results show taht this algorithm is feasible and efficient.

  6. 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.

  7. Precipitation Processes Developed During ARM (1997), TOGA COARE (1992), GATE (1974), SCSMEX (1998), and KWAJEX (1999): Consistent 2D, Semi-3D and 3D Cloud Resolving Model Simulations

    NASA Technical Reports Server (NTRS)

    Tao, W-K.

    2003-01-01

    Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D). A few 3D CRMs have been used to study the response of clouds to large-scale forcing. In these 3D simulations, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical cloud systems with large horizontal domains at the National Center for Atmospheric Research (NACAR) and at NASA Goddard Space Flight Center . At Goddard, a 3D Goddard Cumulus Ensemble (GCE) model was used to simulate periods during TOGA COARE, SCSMEX and KWAJEX using 512 by 512 km domain (with 2 km resolution). The results indicate that surface precipitation and latent heating profiles are very similar between the 2D and 3D GCE model simulations. The reason for the strong similarity between the 2D and 3D CRM simulations is that the same observed large-scale advective tendencies of potential temperature, water vapor mixing ratio, and horizontal momentum were used as the main focusing in both the 2D and 3D models. Interestingly, the 2D and 3D versions of the CRM used at CSU showed significant differences in the rainfall and cloud statistics for three ARM cases. The major objectives of this paper are: (1) to assess the performance of the super-parameterization technique, (2) calculate and examine the surface energy (especially radiation) and water budgets, and (3) identify the differences and similarities in the organization and entrainment rates of convection between simulated 2D and 3D cloud systems.

  8. 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.

  9. 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.

  10. Reconstruction, Quantification, and Visualization of Forest Canopy Based on 3d Triangulations of Airborne Laser Scanning Point Data

    NASA Astrophysics Data System (ADS)

    Vauhkonen, J.

    2015-03-01

    Reconstruction of three-dimensional (3D) forest canopy is described and quantified using airborne laser scanning (ALS) data with densities of 0.6-0.8 points m-2 and field measurements aggregated at resolutions of 400-900 m2. The reconstruction was based on computational geometry, topological connectivity, and numerical optimization. More precisely, triangulations and their filtrations, i.e. ordered sets of simplices belonging to the triangulations, based on the point data were analyzed. Triangulating the ALS point data corresponds to subdividing the underlying space of the points into weighted simplicial complexes with weights quantifying the (empty) space delimited by the points. Reconstructing the canopy volume populated by biomass will thus likely require filtering to exclude that volume from canopy voids. The approaches applied for this purpose were (i) to optimize the degree of filtration with respect to the field measurements, and (ii) to predict this degree by means of analyzing the persistent homology of the obtained triangulations, which is applied for the first time for vegetation point clouds. When derived from optimized filtrations, the total tetrahedral volume had a high degree of determination (R2) with the stem volume considered, both alone (R2=0.65) and together with other predictors (R2=0.78). When derived by analyzing the topological persistence of the point data and without any field input, the R2 were lower, but the predictions still showed a correlation with the field-measured stem volumes. Finally, producing realistic visualizations of a forested landscape using the persistent homology approach is demonstrated.

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

    PubMed

    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.

  12. Existence of two MHD reconnection modes in a solar 3D magnetic null point topology

    NASA Astrophysics Data System (ADS)

    Pariat, Etienne; Antiochos, Spiro; DeVore, C. Richard; Dalmasse, Kévin

    2012-07-01

    Magnetic topologies with a 3D magnetic null point are common in the solar atmosphere and occur at different spatial scales: such structures can be associated with some solar eruptions, with the so-called pseudo-streamers, and with numerous coronal jets. We have recently developed a series of numerical experiments that model magnetic reconnection in such configurations in order to study and explain the properties of jet-like features. Our model uses our state-of-the-art adaptive-mesh MHD solver ARMS. Energy is injected in the system by line-tied motion of the magnetic field lines in a corona-like configuration. We observe that, in the MHD framework, two reconnection modes eventually appear in the course of the evolution of the system. A very impulsive one, associated with a highly dynamic and fully 3D current sheet, is associated with the energetic generation of a jet. Before and after the generation of the jet, a quasi-steady reconnection mode, more similar to the standard 2D Sweet-Parker model, presents a lower global reconnection rate. We show that the geometry of the magnetic configuration influences the trigger of one or the other mode. We argue that this result carries important implications for the observed link between observational features such as solar jets, solar plumes, and the emission of coronal bright points.

  13. 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.

  14. 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…

  15. Saturation point structure of marine stratocumulus clouds

    NASA Technical Reports Server (NTRS)

    Boers, Reinout; Betts, Alan K.

    1988-01-01

    An investigation of the microstructure of a Pacific stratocumulus capped boundary layer is presented. A complex structure of three branches, identified using conserved variable diagrams, is found to correspond well to a conceptual model for the unstable, radiatively cooled cloud topped boundary layer. A simple conditional sampling method was used to identify saturation point pairs for ascending and descending branches of the internal boundary layer circulation. Results indicate a primary circulation scale of 5 km and provide a reasonable cloud top entrainment rate of 1 cm/s.

  16. 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.

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

    PubMed Central

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

    2011-01-01

    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®). Reference point dose rates and output factors were determined from in-air ionization chamber measurements for fields down to ∼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® 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 ∼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∼18% and ∼42% for the 2.5 mm and 1 mm fields, respectively. PRESAGE® 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 ∼72% for the 40 mm field, down to ∼55% for the 1 mm field. EBT and PRESAGE® PDDs agreed within ∼3% in the typical therapy region (1–4 cm). At deeper depths the EBT curves were slightly steeper (2.5% at 5 cm). These results indicate good overall consistency between ion-chamber, EBT

  18. 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.

  19. 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.

  20. Point-, line-, and plane-shaped cellular constructs for 3D tissue assembly.

    PubMed

    Morimoto, Yuya; Hsiao, Amy Y; Takeuchi, Shoji

    2015-12-01

    Microsized cellular constructs such as cellular aggregates and cell-laden hydrogel blocks are attractive cellular building blocks to reconstruct 3D macroscopic tissues with spatially ordered cells in bottom-up tissue engineering. In this regard, microfluidic techniques are remarkable methods to form microsized cellular constructs with high production rate and control of their shapes such as point, line, and plane. The fundamental shapes of the cellular constructs allow for the fabrication of larger arbitrary-shaped tissues by assembling them. This review introduces microfluidic formation methods of microsized cellular constructs and manipulation techniques to assemble them with control of their arrangements. Additionally, we show applications of the cellular constructs to biological studies and clinical treatments and discuss future trends as their potential applications.

  1. 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.

  2. 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.

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

    DOE PAGES

    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 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

  5. 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.

  6. Extensions developed for NOVA/Husky cloud point equation

    SciTech Connect

    Seglin, L. )

    1988-10-24

    Extensions have been developed for the NOVA/Husky cloud point equation that improve the determination of constants for the equation, and optimize the use of available distillate fractions for producing diesel fuels with acceptable cloud points.

  7. Is the 3-D magnetic null point with a convective electric field an efficient particle accelerator?

    NASA Astrophysics Data System (ADS)

    Guo, J.-N.; Büchner, J.; Otto, A.; Santos, J.; Marsch, E.; Gan, W.-Q.

    2010-04-01

    Aims: We study the particle acceleration at a magnetic null point in the solar corona, considering self-consistent magnetic fields, plasma flows and the corresponding convective electric fields. Methods: We calculate the electromagnetic fields by 3-D magnetohydrodynamic (MHD) simulations and expose charged particles to these fields within a full-orbit relativistic test-particle approach. In the 3-D MHD simulation part, the initial magnetic field configuration is set to be a potential field obtained by extrapolation from an analytic quadrupolar photospheric magnetic field with a typically observed magnitude. The configuration is chosen so that the resulting coronal magnetic field contains a null. Driven by photospheric plasma motion, the MHD simulation reveals the coronal plasma motion and the self-consistent electric and magnetic fields. In a subsequent test particle experiment the particle energies and orbits (determined by the forces exerted by the convective electric field and the magnetic field around the null) are calculated in time. Results: Test particle calculations show that protons can be accelerated up to 30 keV near the null if the local plasma flow velocity is of the order of 1000 km s-1 (in solar active regions). The final parallel velocity is much higher than the perpendicular velocity so that accelerated particles escape from the null along the magnetic field lines. Stronger convection electric field during big flare explosions can accelerate protons up to 2 MeV and electrons to 3 keV. Higher initial velocities can help most protons to be strongly accelerated, but a few protons also run the risk to be decelerated. Conclusions: Through its convective electric field and due to magnetic nonuniform drifts and de-magnetization process, the 3-D null can act as an effective accelerator for protons but not for electrons. Protons are more easily de-magnetized and accelerated than electrons because of their larger Larmor radii. Notice that macroscopic MHD

  8. Comparison of clinical bracket point registration with 3D laser scanner and coordinate measuring machine

    PubMed Central

    Nouri, Mahtab; Farzan, Arash; Baghban, Ali Reza Akbarzadeh; Massudi, Reza

    2015-01-01

    OBJECTIVE: The aim of the present study was to assess the diagnostic value of a laser scanner developed to determine the coordinates of clinical bracket points and to compare with the results of a coordinate measuring machine (CMM). METHODS: This diagnostic experimental study was conducted on maxillary and mandibular orthodontic study casts of 18 adults with normal Class I occlusion. First, the coordinates of the bracket points were measured on all casts by a CMM. Then, the three-dimensional coordinates (X, Y, Z) of the bracket points were measured on the same casts by a 3D laser scanner designed at Shahid Beheshti University, Tehran, Iran. The validity and reliability of each system were assessed by means of intraclass correlation coefficient (ICC) and Dahlberg's formula. RESULTS: The difference between the mean dimension and the actual value for the CMM was 0.0066 mm. (95% CI: 69.98340, 69.99140). The mean difference for the laser scanner was 0.107 ± 0.133 mm (95% CI: -0.002, 0.24). In each method, differences were not significant. The ICC comparing the two methods was 0.998 for the X coordinate, and 0.996 for the Y coordinate; the mean difference for coordinates recorded in the entire arch and for each tooth was 0.616 mm. CONCLUSION: The accuracy of clinical bracket point coordinates measured by the laser scanner was equal to that of CMM. The mean difference in measurements was within the range of operator errors. PMID:25741826

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

    NASA Astrophysics Data System (ADS)

    Salvaggio, Katie N.

    Geographically accurate scene models have enormous potential beyond that of just simple visualizations in regard to automated scene generation. In recent years, thanks to ever increasing computational efficiencies, there has been significant growth in both the computer vision and photogrammetry communities pertaining to automatic scene reconstruction from multiple-view imagery. The result of these algorithms is a three-dimensional (3D) point cloud which can be used to derive a final model using surface reconstruction techniques. However, the fidelity of these point clouds has not been well studied, and voids often exist within the point cloud. Voids exist in texturally difficult areas, as well as areas where multiple views were not obtained during collection, constant occlusion existed due to collection angles or overlapping scene geometry, or in regions that failed to triangulate accurately. It may be possible to fill in small voids in the scene using surface reconstruction or hole-filling techniques, but this is not the case with larger more complex voids, and attempting to reconstruct them using only the knowledge of the incomplete point cloud is neither accurate nor aesthetically pleasing. A method is presented for identifying voids in point clouds by using a voxel-based approach to partition the 3D space. By using collection geometry and information derived from the point cloud, it is possible to detect unsampled voxels such that voids can be identified. This analysis takes into account the location of the camera and the 3D points themselves to capitalize on the idea of free space, such that voxels that lie on the ray between the camera and point are devoid of obstruction, as a clear line of sight is a necessary requirement for reconstruction. Using this approach, voxels are classified into three categories: occupied (contains points from the point cloud), free (rays from the camera to the point passed through the voxel), and unsampled (does not contain points

  10. 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).

  11. 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.

  12. 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.

  13. 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.

  14. Study on the data matching of ground-based radar and laser point cloud

    NASA Astrophysics Data System (ADS)

    Qiu, Zhiwei; Wang, Chenxi; Yue, Jianping

    2016-07-01

    Due to the unique imaging approach for ground-based radar, identification and classification in observation area is very difficult. In order to improve the accuracy of the calculation and application combine with other data resource. it is necessary to implement data matching of radar images and 3D laser point cloud. First, the 3D cloud should to be transformed to orthographic maps, and then the horizontal rotation and orbit attitude angle parameters would be estimated for similarity transformation according to the characteristics such as common points and lines. Finally, the same reference point of the ground-based SAR data and cloud data is employed to accomplished in a two-dimensional coordinate system (called local common coordinate system).

  15. From Point Clouds to Architectural Models: Algorithms for Shape Reconstruction

    NASA Astrophysics Data System (ADS)

    Canciani, M.; Falcolini, C.; Saccone, M.; Spadafora, G.

    2013-02-01

    The use of terrestrial laser scanners in architectural survey applications has become more and more common. Row data complexity, as given by scanner restitution, leads to several problems about design and 3D-modelling starting from Point Clouds. In this context we present a study on architectural sections and mathematical algorithms for their shape reconstruction, according to known or definite geometrical rules, focusing on shapes of different complexity. Each step of the semi-automatic algorithm has been developed using Mathematica software and CAD, integrating both programs in order to reconstruct a geometrical CAD model of the object. Our study is motivated by the fact that, for architectural survey, most of three dimensional modelling procedures concerning point clouds produce superabundant, but often unnecessary, information and are also very expensive in terms of cpu time using more and more sophisticated hardware and software. On the contrary, it's important to simplify/decimate the point cloud in order to recognize a particular form out of some definite geometric/architectonic shapes. Such a process consists of several steps: first the definition of plane sections and characterization of their architecture; secondly the construction of a continuous plane curve depending on some parameters. In the third step we allow the selection on the curve of some nodal points with given specific characteristics (symmetry, tangency conditions, shadowing exclusion, corners, … ). The fourth and last step is the construction of a best shape defined by the comparison with an abacus of known geometrical elements, such as moulding profiles, leading to a precise architectonical section. The algorithms have been developed and tested in very different situations and are presented in a case study of complex geometries such as some mouldings profiles in the Church of San Carlo alle Quattro Fontane.

  16. 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.

  17. Observation of superconductivity induced by a point contact on 3D Dirac semimetal Cd3As2 crystals.

    PubMed

    Wang, He; Wang, Huichao; Liu, Haiwen; Lu, Hong; Yang, Wuhao; Jia, Shuang; Liu, Xiong-Jun; Xie, X C; Wei, Jian; Wang, Jian

    2016-01-01

    Three-dimensional (3D) Dirac semimetals, which possess 3D linear dispersion in the electronic structure as a bulk analogue of graphene, have lately generated widespread interest in both materials science and condensed matter physics. Recently, crystalline Cd3As2 has been proposed and proved to be a 3D Dirac semimetal that can survive in the atmosphere. Here, by using point contact spectroscopy measurements, we observe exotic superconductivity around the point contact region on the surface of Cd3As2 crystals. The zero-bias conductance peak (ZBCP) and double conductance peaks (DCPs) symmetric around zero bias suggest p-wave-like unconventional superconductivity. Considering the topological properties of 3D Dirac semimetals, our findings may indicate that Cd3As2 crystals under certain conditions could be topological superconductors, which are predicted to support Majorana zero modes or gapless Majorana edge/surface modes in the boundary depending on the dimensionality of the material.

  18. Observation of superconductivity induced by a point contact on 3D Dirac semimetal Cd3As2 crystals

    NASA Astrophysics Data System (ADS)

    Wang, He; Wang, Huichao; Liu, Haiwen; Lu, Hong; Yang, Wuhao; Jia, Shuang; Liu, Xiong-Jun; Xie, X. C.; Wei, Jian; Wang, Jian

    2016-01-01

    Three-dimensional (3D) Dirac semimetals, which possess 3D linear dispersion in the electronic structure as a bulk analogue of graphene, have lately generated widespread interest in both materials science and condensed matter physics. Recently, crystalline Cd3As2 has been proposed and proved to be a 3D Dirac semimetal that can survive in the atmosphere. Here, by using point contact spectroscopy measurements, we observe exotic superconductivity around the point contact region on the surface of Cd3As2 crystals. The zero-bias conductance peak (ZBCP) and double conductance peaks (DCPs) symmetric around zero bias suggest p-wave-like unconventional superconductivity. Considering the topological properties of 3D Dirac semimetals, our findings may indicate that Cd3As2 crystals under certain conditions could be topological superconductors, which are predicted to support Majorana zero modes or gapless Majorana edge/surface modes in the boundary depending on the dimensionality of the material.

  19. 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.

  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. Voxelization algorithms for geospatial applications: Computational methods for voxelating spatial datasets of 3D city models containing 3D surface, curve and point data models.

    PubMed

    Nourian, Pirouz; Gonçalves, Romulo; Zlatanova, Sisi; Ohori, Ken Arroyo; Vu Vo, Anh

    2016-01-01

    Voxel representations have been used for years in scientific computation and medical imaging. The main focus of our research is to provide easy access to methods for making large-scale voxel models of built environment for environmental modelling studies while ensuring they are spatially correct, meaning they correctly represent topological and semantic relations among objects. In this article, we present algorithms that generate voxels (volumetric pixels) out of point cloud, curve, or surface objects. The algorithms for voxelization of surfaces and curves are a customization of the topological voxelization approach [1]; we additionally provide an extension of this method for voxelization of point clouds. The developed software has the following advantages:•It provides easy management of connectivity levels in the resulting voxels.•It is not dependant on any external library except for primitive types and constructs; therefore, it is easy to integrate them in any application.•One of the algorithms is implemented in C++ and C for platform independence and efficiency.

  2. Voxelization algorithms for geospatial applications: Computational methods for voxelating spatial datasets of 3D city models containing 3D surface, curve and point data models.

    PubMed

    Nourian, Pirouz; Gonçalves, Romulo; Zlatanova, Sisi; Ohori, Ken Arroyo; Vu Vo, Anh

    2016-01-01

    Voxel representations have been used for years in scientific computation and medical imaging. The main focus of our research is to provide easy access to methods for making large-scale voxel models of built environment for environmental modelling studies while ensuring they are spatially correct, meaning they correctly represent topological and semantic relations among objects. In this article, we present algorithms that generate voxels (volumetric pixels) out of point cloud, curve, or surface objects. The algorithms for voxelization of surfaces and curves are a customization of the topological voxelization approach [1]; we additionally provide an extension of this method for voxelization of point clouds. The developed software has the following advantages:•It provides easy management of connectivity levels in the resulting voxels.•It is not dependant on any external library except for primitive types and constructs; therefore, it is easy to integrate them in any application.•One of the algorithms is implemented in C++ and C for platform independence and efficiency. PMID:27408832

  3. 3D dust clouds (Yukawa Balls) in strongly coupled dusty plasmas

    SciTech Connect

    Melzer, A.; Passvogel, M.; Miksch, T.; Ikkurthi, V. R.; Schneider, R.; Block, D.; Piel, A.

    2010-06-16

    Three-dimensional finite systems of charged dust particles confined to concentric spherical shells in a dusty plasma, so-called 'Yukawa balls', have been studied with respect to their static and dynamic properties. Here, we review the charging of particles in a dusty plasma discharge by computer simulations and the respective particle arrangements. The normal mode spectrum of Yukawa balls is measured from the 3D thermal Brownian motion of the dust particles around their equilibrium positions.

  4. D Point Cloud Model Colorization by Dense Registration of Digital Images

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

  5. Imaging open-path Fourier transform infrared spectrometer for 3D cloud profiling

    NASA Astrophysics Data System (ADS)

    Rentz Dupuis, Julia; Mansur, David J.; Vaillancourt, Robert; Carlson, David; Evans, Thomas; Schundler, Elizabeth; Todd, Lori; Mottus, Kathleen

    2009-05-01

    OPTRA is developing an imaging open-path Fourier transform infrared (I-OP-FTIR) spectrometer for 3D profiling of chemical and biological agent simulant plumes released into test ranges and chambers. An array of I-OP-FTIR instruments positioned around the perimeter of the test site, in concert with advanced spectroscopic algorithms, enables real time tomographic reconstruction of the plume. The approach is intended as a referee measurement for test ranges and chambers. This Small Business Technology Transfer (STTR) effort combines the instrumentation and spectroscopic capabilities of OPTRA, Inc. with the computed tomographic expertise of the University of North Carolina, Chapel Hill.

  6. 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

  7. Visualisation of Complex 3d City Models on Mobile Webbrowsers Using Cloud-Based Image Provisioning

    NASA Astrophysics Data System (ADS)

    Christen, M.; Nebiker, S.

    2015-08-01

    Rendering large city models with high polygon count and a vast amount of textures at interactive frame rates is a rather difficult to impossible task as it highly depends on the client hardware, which is often insufficient, even if out-of-core rendering techniques and level of detail approaches are used. Rendering complex city models on mobile devices is even more challenging. An approach of rendering and caching very large city models in the cloud using ray-tracing based image provisioning is introduced. This allows rendering large scenes efficiently, including on mobile devices. With this approach, it is possible to render cities with nearly unlimited number of polygons and textures.

  8. Integration of Image Data for Refining Building Boundaries Derived from Point Clouds

    NASA Astrophysics Data System (ADS)

    Perera, S. N.; Hetti Arachchige, N.; Schneider, D.

    2014-08-01

    Geometrically and topologically correct 3D building models are required to satisfy with new demands such as 3D cadastre, map updating, and decision making. More attention on building reconstruction has been paid using Airborne Laser Scanning (ALS) point cloud data. The planimetric accuracy of roof outlines, including step-edges is questionable in building models derived from only point clouds. This paper presents a new approach for the detection of accurate building boundaries by merging point clouds acquired by ALS and aerial photographs. It comprises two major parts: reconstruction of initial roof models from point clouds only, and refinement of their boundaries. A shortest closed circle (graph) analysis method is employed to generate building models in the first step. Having the advantages of high reliability, this method provides reconstruction without prior knowledge of primitive building types even when complex height jumps and various types of building roof are available. The accurate position of boundaries of the initial models is determined by the integration of the edges extracted from aerial photographs. In this process, scene constraints defined based on the initial roof models are introduced as the initial roof models are representing explicit unambiguous geometries about the scene. Experiments were conducted using the ISPRS benchmark test data. Based on test results, we show that the proposed approach can reconstruct 3D building models with higher geometrical (planimetry and vertical) and topological accuracy.

  9. 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.

  10. Imaging open-path Fourier transform infrared spectrometer for 3D cloud profiling

    NASA Astrophysics Data System (ADS)

    Rentz Dupuis, Julia; Mansur, David J.; Engel, James R.; Vaillancourt, Robert; Todd, Lori; Mottus, Kathleen

    2008-04-01

    OPTRA and University of North Carolina are developing an imaging open-path Fourier transform infrared (I-OP-FTIR) spectrometer for 3D profiling of chemical and biological agent simulant plumes released into test ranges and chambers. An array of I-OP-FTIR instruments positioned around the perimeter of the test site, in concert with advanced spectroscopic algorithms, enables real time tomographic reconstruction of the plume. The approach will be considered as a candidate referee measurement for test ranges and chambers. This Small Business Technology Transfer (STTR) effort combines the instrumentation and spectroscopic capabilities of OPTRA, Inc. with the computed tomographic expertise of the University of North Carolina, Chapel Hill. In this paper, we summarize progress to date and overall system performance projections based on the instrument, spectroscopy, and tomographic reconstruction accuracy. We then present a preliminary optical design of the I-OP-FTIR.

  11. Imaging open-path Fourier transform infrared spectrometer for 3D cloud profiling

    NASA Astrophysics Data System (ADS)

    Rentz Dupuis, Julia; Mansur, David J.; Vaillancourt, Robert; Carlson, David; Evans, Thomas; Schundler, Elizabeth; Todd, Lori; Mottus, Kathleen

    2010-04-01

    OPTRA has developed an imaging open-path Fourier transform infrared (I-OP-FTIR) spectrometer for 3D profiling of chemical and biological agent simulant plumes released into test ranges and chambers. An array of I-OP-FTIR instruments positioned around the perimeter of the test site, in concert with advanced spectroscopic algorithms, enables real time tomographic reconstruction of the plume. The approach is intended as a referee measurement for test ranges and chambers. This Small Business Technology Transfer (STTR) effort combines the instrumentation and spectroscopic capabilities of OPTRA, Inc. with the computed tomographic expertise of the University of North Carolina, Chapel Hill. In this paper, we summarize the design and build and detail system characterization and test of a prototype I-OP-FTIR instrument. System characterization includes radiometric performance and spectral resolution. Results from a series of tomographic reconstructions of sulfur hexafluoride plumes in a laboratory setting are also presented.

  12. Comparison of different techniques in optical trap for generating picokelvin 3D atom cloud in microgravity

    NASA Astrophysics Data System (ADS)

    Yao, Hepeng; Luan, Tian; Li, Chen; Zhang, Yin; Ma, Zhaoyuan; Chen, Xuzong

    2016-01-01

    Pursuing ultralow temperature 3D atom gas under microgravity conditions is one of the popular topics in the field of ultracold research. Many groups around the world are using, or are planning to use, delta-kick cooling (DKC) in microgravity. Our group has also proposed a two-stage crossed beam cooling (TSCBC) method that also provides a path to picokelvin temperatures. In this paper, we compare the characteristics of TSCBC and DKC for producing a picokelvin system in microgravity. Using a direct simulation Monte Carlo (DSMC) method, we simulate the cooling process of 87Rb using the two different cooling techniques. Under the same initial conditions, 87Rb can reach 7 pK in 15 s using TSCBC and 75 pK in 5.1 s with DKC. The simulation results show that TSCBC can reach lower temperatures compared with DKC, but needs more time and a more stable laser.

  13. Automated 3D Motion Tracking using Gabor Filter Bank, Robust Point Matching, and Deformable Models

    PubMed Central

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

    2013-01-01

    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

  14. 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.

  15. Precipitation Processes developed during ARM (1997), TOGA COARE(1992), GATE(1 974), SCSMEX(1998) and KWAJEX(1999): Consistent 2D and 3D Cloud Resolving Model Simulations

    NASA Technical Reports Server (NTRS)

    Tao, W.-K.; Shie, C.-H.; Simpson, J.; Starr, D.; Johnson, D.; Sud, Y.

    2003-01-01

    Real clouds and clouds systems are inherently three dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D). A few 3D CRMs have been used to study the response of clouds to large-scale forcing. In these 3D simulations, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical cloud system with large horizontal domains at the National Center for Atmospheric Research. The results indicate that surface precipitation and latent heating profiles are very similar between the 2D and 3D simulations of these same cases. The reason for the strong similarity between the 2D and 3D CRM simulations is that the observed large-scale advective tendencies of potential temperature, water vapor mixing ratio, and horizontal momentum were used as the main forcing in both the 2D and 3D models. Interestingly, the 2D and 3D versions of the CRM used in CSU and U.K. Met Office showed significant differences in the rainfall and cloud statistics for three ARM cases. The major objectives of this project are to calculate and axamine: (1)the surface energy and water budgets, (2) the precipitation processes in the convective and stratiform regions, (3) the cloud upward and downward mass fluxes in the convective and stratiform regions; (4) cloud characteristics such as size, updraft intensity and lifetime, and (5) the entrainment and detrainment rates associated with clouds and cloud systems that developed in TOGA COARE, GATE, SCSMEX, ARM and KWAJEX. Of special note is that the analyzed (model generated) data sets are all produced by the same current version of the GCE model, i.e. consistent model physics and configurations. Trajectory analyse and inert tracer calculation will be conducted to identify the differences and similarities in the organization of convection between simulated 2D and 3D cloud systems.

  16. Registration algorithm of point clouds based on multiscale normal features

    NASA Astrophysics Data System (ADS)

    Lu, Jun; Peng, Zhongtao; Su, Hang; Xia, GuiHua

    2015-01-01

    The point cloud registration technology for obtaining a three-dimensional digital model is widely applied in many areas. To improve the accuracy and speed of point cloud registration, a registration method based on multiscale normal vectors is proposed. The proposed registration method mainly includes three parts: the selection of key points, the calculation of feature descriptors, and the determining and optimization of correspondences. First, key points are selected from the point cloud based on the changes of magnitude of multiscale curvatures obtained by using principal components analysis. Then the feature descriptor of each key point is proposed, which consists of 21 elements based on multiscale normal vectors and curvatures. The correspondences in a pair of two point clouds are determined according to the descriptor's similarity of key points in the source point cloud and target point cloud. Correspondences are optimized by using a random sampling consistency algorithm and clustering technology. Finally, singular value decomposition is applied to optimized correspondences so that the rigid transformation matrix between two point clouds is obtained. Experimental results show that the proposed point cloud registration algorithm has a faster calculation speed, higher registration accuracy, and better antinoise performance.

  17. D Building Reconstruction from LIDAR Point Clouds by Adaptive Dual Contouring

    NASA Astrophysics Data System (ADS)

    Orthuber, E.; Avbelj, J.

    2015-03-01

    This paper presents a novel workflow for data-driven building reconstruction from Light Detection and Ranging (LiDAR) point clouds. The method comprises building extraction, a detailed roof segmentation using region growing with adaptive thresholds, segment boundary creation, and a structural 3D building reconstruction approach using adaptive 2.5D Dual Contouring. First, a 2D-grid is overlain on the segmented point cloud. Second, in each grid cell 3D vertices of the building model are estimated from the corresponding LiDAR points. Then, the number of 3D vertices is reduced in a quad-tree collapsing procedure, and the remaining vertices are connected according to their adjacency in the grid. Roof segments are represented by a Triangular Irregular Network (TIN) and are connected to each other by common vertices or - at height discrepancies - by vertical walls. Resulting 3D building models show a very high accuracy and level of detail, including roof superstructures such as dormers. The workflow is tested and evaluated for two data sets, using the evaluation method and test data of the "ISPRS Test Project on Urban Classification and 3D Building Reconstruction" (Rottensteiner et al., 2012). Results show that the proposed method is comparable with the state of the art approaches, and outperforms them regarding undersegmentation and completeness of the scene reconstruction.

  18. 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.

  19. Scan Profiles Based Method for Segmentation and Extraction of Planar Objects in Mobile Laser Scanning Point Clouds

    NASA Astrophysics Data System (ADS)

    Nguyen, Hoang Long; Belton, David; Helmholz, Petra

    2016-06-01

    The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

  20. 3D hybrid simulations of the interaction of a magnetic cloud with a bow shock

    NASA Astrophysics Data System (ADS)

    Turc, L.; Fontaine, D.; Savoini, P.; Modolo, R.

    2015-08-01

    In this paper, we investigate the interaction of a magnetic cloud (MC) with a planetary bow shock using hybrid simulations. It is the first time to our knowledge that this interaction is studied using kinetic simulations which include self-consistently both the ion foreshock and the shock wave dynamics. We show that when the shock is in a quasi-perpendicular configuration, the MC's magnetic structure in the magnetosheath remains similar to that in the solar wind, whereas it is strongly altered downstream of a quasi-parallel shock. The latter can result in a reversal of the magnetic field north-south component in some parts of the magnetosheath. We also investigate how the MC affects in turn the outer parts of the planetary environment, i.e., from the foreshock to the magnetopause. We find the following: (i) The decrease of the Alfvén Mach number at the MC's arrival causes an attenuation of the foreshock region because of the weakening of the bow shock. (ii) The foreshock moves along the bow shock's surface, following the rotation of the MC's magnetic field. (iii) Owing to the low plasma beta, asymmetric flows arise inside the magnetosheath, due to the magnetic tension force which accelerates the particles in some parts of the magnetosheath and slows them down in others. (iv) The quasi-parallel region forms a depression in the shock's surface. Other deformations of the magnetopause and the bow shock are also highlighted. All these effects can contribute to significantly modify the solar wind/magnetosphere coupling during MC events.

  1. 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

  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. Analysis of Point Cloud Generation from UAS Images

    NASA Astrophysics Data System (ADS)

    Ostrowski, S.; Jóźków, G.; Toth, C.; Vander Jagt, B.

    2014-11-01

    Unmanned Aerial Systems (UAS) allow for the collection of low altitude aerial images, along with other geospatial information from a variety of companion sensors. The images can then be processed using sophisticated algorithms from the Computer Vision (CV) field, guided by the traditional and established procedures from photogrammetry. Based on highly overlapped images, new software packages which were specifically developed for UAS technology can easily create ground models, such as Point Clouds (PC), Digital Surface Model (DSM), orthoimages, etc. The goal of this study is to compare the performance of three different software packages, focusing on the accuracy of the 3D products they produce. Using a Nikon D800 camera installed on an ocotocopter UAS platform, images were collected during subsequent field tests conducted over the Olentangy River, north from the Ohio State University campus. Two areas around bike bridges on the Olentangy River Trail were selected because of the challenge the packages would have in creating accurate products; matching pixels over the river and dense canopy on the shore presents difficult scenarios to model. Ground Control Points (GCP) were gathered at each site to tie the models to a local coordinate system and help assess the absolute accuracy for each package. In addition, the models were also relatively compared to each other using their PCs.

  4. 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.

  5. Combined Geometric/radiometric Point Cloud Matching for Shear Analysis

    NASA Astrophysics Data System (ADS)

    Gehrke, S.

    2012-07-01

    In the recent past, dense image matching methods such as Semi-Global Matching (SGM) became popular for many applications. The SGM approach has been adapted to and implemented for Leica ADS line-scanner data by North West Geomatics (North West) in co-operation with Leica Geosystems; it is used in North West's production workflow. One of the advantages of ADS imagery is the calibrated color information (RGB and near infrared), extending SGM-derived point clouds to dense "image point clouds" or, more general, information clouds (info clouds). With the goal of automating the quality control of ADS data, info clouds are utilized for Shear Analysis: Three-dimensional offsets of adjacent ADS image strips are determined from a pattern of info cloud pairs in strip overlaps by point cloud matching. The presented approach integrates geometry (height) and radiometry (intensity) information; matching is based on local point-to-plane distances for all points in a given cloud. The offset is derived in a least squares adjustment by applying it to each individual distance computation equation. Using intensities in addition to heights greatly benefits the offset computation, because intensity gradients tend to occur more frequently than height gradients. They can provide or complement the required information for the derivation of planimetric offset components. The paper details the combined geometric/radiometric point cloud matching approach and verifies the results against manual measurements.

  6. Global Calibration Method of a Camera Using the Constraint of Line Features and 3D World Points

    NASA Astrophysics Data System (ADS)

    Xu, Guan; Zhang, Xinyuan; Li, Xiaotao; Su, Jian; Hao, Zhaobing

    2016-08-01

    We present a reliable calibration method using the constraint of 2D projective lines and 3D world points to elaborate the accuracy of the camera calibration. Based on the relationship between the 3D points and the projective plane, the constraint equations of the transformation matrix are generated from the 3D points and 2D projective lines. The transformation matrix is solved by the singular value decomposition. The proposed method is compared with the point-based calibration to verify the measurement validity. The mean values of the root-mean-square errors using the proposed method are 7.69×10-4, 6.98×10-4, 2.29×10-4, and 1.09×10-3 while the ones of the original method are 8.10×10-4, 1.29×10-2, 2.58×10-2, and 8.12×10-3. Moreover, the average logarithmic errors of the calibration method are evaluated and compared with the former method in different Gaussian noises and projective lines. The variances of the average errors using the proposed method are 1.70×10-5, 1.39×10-4, 1.13×10-4, and 4.06×10-4, which indicates the stability and accuracy of the method.

  7. 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

  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. 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

  10. 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.

  11. 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

  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

    2016-01-01

    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. 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.

  14. Computer-aided determination of occlusal contact points for dental 3-D CAD.

    PubMed

    Maruyama, Tomoaki; Nakamura, Yasuo; Hayashi, Toyohiko; Kato, Kazumasa

    2006-05-01

    Present dental CAD systems enable us to design functional occlusal tooth surfaces which harmonize with the patient's stomatognathic function. In order to avoid occlusal interferences during tooth excursions, currently available systems usually use the patient's functional occlusal impressions for the design of occlusal contact points. Previous interfere-free design, however, has been done on a trial-and-error basis by using visual inspection. To improve this time-consuming procedure, this paper proposes a computer-aided system for assisting in the determination of the occlusal contact points by visualizing the appropriate regions of the opposing surface. The system can designate such regions from data of the opposing occlusal surfaces and their relative movements can be simulated by using a virtual articulator. Experiments for designing the crown of a lower first molar demonstrated that all contact points selected within the designated regions completely satisfied the required contact or separation during tooth excursions, confirming the effectiveness of our computer-aided procedure.

  15. 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.

  16. Ice formation in Arctic mixed-phase clouds: Insights from a 3-D cloud-resolving model with size-resolved aerosol and cloud microphysics

    NASA Astrophysics Data System (ADS)

    Fan, Jiwen; Ovtchinnikov, Mikhail; Comstock, Jennifer M.; McFarlane, Sally A.; Khain, Alexander

    2009-02-01

    The single-layer mixed-phase clouds observed during the Atmospheric Radiation Measurement (ARM) program's Mixed-Phase Arctic Cloud Experiment (MPACE) are simulated with a three-dimensional cloud-resolving model, the System for Atmospheric Modeling (SAM), coupled with an explicit bin microphysics scheme and a radar simulator. By implementing an aerosol-dependent and a temperature- and supersaturation-dependent ice nucleation scheme and treating IN size distribution prognostically, the link between ice crystal and aerosol properties is established to study aerosol indirect effects. Two possible ice enhancement mechanisms, activation of droplet evaporation residues by condensation followed by freezing and droplet evaporation freezing by contact freezing inside out, are scrutinized by extensive comparisons with the in situ and remote sensing measurements. Simulations with either mechanism agree well with the in situ and remote sensing measurements of ice microphysical properties but liquid water content is slightly underpredicted. These two mechanisms give similar cloud properties, although ice nucleation occurs at very different rates and locations. Ice nucleation from activation of evaporation nuclei occurs mostly near cloud top areas, while ice nucleation from the drop freezing during evaporation has no significant location preference. Both ice enhancement mechanisms contribute dramatically to ice formation with ice particle concentration of 10-15 times higher relative to the simulation without either of them. Ice nuclei (IN) recycling from ice sublimation contributes significantly to maintaining concentrations of IN and ice particles in this case, implying an important role to maintain the observed long-term existence of mixed-phase clouds. Cloud can be very sensitive to IN initially but become much less sensitive as cloud evolves to a steady mixed-phase condition.

  17. 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...

  18. 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.

  19. 3D modelling of the early martian climate under a denser CO2 atmosphere: Temperatures and CO2 ice clouds

    NASA Astrophysics Data System (ADS)

    Forget, F.; Wordsworth, R.; Millour, E.; Madeleine, J.-B.; Kerber, L.; Leconte, J.; Marcq, E.; Haberle, R. M.

    2013-01-01

    On the basis of geological evidence, it is often stated that the early martian climate was warm enough for liquid water to flow on the surface thanks to the greenhouse effect of a thick atmosphere. We present 3D global climate simulations of the early martian climate performed assuming a faint young Sun and a CO2 atmosphere with surface pressure between 0.1 and 7 bars. The model includes a detailed radiative transfer model using revised CO2 gas collision induced absorption properties, and a parameterisation of the CO2 ice cloud microphysical and radiative properties. A wide range of possible climates is explored using various values of obliquities, orbital parameters, cloud microphysic parameters, atmospheric dust loading, and surface properties. Unlike on present day Mars, for pressures higher than a fraction of a bar, surface temperatures vary with altitude because of the adiabatic cooling and warming of the atmosphere when it moves vertically. In most simulations, CO2 ice clouds cover a major part of the planet. Previous studies had suggested that they could have warmed the planet thanks to their scattering greenhouse effect. However, even assuming parameters that maximize this effect, it does not exceed +15 K. Combined with the revised CO2 spectroscopy and the impact of surface CO2 ice on the planetary albedo, we find that a CO2 atmosphere could not have raised the annual mean temperature above 0 °C anywhere on the planet. The collapse of the atmosphere into permanent CO2 ice caps is predicted for pressures higher than 3 bar, or conversely at pressure lower than 1 bar if the obliquity is low enough. Summertime diurnal mean surface temperatures above 0 °C (a condition which could have allowed rivers and lakes to form) are predicted for obliquity larger than 40° at high latitudes but not in locations where most valley networks or layered sedimentary units are observed. In the absence of other warming mechanisms, our climate model results are thus consistent

  20. 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.

  1. Design point variation of 3-D loss and deviation for axial compressor middle stages

    NASA Technical Reports Server (NTRS)

    Roberts, William B.; Serovy, George K.; Sandercock, Donald M.

    1988-01-01

    The available data on middle-stage research compressors operating near design point are used to derive simple empirical models for the spanwise variation of three-dimensional viscous loss coefficients for middle-stage axial compressor blading. The models make it possible to quickly estimate the total loss and deviation across the blade span when the three-dimensional distribution is superimposed on the two-dimensional variation calculated for each blade element. It is noted that extrapolated estimates should be used with caution since the correlations have been derived from a limited data base.

  2. 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

  3. Absence of Critical Points of Solutions to the Helmholtz Equation in 3D

    NASA Astrophysics Data System (ADS)

    Alberti, Giovanni S.

    2016-11-01

    The focus of this paper is to show the absence of critical points for the solutions to the Helmholtz equation in a bounded domain {Ωsubset{R}3} , given by { div(a nabla u_{ω}g)-ω qu_{ω}g=0&quad{in Ω,} u_{ω}g=g quad{on partialΩ.} We prove that for an admissible g there exists a finite set of frequencies K in a given interval and an open cover {overline{Ω}=\\cup_{ωin K} Ω_{ω}} such that {|nabla u_{ω}g(x)| > 0} for every {ωin K} and {xinΩ_{ω}} . The set K is explicitly constructed. If the spectrum of this problem is simple, which is true for a generic domain {Ω} , the admissibility condition on g is a generic property.

  4. 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.

  5. Binocular and Monocular Depth Cues in Online Feedback Control of 3-D Pointing Movement

    PubMed Central

    Hu, Bo; Knill, David C.

    2012-01-01

    Previous work has shown that humans continuously use visual feedback of the hand to control goal-directed movements online. In most studies, visual error signals were predominantly in the image plane and thus were available in an observer’s retinal image. We investigate how humans use visual feedback about finger depth provided by binocular and monocular depth cues to control pointing movements. When binocularly viewing a scene in which the hand movement was made in free space, subjects were about 60 ms slower in responding to perturbations in depth than in the image plane. When monocularly viewing a scene designed to maximize the available monocular cues to finger depth (motion, changing size and cast shadows), subjects showed no response to perturbations in depth. Thus, binocular cues from the finger are critical to effective online control of hand movements in depth. An optimal feedback controller that takes into account of the low peripheral stereoacuity and inherent ambiguity in cast shadows can explain the difference in response time in the binocular conditions and lack of response in monocular conditions. PMID:21724567

  6. A cloud chemistry module for the 3-D cloud-resolving mesoscale model Meso-NH with application to idealized cases

    NASA Astrophysics Data System (ADS)

    Leriche, M.; Pinty, J.-P.; Mari, C.; Gazen, D.

    2013-08-01

    efficiency of gases in the ice phase. The 2-D and 3-D simulations illustrate that the retention in ice of a moderately soluble gas such as formaldehyde substantially decreases its concentration in the upper troposphere. In these simulations, retention of highly soluble species in the ice phase significantly increased the wet deposition rates.

  7. A cloud chemistry module for the 3-D cloud-resolving mesoscale model Meso-NH with application to idealized cases

    NASA Astrophysics Data System (ADS)

    Leriche, M.; Pinty, J.-P.; Mari, C.; Gazen, D.

    2013-02-01

    efficiency of gases in the ice phase. The 2-D and 3-D simulations illustrate that the retention in ice of a moderately soluble gas such as formaldehyde substantially decreases its concentration in the upper troposphere. In these simulations, retention of highly soluble species in the ice phase significantly increased the wet deposition rates.

  8. 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

  9. 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.

  10. Tunnel Point Cloud Filtering Method Based on Elliptic Cylindrical Model

    NASA Astrophysics Data System (ADS)

    Zhua, Ningning; Jiaa, Yonghong; Luo, Lun

    2016-06-01

    The large number of bolts and screws that attached to the subway shield ring plates, along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls, make the laser point cloud data include lots of non-tunnel section points (hereinafter referred to as non-points), therefore affecting the accuracy for modeling and deformation monitoring. This paper proposed a filtering method for the point cloud based on the elliptic cylindrical model. The original laser point cloud data was firstly projected onto a horizontal plane, and a searching algorithm was given to extract the edging points of both sides, which were used further to fit the tunnel central axis. Along the axis the point cloud was segmented regionally, and then fitted as smooth elliptic cylindrical surface by means of iteration. This processing enabled the automatic filtering of those inner wall non-points. Experiments of two groups showed coincident results, that the elliptic cylindrical model based method could effectively filter out the non-points, and meet the accuracy requirements for subway deformation monitoring. The method provides a new mode for the periodic monitoring of tunnel sections all-around deformation in subways routine operation and maintenance.

  11. 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

  12. 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…

  13. 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.

  14. a Semi-Automatic Procedure for Texturing of Laser Scanning Point Clouds with Google Streetview Images

    NASA Astrophysics Data System (ADS)

    Lichtenauer, J. F.; Sirmacek, B.

    2015-08-01

    We introduce a method to texture 3D urban models with photographs that even works for Google Streetview images and can be done with currently available free software. This allows realistic texturing, even when it is not possible or cost-effective to (re)visit a scanned site to take textured scans or photographs. Mapping a photograph onto a 3D model requires knowledge of the intrinsic and extrinsic camera parameters. The common way to obtain intrinsic parameters of a camera is by taking several photographs of a calibration object with a priori known structure. The extra challenge of using images from a database such as Google Streetview, rather than your own photographs, is that it does not allow for any controlled calibration. To overcome this limitation, we propose to calibrate the panoramic viewer of Google Streetview using Structure from Motion (SfM) on any structure of which Google Streetview offers views from multiple angles. After this, the extrinsic parameters for any other view can be calculated from 3 or more tie points between the image from Google Streetview and a 3D model of the scene. These point correspondences can either be obtained automatically or selected by manual annotation. We demonstrate how this procedure provides realistic 3D urban models in an easy and effective way, by using it to texture a publicly available point cloud from a terrestrial laser scan made in Bremen, Germany, with a screenshot from Google Streetview, after estimating the focal length from views from Paris, France.

  15. Extracting geometry information from point cloud of urban building

    NASA Astrophysics Data System (ADS)

    Yang, Changqiang; Ye, Zetian

    2009-10-01

    The main purpose of the algorithm in this paper is to extract geometry information (mainly includes the plane function, the outlines and the corners of the building surfaces) from the point cloud. The main process includes: filter and sort the point cloud, get fitting lines in each column and merge the fitting lines, get fitting faces from the fitting lines and merge the fitting faces, extract the outlines and corners of the merged faces. The advantage of the algorithm includes: A face's adjacent faces can be got, the disturbing points can be automatically removed and the computation amount is relatively small. The algorithm is particularly suit for the point cloud got by vehicle-borne laser scan.

  16. Automatic Creation of Structural Models from Point Cloud Data: the Case of Masonry Structures

    NASA Astrophysics Data System (ADS)

    Riveiro, B.; Conde-Carnero, B.; González-Jorge, H.; Arias, P.; Caamaño, J. C.

    2015-08-01

    One of the fields where 3D modelling has an important role is in the application of such 3D models to structural engineering purposes. The literature shows an intense activity on the conversion of 3D point cloud data to detailed structural models, which has special relevance in masonry structures where geometry plays a key role. In the work presented in this paper, color data (from Intensity attribute) is used to automatically segment masonry structures with the aim of isolating masonry blocks and defining interfaces in an automatic manner using a 2.5D approach. An algorithm for the automatic processing of laser scanning data based on an improved marker-controlled watershed segmentation was proposed and successful results were found. Geometric accuracy and resolution of point cloud are constrained by the scanning instruments, giving accuracy levels reaching a few millimetres in the case of static instruments and few centimetres in the case of mobile systems. In any case, the algorithm is not significantly sensitive to low quality images because acceptable segmentation results were found in cases where blocks could not be visually segmented.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 3-D seismic over the Fausse Pointe Field: A case history of acquisition in a harsh environment

    SciTech Connect

    Duncan, P.M.; Nester, D.C.; Martin, J.A.; Moles, J.R.

    1995-12-31

    A 50 square mile 3D seismic survey was successfully acquired over Fausse Point Field in the latter half of 1994. The geophysical and logistical challenges of this project were immense. The steep dips and extensive range of target depths required a large shoot area with a relatively fine sampling interval. The surface, while essentially flat, included areas of cane field, crawfish ponds, thick brush, swamp, open lakes and deep canals -- all typical of southern Louisiana. Planning and permitting of the survey began in late 1993. Field operations began in June 1994 and were complete in January 1995. Field personnel numbered 150 at the peak of operations. More than 19,000 crew hours were required to complete the job at a cost of over 5,000,000. The project was complete on time and on budget. The resulting images of the salt dome and surrounding rocks are not only beautiful but are revealing many opportunities for new hydrocarbon development.

  2. Coarse Point Cloud Registration by Egi Matching of Voxel Clusters

    NASA Astrophysics Data System (ADS)

    Wang, Jinhu; Lindenbergh, Roderik; Shen, Yueqian; Menenti, Massimo

    2016-06-01

    Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

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

    PubMed

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

    2012-11-01

    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.

  4. 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.

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

    PubMed

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

    2012-11-01

    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. PMID:23187409

  6. Measurement of cloud point temperature in polymer solutions.

    PubMed

    Mannella, G A; La Carrubba, V; Brucato, V

    2013-07-01

    A temperature-controlled turbidity measurement apparatus for the characterization of polymer solutions has been instrumented and set up. The main features are the coupled temperature-light transmittance measurement and the accurate temperature control, achieved by means of peltier cells. The apparatus allows to measure cloud point temperatures by adopting different cooling protocols: low rate for quasi-equilibrium measurements and high rate for detect kinetic effects. A ternary polymeric solution was adopted as case study system showing that cooling rate affects the measured cloud point temperature.

  7. 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

  8. Image-based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET.

    PubMed

    Rapisarda, E; Bettinardi, V; Thielemans, K; Gilardi, M C

    2010-07-21

    The interest in positron emission tomography (PET) and particularly in hybrid integrated PET/CT systems has significantly increased in the last few years due to the improved quality of the obtained images. Nevertheless, one of the most important limits of the PET imaging technique is still its poor spatial resolution due to several physical factors originating both at the emission (e.g. positron range, photon non-collinearity) and at detection levels (e.g. scatter inside the scintillating crystals, finite dimensions of the crystals and depth of interaction). To improve the spatial resolution of the images, a possible way consists of measuring the point spread function (PSF) of the system and then accounting for it inside the reconstruction algorithm. In this work, the system response of the GE Discovery STE operating in 3D mode has been characterized by acquiring (22)Na point sources in different positions of the scanner field of view. An image-based model of the PSF was then obtained by fitting asymmetric two-dimensional Gaussians on the (22)Na images reconstructed with small pixel sizes. The PSF was then incorporated, at the image level, in a three-dimensional ordered subset maximum likelihood expectation maximization (OS-MLEM) reconstruction algorithm. A qualitative and quantitative validation of the algorithm accounting for the PSF has been performed on phantom and clinical data, showing improved spatial resolution, higher contrast and lower noise compared with the corresponding images obtained using the standard OS-MLEM algorithm.

  9. Image-based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET

    NASA Astrophysics Data System (ADS)

    Rapisarda, E.; Bettinardi, V.; Thielemans, K.; Gilardi, M. C.

    2010-07-01

    The interest in positron emission tomography (PET) and particularly in hybrid integrated PET/CT systems has significantly increased in the last few years due to the improved quality of the obtained images. Nevertheless, one of the most important limits of the PET imaging technique is still its poor spatial resolution due to several physical factors originating both at the emission (e.g. positron range, photon non-collinearity) and at detection levels (e.g. scatter inside the scintillating crystals, finite dimensions of the crystals and depth of interaction). To improve the spatial resolution of the images, a possible way consists of measuring the point spread function (PSF) of the system and then accounting for it inside the reconstruction algorithm. In this work, the system response of the GE Discovery STE operating in 3D mode has been characterized by acquiring 22Na point sources in different positions of the scanner field of view. An image-based model of the PSF was then obtained by fitting asymmetric two-dimensional Gaussians on the 22Na images reconstructed with small pixel sizes. The PSF was then incorporated, at the image level, in a three-dimensional ordered subset maximum likelihood expectation maximization (OS-MLEM) reconstruction algorithm. A qualitative and quantitative validation of the algorithm accounting for the PSF has been performed on phantom and clinical data, showing improved spatial resolution, higher contrast and lower noise compared with the corresponding images obtained using the standard OS-MLEM algorithm.

  10. 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.

  11. Continuum Limit of Total Variation on Point Clouds

    NASA Astrophysics Data System (ADS)

    García Trillos, Nicolás; Slepčev, Dejan

    2016-04-01

    We consider point clouds obtained as random samples of a measure on a Euclidean domain. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. Our goal is to develop mathematical tools needed to study the consistency, as the number of available data points increases, of graph-based machine learning algorithms for tasks such as clustering. In particular, we study when the cut capacity, and more generally total variation, on these graphs is a good approximation of the perimeter (total variation) in the continuum setting. We address this question in the setting of Γ-convergence. We obtain almost optimal conditions on the scaling, as the number of points increases, of the size of the neighborhood over which the points are connected by an edge for the Γ-convergence to hold. Taking of the limit is enabled by a transportation based metric which allows us to suitably compare functionals defined on different point clouds.

  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. 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.

  14. 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.

  15. 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

  16. Classification of mobile terrestrial laser point clouds using semantic constraints

    NASA Astrophysics Data System (ADS)

    Pu, Shi; Zhan, Qingming

    2009-08-01

    With mobile terrestrial laser scanning, laser point clouds of large urban areas can be acquainted rapidly during normal speed driving. Classification of the laser points is beneficial to the city reconstruction from laser point cloud, but a manual classification process can be rather time-consuming due to the huge amount of laser points. Although the pulse return is often used to automate classification, it is only possible to distinguish limited types such as vegetation and ground. In this paper we present a new method which classifies mobile terrestrial laser point clouds using only coordinate information. First, a point of a whole urban scene is segmented, and geometric properties of each segment are computed. Then semantic constraints for several object types are derived from observation and knowledge. These constraints concern not only geometric properties of the semantic objects, but also regulate the topological and hierarchical relations between objects. A search tree is formulated from the semantic constraints and applied to the laser segments for interpretation. 2D map can provide the approximate locations of the buildings and roads as well as the roads' dominant directions, so it is integrated to reduce the search space. The applicability of this method is demonstrated with a Lynx data of the city Enschede and a Streetmapper data of the city Esslingen. Four object types: ground, road, building façade, and traffic symbols, are classified in these data sets.

  17. Thick fibrous composite reinforcements behave as special second-gradient materials: three-point bending of 3D interlocks

    NASA Astrophysics Data System (ADS)

    Madeo, Angela; Ferretti, Manuel; dell'Isola, Francesco; Boisse, Philippe

    2015-08-01

    In this paper, we propose to use a second gradient, 3D orthotropic model for the characterization of the mechanical behavior of thick woven composite interlocks. Such second-gradient theory is seen to directly account for the out-of-plane bending rigidity of the yarns at the mesoscopic scale which is, in turn, related to the bending stiffness of the fibers composing the yarns themselves. The yarns' bending rigidity evidently affects the macroscopic bending of the material and this fact is revealed by presenting a three-point bending test on specimens of composite interlocks. These specimens differ one from the other for the different relative direction of the yarns with respect to the edges of the sample itself. Both types of specimens are independently seen to take advantage of a second-gradient modeling for the correct description of their macroscopic bending modes. The results presented in this paper are essential for the setting up of a correct continuum framework suitable for the mechanical characterization of composite interlocks. The few second-gradient parameters introduced by the present model are all seen to be associated with peculiar deformation modes of the mesostructure (bending of the yarns) and are determined by inverse approach. Although the presented results undoubtedly represent an important step toward the complete characterization of the mechanical behavior of fibrous composite reinforcements, more complex hyperelastic second-gradient constitutive laws must be conceived in order to account for the description of all possible mesostructure-induced deformation patterns.

  18. 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.

  19. 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. PMID:25372707

  20. Road traffic sign detection and classification from mobile LiDAR point clouds

    NASA Astrophysics Data System (ADS)

    Weng, Shengxia; Li, Jonathan; Chen, Yiping; Wang, Cheng

    2016-03-01

    Traffic signs are important roadway assets that provide valuable information of the road for drivers to make safer and easier driving behaviors. Due to the development of mobile mapping systems that can efficiently acquire dense point clouds along the road, automated detection and recognition of road assets has been an important research issue. This paper deals with the detection and classification of traffic signs in outdoor environments using mobile light detection and ranging (Li- DAR) and inertial navigation technologies. The proposed method contains two main steps. It starts with an initial detection of traffic signs based on the intensity attributes of point clouds, as the traffic signs are always painted with highly reflective materials. Then, the classification of traffic signs is achieved based on the geometric shape and the pairwise 3D shape context. Some results and performance analyses are provided to show the effectiveness and limits of the proposed method. The experimental results demonstrate the feasibility and effectiveness of the proposed method in detecting and classifying traffic signs from mobile LiDAR point clouds.

  1. 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.

  2. 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.

  3. 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].

  4. 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.

  5. 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.

  6. Automatic differentiation as a tool for sensitivity analysis of a convective storm in a 3-D cloud model

    SciTech Connect

    Park, S.K.; Droegemeier, K.K.; Bischof, C.H.

    1996-10-01

    The ADIFOR automatic differentiation tool is applied to a 3-D storm-scale meteorological model to generate a sensitivity-enhanced code capable of providing derivatives of all model output variables and related diagnostic (derived) parameters as a function of specified control parameters. The tangent linear approximation, applied to a deep convective storm by the first of its kind using a full-physics compressible model, is valid up to 50 min for a 1% water vapor perturbations. The result is very encouraging considering the highly nonlinear and discontinuous properties of solutions. The ADIFOR-generated code has provided valuable sensitivity information on storm dynamics. Especially, it is very efficient and useful for investigating how a perturbation inserted at earlier time propagates through the model variables at later times. However, it is computationally very expensive to be applied to the variational data assimilation, especially for 3-D meteorological models, which potentially have a large number of input variables.

  7. 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.

  8. Rapid Inspection of Pavement Markings Using Mobile LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Zhang, Haocheng; Li, Jonathan; Cheng, Ming; Wang, Cheng

    2016-06-01

    This study aims at building a robust semi-automated pavement marking extraction workflow based on the use of mobile LiDAR point clouds. The proposed workflow consists of three components: preprocessing, extraction, and classification. In preprocessing, the mobile LiDAR point clouds are converted into the radiometrically corrected intensity imagery of the road surface. Then the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu's thresholding, neighbor-counting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters using a manually defined decision tree. Case studies are conducted using the mobile LiDAR dataset acquired in Xiamen (Fujian, China) with different road environments by the RIEGL VMX-450 system. The results demonstrated that the proposed workflow and our software tool can achieve 93% in completeness, 95% in correctness, and 94% in F-score when using Xiamen dataset.

  9. Octree-based region growing for point cloud segmentation

    NASA Astrophysics Data System (ADS)

    Vo, Anh-Vu; Truong-Hong, Linh; Laefer, Debra F.; Bertolotto, Michela

    2015-06-01

    This paper introduces a novel, region-growing algorithm for the fast surface patch segmentation of three-dimensional point clouds of urban environments. The proposed algorithm is composed of two stages based on a coarse-to-fine concept. First, a region-growing step is performed on an octree-based voxelized representation of the input point cloud to extract major (coarse) segments. The output is then passed through a refinement process. As part of this, there are two competing factors related to voxel size selection. To balance the constraints, an adaptive octree is created in two stages. Empirical studies on real terrestrial and airborne laser scanning data for complex buildings and an urban setting show the proposed approach to be at least an order of magnitude faster when compared to a conventional region growing method and able to incorporate semantic-based feature criteria, while achieving precision, recall, and fitness scores of at least 75% and as much as 95%.

  10. Cloud point determination using a thickness shear mode resonator

    SciTech Connect

    Spates, J.J.; Martin, S.J.; Mansure, A.J.; Germer, J.W.

    1995-12-31

    Crude oils and crude oil products contain substantial amounts of petroleum waxes, consisting of a distribution of high molecular weight hydrocarbons. These waxes or paraffins have limited solubility in oil and tend to precipitate out at a temperature determined by the concentration and constituents of the wax. Precipitation and deposition of wax results in narrowing of pipelines, making crude oil recovery difficult. A parameter of practical importance is the wax precipitation temperature, traditionally known as the cloudpoint, at which visible crystallization occurs. Deposition problems arise in oil field operations at or below this temperature. Several techniques can be used to determine the cloud point: (1) visual observation, (2) viscosity measurement, (3) differential thermal analysis, and (4) pulsed nuclear magnetic resonance. This report describes a method for determination of cloud point with the use of a thickness shear mode resonator.

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

    NASA Astrophysics Data System (ADS)

    Rutzinger, Martin; Bremer, Magnus; Ragg, Hansjörg

    2013-04-01

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

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. The simulation of a convective cloud in a 3D model with explicit microphysics. Part II: Dynamical and microphysical aspects of cloud merger

    SciTech Connect

    Kogan, Y.L.; Shapiro, A.

    1996-09-01

    The development and merger of pairs of convective clouds in a shear-free environment were simulated in an explicit microphysical cloud model. The occurrence or nonoccurrence of updraft merger and the timing of merger depended critically on the initial spacing of the thermal perturbations imposed in the model`s initialization. In the unmerged cases the presence of a neighbor cloud was detrimental to cloud development at all times. In the merged cases this negative interaction was still operating but only until the onset of updraft merger. Based on the visual form of the updraft merger, it was hypothesized that low-level merger was a consequence of mutual advection, that is, that each cloud caught its neighbor in its radial inflow and advected it inward. This low-level advection hypothesis was quantified by considering a potential flow induced by two line sinks whose strengths were set equal to the low-level mass flux into the numerically simulated clouds. The merger times obtained from the advection hypothesis were in good agreement with the merger times observed in the simulations. Moreover, if merger did not occur, the advection hypothesis suggested that merger should not have occurred. The merger process was accompanied by the presence of trimodal drop spectra at the upper levels of the cloud. It was shown that the drop size distribution depends not only on the autoconversion and accretion rates, but also on the nonlinear interaction between various source and sink terms affecting rain formation, particularly on the rates of condensation-evaporation, sedimentation, and breakup processes. The analysis of raindrop trajectories showed the details of rain formation in different cloud regions and the effect of dynamical conditions on the growth of rain particles. 41 refs., 17 figs., 1 tab.

  18. 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).

  19. Terrestrial and unmanned aerial system imagery for deriving photogrammetric three-dimensional point clouds and volume models of mass wasting sites

    NASA Astrophysics Data System (ADS)

    Hämmerle, Martin; Schütt, Fabian; Höfle, Bernhard

    2016-04-01

    Three-dimensional (3-D) geodata of mass wasting sites are important to model surfaces, volumes, and their changes over time. With a photogrammetric approach commonly known as structure from motion, 3-D point clouds can be derived from image collections in a straightforward way. The quality of point clouds covering a quarry dump derived from terrestrial and aerial imagery is compared and assessed. A comprehensive set of quality indicators is calculated and compared to surveyed reference data and to a terrestrial LiDAR point cloud. The examined indicators are completeness of coverage, point density, vertical accuracy, multiscale point cloud distance, scaling accuracy, and dump volume. It is found that the photogrammetric datasets generally represent the examined dump well with, for example, an area coverage of up to 90% and 100% in case of terrestrial and aerial imagery, respectively, a maximum scaling difference of 0.62%, and volume estimations reaching up to 100% of the LiDAR reference. Combining the advantages of 3-D geodata derived from terrestrial (high detail, accurate volume calculation even with a small number of input images) and aerial images (high coverage) can be a promising method to further improve the quality of 3-D geodata derived with low-cost approaches.

  20. Electronic and magnetic structure of 3d-transition-metal point defects in silicon calculated from first principles

    NASA Astrophysics Data System (ADS)

    Beeler, F.; Andersen, O. K.; Scheffler, M.

    1990-01-01

    We describe spin-unrestricted self-consistent linear muffin-tin-orbital (LMTO) Green-function calculations for Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and Cu transition-metal impurities in crystalline silicon. Both defect sites of tetrahedral symmetry are considered. All possible charge states with their spin multiplicities, magnetization densities, and energy levels are discussed and explained with a simple physical picture. The early transition-metal interstitial and late transition-metal substitutional 3d ions are found to have low spin. This is in conflict with the generally accepted crystal-field model of Ludwig and Woodbury, but not with available experimental data. For the interstitial 3d ions, the calculated deep donor and acceptor levels reproduce all experimentally observed transitions. For substitutional 3d ions, a large number of predictions is offered to be tested by future experimental studies.

  1. 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

  2. Time Resolved 3-D Mapping of Atmospheric Aerosols and Clouds During the Recent ARM Water Vapor IOP

    NASA Technical Reports Server (NTRS)

    Schwemmer, Geary; Miller, David; Wilkerson, Thomas; Andrus, Ionio; Starr, David OC. (Technical Monitor)

    2001-01-01

    The HARLIE lidar was deployed at the ARM SGP site in north central Oklahoma and recorded over 100 hours of data on 16 days between 17 September and 6 October 2000 during the recent Water Vapor Intensive Operating Period (IOP). Placed in a ground-based trailer for upward looking scanning measurements of clouds and aerosols, HARLIE provided a unique record of time-resolved atmospheric backscatter at 1 micron wavelength. The conical scanning lidar images atmospheric backscatter along the surface of an inverted 90 degree (full angle) cone up to an altitude of 20 km. 360 degree scans having spatial resolutions of 20 meters in the vertical and 1 degree in azimuth were obtained every 36 seconds. Various boundary layer and cloud parameters are derived from the lidar data, as well as atmospheric wind vectors where there is Sufficiently resolved structure that can be traced moving through the surface described by the scanning laser beam. Comparison of HARLIE measured winds with radiosonde measured winds validates the accuracy of this new technique for remotely measuring atmospheric winds without Doppler information.

  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. 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…

  5. 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…

  6. 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

  7. 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

  8. 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.

  9. 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

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

    PubMed

    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%.

  11. 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.

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

    NASA Astrophysics Data System (ADS)

    Bremer, Magnus; Schmidtner, Korbinian; Rutzinger, Martin

    2015-04-01

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

  13. 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°.

  14. 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.

  15. Contextual Classification of Point Clouds Using a Two-Stage Crf

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    In this investigation, we address the task of airborne LiDAR point cloud labelling for urban areas by presenting a contextual classification methodology based on a Conditional Random Field (CRF). A two-stage CRF is set up: in a first step, a point-based CRF is applied. The resulting labellings are then used to generate a segmentation of the classified points using a Conditional Euclidean Clustering algorithm. This algorithm combines neighbouring points with the same object label into one segment. The second step comprises the classification of these segments, again with a CRF. As the number of the segments is much smaller than the number of points, it is computationally feasible to integrate long range interactions into this framework. Additionally, two different types of interactions are introduced: one for the local neighbourhood and another one operating on a coarser scale. This paper presents the entire processing chain. We show preliminary results achieved using the Vaihingen LiDAR dataset from the ISPRS Benchmark on Urban Classification and 3D Reconstruction, which consists of three test areas characterised by different and challenging conditions. The utilised classification features are described, and the advantages and remaining problems of our approach are discussed. We also compare our results to those generated by a point-based classification and show that a slight improvement is obtained with this first implementation.

  16. 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.

  17. 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.

  18. Integrated ray tracing simulation of annual variation of spectral bio-signatures from cloud free 3D optical Earth model

    NASA Astrophysics Data System (ADS)

    Ryu, Dongok; Kim, Sug-Whan; Kim, Dae Wook; Lee, Jae-Min; Lee, Hanshin; Park, Won Hyun; Seong, Sehyun; Ham, Sun-Jeong

    2010-09-01

    Understanding the Earth spectral bio-signatures provides an important reference datum for accurate de-convolution of collapsed spectral signals from potential earth-like planets of other star systems. This study presents a new ray tracing computation method including an improved 3D optical earth model constructed with the coastal line and vegetation distribution data from the Global Ecological Zone (GEZ) map. Using non-Lambertian bidirectional scattering distribution function (BSDF) models, the input earth surface model is characterized with three different scattering properties and their annual variations depending on monthly changes in vegetation distribution, sea ice coverage and illumination angle. The input atmosphere model consists of one layer with Rayleigh scattering model from the sea level to 100 km in altitude and its radiative transfer characteristics is computed for four seasons using the SMART codes. The ocean scattering model is a combination of sun-glint scattering and Lambertian scattering models. The land surface scattering is defined with the semi empirical parametric kernel method used for MODIS and POLDER missions. These three component models were integrated into the final Earth model that was then incorporated into the in-house built integrated ray tracing (IRT) model capable of computing both spectral imaging and radiative transfer performance of a hypothetical space instrument as it observes the Earth from its designated orbit. The IRT model simulation inputs include variation in earth orientation, illuminated phases, and seasonal sea ice and vegetation distribution. The trial simulation runs result in the annual variations in phase dependent disk averaged spectra (DAS) and its associated bio-signatures such as NDVI. The full computational details are presented together with the resulting annual variation in DAS and its associated bio-signatures.

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

    PubMed Central

    Valero, Enrique; Adán, Antonio; Cerrada, Carlos

    2012-01-01

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

  20. 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.

  1. Automatic method for building indoor boundary models from dense point clouds collected by laser scanners.

    PubMed

    Valero, Enrique; Adán, Antonio; Cerrada, Carlos

    2012-01-01

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

  2. Surface reconstruction for 3D remote sensing

    NASA Astrophysics Data System (ADS)

    Baran, Matthew S.; Tutwiler, Richard L.; Natale, Donald J.

    2012-05-01

    This paper examines the performance of the local level set method on the surface reconstruction problem for unorganized point clouds in three dimensions. Many laser-ranging, stereo, and structured light devices produce three dimensional information in the form of unorganized point clouds. The point clouds are sampled from surfaces embedded in R3 from the viewpoint of a camera focal plane or laser receiver. The reconstruction of these objects in the form of a triangulated geometric surface is an important step in computer vision and image processing. The local level set method uses a Hamilton-Jacobi partial differential equation to describe the motion of an implicit surface in threespace. An initial surface which encloses the data is allowed to move until it becomes a smooth fit of the unorganized point data. A 3D point cloud test suite was assembled from publicly available laser-scanned object databases. The test suite exhibits nonuniform sampling rates and various noise characteristics to challenge the surface reconstruction algorithm. Quantitative metrics are introduced to capture the accuracy and efficiency of surface reconstruction on the degraded data. The results characterize the robustness of the level set method for surface reconstruction as applied to 3D remote sensing.

  3. Automatic registration of large-scale urban scene point clouds based on semantic feature points

    NASA Astrophysics Data System (ADS)

    Yang, Bisheng; Dong, Zhen; Liang, Fuxun; Liu, Yuan

    2016-03-01

    Point clouds collected by terrestrial laser scanning (TLS) from large-scale urban scenes contain a wide variety of objects (buildings, cars, pole-like objects, and others) with symmetric and incomplete structures, and relatively low-textured surfaces, all of which pose great challenges for automatic registration between scans. To address the challenges, this paper proposes a registration method to provide marker-free and multi-view registration based on the semantic feature points extracted. First, the method detects the semantic feature points within a detection scheme, which includes point cloud segmentation, vertical feature lines extraction and semantic information calculation and finally takes the intersections of these lines with the ground as the semantic feature points. Second, the proposed method matches the semantic feature points using geometrical constraints (3-point scheme) as well as semantic information (category and direction), resulting in exhaustive pairwise registration between scans. Finally, the proposed method implements multi-view registration by constructing a minimum spanning tree of the fully connected graph derived from exhaustive pairwise registration. Experiments have demonstrated that the proposed method performs well in various urban environments and indoor scenes with the accuracy at the centimeter level and improves the efficiency, robustness, and accuracy of registration in comparison with the feature plane-based methods.

  4. Simple computation of reaction-diffusion processes on point clouds.

    PubMed

    Macdonald, Colin B; Merriman, Barry; Ruuth, Steven J

    2013-06-01

    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.

  5. Simple computation of reaction–diffusion processes on point clouds

    PubMed Central

    Macdonald, Colin B.; Merriman, Barry; Ruuth, Steven J.

    2013-01-01

    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. PMID:23690616

  6. 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.

  7. 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.

  8. Different effects of bladder distention on point A-based and 3D-conformal intracavitary brachytherapy planning for cervical cancer.

    PubMed

    Ju, Sang Gyu; Huh, Seung Jae; Shin, Jung Suk; Park, Won; Nam, Heerim; Bae, Sunhyun; Oh, Dongryul; Hong, Chae-Seon; Kim, Jin Sung; Han, Youngyih; Choi, Doo Ho

    2013-03-01

    This study sought to evaluate the differential effects of bladder distention on point A-based (AICBT) and three-dimensional conformal intracavitary brachytherapy (3D-ICBT) planning for cervical cancer. Two sets of CT scans were obtained for ten patients to evaluate the effect of bladder distention. After the first CT scan, with an empty bladder, a second set of CT scans was obtained with the bladder filled. The clinical target volume (CTV), bladder, rectum, and small bowel were delineated on each image set. The AICBT and 3D-ICBT plans were generated, and we compared the different planning techniques with respect to the dose characteristics of CTV and organs at risk. As a result of bladder distention, the mean dose (D50) was decreased significantly and geometrical variations were observed in the bladder and small bowel, with acceptable minor changes in the CTV and rectum. The average D2 cm(3)and D1 cm(3)showed a significant change in the bladder and small bowel with AICBT; however, no change was detected with the 3D-ICBT planning. No significant dose change in the CTV or rectum was observed with either the AICBT or the 3D-ICBT plan. The effect of bladder distention on dosimetrical change in 3D-ICBT planning appears to be minimal, in comparison with AICBT planning.

  9. 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

  10. 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.

  11. 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.

  12. Uav-Based Photogrammetric Point Clouds - Tree STEM Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

    NASA Astrophysics Data System (ADS)

    Fritz, A.; Kattenborn, T.; Koch, B.

    2013-08-01

    In both ecology and forestry, there is a high demand for structural information of forest stands. Forest structures, due to their heterogeneity and density, are often difficult to assess. Hence, a variety of technologies are being applied to account for this "difficult to come by" information. Common techniques are aerial images or ground- and airborne-Lidar. In the present study we evaluate the potential use of unmanned aerial vehicles (UAVs) as a platform for tree stem detection in open stands. A flight campaign over a test site near Freiburg, Germany covering a target area of 120 × 75 [m2] was conducted. The dominant tree species of the site is oak (quercus robur) with almost no understory growth. Over 1000 images with a tilt angle of 45° were shot. The flight pattern applied consisted of two antipodal staggered flight routes at a height of 55 [m] above the ground. We used a Panasonic G3 consumer camera equipped with a 14-42 [mm] standard lens and a 16.6 megapixel sensor. The data collection took place in leaf-off state in April 2013. The area was prepared with artificial ground control points for transformation of the structure-from-motion (SFM) point cloud into real world coordinates. After processing, the results were compared with a terrestrial laser scanner (TLS) point cloud of the same area. In the 0.9 [ha] test area, 102 individual trees above 7 [cm] diameter at breast height were located on in the TLS-cloud. We chose the software CMVS/PMVS-2 since its algorithms are developed with focus on dense reconstruction. The processing chain for the UAV-acquired images consists of six steps: a. cleaning the data: removing of blurry, under- or over exposed and off-site images; b. applying the SIFT operator [Lowe, 2004]; c. image matching; d. bundle adjustment; e. clustering; and f. dense reconstruction. In total, 73 stems were considered as reconstructed and located within one meter of the reference trees. In general stems were far less accurate and complete as

  13. Semi-automatic extraction of sectional view from point clouds - The case of Ottmarsheim's abbey-church

    NASA Astrophysics Data System (ADS)

    Landes, T.; Bidino, S.; Guild, R.

    2014-06-01

    Today, elevations or sectional views of buildings are often produced from terrestrial laser scanning. However, due to the amount of data to process and because usually 2D maps are required by customers, the 3D point cloud is often degraded into 2D slices. In a sectional view, not only the portions of the objet which are intersected by the cutting plane but also edges and contours of other parts of the object which are visible behind the cutting plane are represented. To avoid the tedious manual drawing, the aim of this work is to propose a semi-automatic approach for creating sectional views by point cloud processing. The extraction of sectional views requires in a first step the segmentation of the point cloud into planar and non-planar entities. Since in cultural heritage buildings, arches, vaults, columns can be found, the position and the direction of the sectional view must be taken into account before contours extraction. Indeed, the edges of surfaces of revolution depend on the chosen view. The developed extraction approach is detailed based on point clouds acquired inside and outside churches. The resulting sectional view has been evaluated in a qualitative and quantitative way by comparing it with a reference sectional view made by hand. A mean deviation of 3 cm between both sections proves that the proposed approach is promising. Regarding the processing time, despite a few manual corrections, it has saved 40% of the time required for manual drawing.

  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. 3d-3d correspondence revisited

    NASA Astrophysics Data System (ADS)

    Chung, Hee-Joong; Dimofte, Tudor; Gukov, Sergei; Sułkowski, Piotr

    2016-04-01

    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. We also consider the cutting and gluing of 3-manifolds along smooth boundaries and the role played by all flat connections in this operation.

  16. 3d-3d correspondence revisited

    DOE PAGES

    Chung, Hee -Joong; Dimofte, Tudor; Gukov, Sergei; Sułkowski, Piotr

    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.

  17. 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.

  18. SHAPES - Spatial, High-Accuracy, Position-Encoding Sensor for multi-point, 3-D position measurement of large flexible structures

    NASA Technical Reports Server (NTRS)

    Nerheim, N. M

    1987-01-01

    An electro-optical position sensor for precise simultaneous measurement of the 3-D positions of multiple points on large space structures is described. The sensor data rate is sufficient for most control purposes. Range is determined by time-of-flight correlation of short laser pulses returned from retroreflector targets using a streak tube/CCD detector. Angular position is determined from target image locations on a second CCD. Experimental verification of dynamic ranging to multiple targets is discussed.

  19. 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.

  20. 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.

  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. 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

  3. An improved approach for flow-based cloud point extraction.

    PubMed

    Frizzarin, Rejane M; Rocha, Fábio R P

    2014-04-11

    Novel strategies are proposed to circumvent the main drawbacks of flow-based cloud point extraction (CPE). The surfactant-rich phase (SRP) was directly retained into the optical path of the spectrophotometric cell, thus avoiding its dilution previously to the measurement and yielding higher sensitivity. Solenoid micro-pumps were exploited to improve mixing by the pulsed flow and also to modulate the flow-rate for retention and removal of the SRP, thus avoiding the elution step, often carried out with organic solvents. The heat released and the increase of the salt concentration provided by an on-line neutralization reaction were exploited to induce the cloud point without an external heating device. These innovations were demonstrated by the spectrophotometric determination of iron, yielding a linear response from 10 to 200 μg L(-1) with a coefficient of variation of 2.3% (n=7). Detection limit and sampling rate were estimated at 5 μg L(-1) (95% confidence level) and 26 samples per hour, respectively. The enrichment factor was 8.9 and the procedure consumed only 6 μg of TAN and 390 μg of Triton X-114 per determination. At the 95% confidence level, the results obtained for freshwater samples agreed with the reference procedure and those obtained for digests of bovine muscle, rice flour, brown bread and tort lobster agreed with the certified reference values. The proposed procedure thus shows advantages in relation to previously proposed approaches for flow-based CPE, being a fast and environmental friendly alternative for on-line separation and pre-concentration.

  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. 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

  6. 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. PMID:25996561

  7. A New Stochastic Modeling of 3-D Mud Drapes Inside Point Bar Sands in Meandering River Deposits

    SciTech Connect

    Yin, Yanshu

    2013-12-15

    The environment of major sediments of eastern China oilfields is a meandering river where mud drapes inside point bar sand occur and are recognized as important factors for underground fluid flow and distribution of the remaining oil. The present detailed architectural analysis, and the related mud drapes' modeling inside a point bar, is practical work to enhance oil recovery. This paper illustrates a new stochastic modeling of mud drapes inside point bars. The method is a hierarchical strategy and composed of three nested steps. Firstly, the model of meandering channel bodies is established using the Fluvsim method. Each channel centerline obtained from the Fluvsim is preserved for the next simulation. Secondly, the curvature ratios of each meandering river at various positions are calculated to determine the occurrence of each point bar. The abandoned channel is used to characterize the geometry of each defined point bar. Finally, mud drapes inside each point bar are predicted through random sampling of various parameters, such as number, horizontal intervals, dip angle, and extended distance of mud drapes. A dataset, collected from a reservoir in the Shengli oilfield of China, was used to illustrate the mud drapes' building procedure proposed in this paper. The results show that the inner architectural elements of the meandering river are depicted fairly well in the model. More importantly, the high prediction precision from the cross validation of five drilled wells shows the practical value and significance of the proposed method.

  8. 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

  9. 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.

  10. Two-step adaptive extraction method for ground points and breaklines from lidar point clouds

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

    The extraction of ground points and breaklines is a crucial step during generation of high quality digital elevation models (DEMs) from airborne LiDAR point clouds. In this study, we propose a novel automated method for this task. To overcome the disadvantages of applying a single filtering method in areas with various types of terrain, the proposed method first classifies the points into a set of segments and one set of individual points, which are filtered by segment-based filtering and multi-scale morphological filtering, respectively. In the process of multi-scale morphological filtering, the proposed method removes amorphous objects from the set of individual points to decrease the effect of the maximum scale on the filtering result. The proposed method then extracts the breaklines from the ground points, which provide a good foundation for generation of a high quality DEM. Finally, the experimental results demonstrate that the proposed method extracts ground points in a robust manner while preserving the breaklines.

  11. 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.

  12. 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.

  13. 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 calculating of displacement vector and analyzing possible physiological and anatomical changes during treatment.

  14. A 4-point in-situ method to locate a discrete gamma-ray source in 3-D space.

    PubMed

    Byun, Jong-In; Choi, Hee-Yeoul; Yun, Ju-Yong

    2010-02-01

    The determination of the source position (x,y,z) of a discrete gamma-ray source using peak count rates from four measurement points was studied. We derived semi-empirical formulas to find the position under the condition to neglect attenuation effects by obstacles between the target source and the detector. To validate the methodology, we performed the locating experiments for a (137)Cs small volume source placed at 10 different positions on the floor of a laboratory using the formulas derived in this study. In this study, a portable HPGe gamma spectrometry system with a virtual point detector concept was used. The calculation results for the source positions were compared with reference values measured with a rule. The applicability of the methodology was estimated based on the differences of the results. PMID:19932029

  15. Advanced 3-D analysis, client-server systems, and cloud computing—Integration of cardiovascular imaging data into clinical workflows of transcatheter aortic valve replacement

    PubMed Central

    Zimmermann, Mathis; Falkner, Juergen

    2013-01-01

    Degenerative aortic stenosis is highly prevalent in the aging populations of industrialized countries and is associated with poor prognosis. Surgical valve replacement has been the only established treatment with documented improvement of long-term outcome. However, many of the older patients with aortic stenosis (AS) are high-risk or ineligible for surgery. For these patients, transcatheter aortic valve replacement (TAVR) has emerged as a treatment alternative. The TAVR procedure is characterized by a lack of visualization of the operative field. Therefore, pre- and intra-procedural imaging is critical for patient selection, pre-procedural planning, and intra-operative decision-making. Incremental to conventional angiography and 2-D echocardiography, multidetector computed tomography (CT) has assumed an important role before TAVR. The analysis of 3-D CT data requires extensive post-processing during direct interaction with the dataset, using advance analysis software. Organization and storage of the data according to complex clinical workflows and sharing of image information have become a critical part of these novel treatment approaches. Optimally, the data are integrated into a comprehensive image data file accessible to multiple groups of practitioners across the hospital. This creates new challenges for data management requiring a complex IT infrastructure, spanning across multiple locations, but is increasingly achieved with client-server solutions and private cloud technology. This article describes the challenges and opportunities created by the increased amount of patient-specific imaging data in the context of TAVR. PMID:24282750

  16. Evaluation of the Quantitative Accuracy of 3D Reconstruction of Edentulous Jaw Models with Jaw Relation Based on Reference Point System Alignment

    PubMed Central

    Li, Weiwei; Yuan, Fusong; Lv, Peijun; Wang, Yong; Sun, Yuchun

    2015-01-01

    Objectives To apply contact measurement and reference point system (RPS) alignment techniques to establish a method for 3D reconstruction of the edentulous jaw models with centric relation and to quantitatively evaluate its accuracy. Methods Upper and lower edentulous jaw models were clinically prepared, 10 pairs of resin cylinders with same size were adhered to axial surfaces of upper and lower models. The occlusal bases and the upper and lower jaw models were installed in the centric relation position. Faro Edge 1.8m was used to directly obtain center points of the base surface of the cylinders (contact method). Activity 880 dental scanner was used to obtain 3D data of the cylinders and the center points were fitted (fitting method). 3 pairs of center points were used to align the virtual model to centric relation. An observation coordinate system was interactively established. The straight-line distances in the X (horizontal left/right), Y (horizontal anterior/posterior), and Z (vertical) between the remaining 7 pairs of center points derived from contact method and fitting method were measured respectively and analyzed using a paired t-test. Results The differences of the straight-line distances of the remaining 7 pairs of center points between the two methods were X: 0.074 ± 0.107 mm, Y: 0.168 ± 0.176 mm, and Z: −0.003± 0.155 mm. The results of paired t-test were X and Z: p >0.05, Y: p <0.05. Conclusion By using contact measurement and the reference point system alignment technique, highly accurate reconstruction of the vertical distance and centric relation of a digital edentulous jaw model can be achieved, which meets the design and manufacturing requirements of the complete dentures. The error of horizontal anterior/posterior jaw relation was relatively large. PMID:25659133

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

    NASA Astrophysics Data System (ADS)

    Yang, Bisheng; Dong, Zhen

    2013-07-01

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

  18. Dynamic topology and flux rope evolution during non-linear tearing of 3D null point current sheets

    SciTech Connect

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

    2014-10-15

    In this work, the dynamic magnetic field within a tearing-unstable three-dimensional current sheet about a magnetic null point is described in detail. We focus on the evolution of the magnetic null points and flux ropes that are formed during the tearing process. Generally, we find that both magnetic structures are created prolifically within the layer and are non-trivially related. We examine how nulls are created and annihilated during bifurcation processes, and describe how they evolve within the current layer. The type of null bifurcation first observed is associated with the formation of pairs of flux ropes within the current layer. We also find that new nulls form within these flux ropes, both following internal reconnection and as adjacent flux ropes interact. The flux ropes exhibit a complex evolution, driven by a combination of ideal kinking and their interaction with the outflow jets from the main layer. The finite size of the unstable layer also allows us to consider the wider effects of flux rope generation. We find that the unstable current layer acts as a source of torsional magnetohydrodynamic waves and dynamic braiding of magnetic fields. The implications of these results to several areas of heliophysics are discussed.

  19. 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

  20. Quantitative data quality metrics for 3D laser radar systems

    NASA Astrophysics Data System (ADS)

    Stevens, Jeffrey R.; Lopez, Norman A.; Burton, Robin R.

    2011-06-01

    Several quantitative data quality metrics for three dimensional (3D) laser radar systems are presented, namely: X-Y contrast transfer function, Z noise, Z resolution, X-Y edge & line spread functions, 3D point spread function and data voids. These metrics are calculated from both raw and/or processed point cloud data, providing different information regarding the performance of 3D imaging laser radar systems and the perceptual quality attributes of 3D datasets. The discussion is presented within the context of 3D imaging laser radar systems employing arrays of Geiger-mode Avalanche Photodiode (GmAPD) detectors, but the metrics may generally be applied to linear mode systems as well. An example for the role of these metrics in comparison of noise removal algorithms is also provided.

  1. Bootstrapping 3D fermions

    DOE PAGES

    Iliesiu, Luca; Kos, Filip; Poland, David; Pufu, Silviu S.; Simmons-Duffin, David; Yacoby, Ran

    2016-03-17

    We study the conformal bootstrap for a 4-point function of fermions <ψψψψ> in 3D. We first introduce an embedding formalism for 3D spinors and compute the conformal blocks appearing in fermion 4-point functions. Using these results, we find general bounds on the dimensions of operators appearing in the ψ × ψ OPE, and also on the central charge CT. We observe features in our bounds that coincide with scaling dimensions in the GrossNeveu models at large N. Finally, we also speculate that other features could coincide with a fermionic CFT containing no relevant scalar operators.

  2. Buildings and Terrain of Urban Area Point Cloud Segmentation based on PCL

    NASA Astrophysics Data System (ADS)

    Liu, Ying; Zhong, Ruofei

    2014-03-01

    One current problem with laser radar point data classification is building and urban terrain segmentation, this paper proposes a point cloud segmentation method base on PCL libraries. PCL is a large cross-platform open source C++ programming library, which implements a large number of point cloud related efficient data structures and generic algorithms involving point cloud retrieval, filtering, segmentation, registration, feature extraction and curved surface reconstruction, visualization, etc. Due to laser radar point cloud characteristics with large amount of data, unsymmetrical distribution, this paper proposes using the data structure of kd-tree to organize data; then using Voxel Grid filter for point cloud resampling, namely to reduce the amount of point cloud data, and at the same time keep the point cloud shape characteristic; use PCL Segmentation Module, we use a Euclidean Cluster Extraction class with Europe clustering for buildings and ground three-dimensional point cloud segmentation. The experimental results show that this method avoids the multiple copy system existing data needs, saves the program storage space through the call of PCL library method and class, shortens the program compiled time and improves the running speed of the program.

  3. Random-profiles-based 3D face recognition system.

    PubMed

    Kim, Joongrock; Yu, Sunjin; Lee, Sangyoun

    2014-01-01

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.

  4. Random-Profiles-Based 3D Face Recognition System

    PubMed Central

    Joongrock, Kim; Sunjin, Yu; Sangyoun, Lee

    2014-01-01

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation. PMID:24691101

  5. Simulation and Analysis of Icesat-2 Point Clouds

    NASA Astrophysics Data System (ADS)

    Kerekes, J. P.; Brown, S. D.; Zhang, J.; Yang, J.; Csatho, B. M.; Schenk, A. F.

    2014-12-01

    The ATLAS instrument on the upcoming ICESat-2 mission contains a high-repetition rate micropulse laser and photon counting detectors for high sensitivity and dense along-track sampling. As evidenced by the airborne MABEL photon-counting system, the data collected by photon-counting detectors have substantial noise and will require considerable processing to accurately retrieve the surface elevation in many situations. To study the characteristics of these data and in support of pre-launch algorithm development, researchers at RIT have been generating simulated ATLAS point clouds using their DIRISG tool, a first-principles radiative transfer remote sensing data simulation package. Included in the simulated data are noise returns specified using pre-launch measurements of the flight detectors. These simulated data have been used to assess the accuracy of surface-finding algorithms and to study the anticipated elevation retrieval performance on complex snow and ice surfaces. This work has found single-track biases of up to 2 cm and error standard deviations of up to 10 cm on complex snow surfaces. Additionally, the research has shown quantitative sensitivity confirming smoother surfaces result in higher accuracy and a lower surface diffuse albedo result in a smaller bias.

  6. 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.

  7. 3D affine registration using teaching-learning based optimization

    NASA Astrophysics Data System (ADS)

    Jani, Ashish; Savsani, Vimal; Pandya, Abhijit

    2013-09-01

    3D image registration is an emerging research field in the study of computer vision. In this paper, two effective global optimization methods are considered for the 3D registration of point clouds. Experiments were conducted by applying each algorithm and their performance was evaluated with respect to rigidity, similarity and affine transformations. Comparison of algorithms and its effectiveness was tested for the average performance to find the global solution for minimizing the error in the terms of distance between the model cloud and the data cloud. The parameters for the transformation matrix were considered as the design variables. Further comparisons of the considered methods were done for the computational effort, computational time and the convergence of the algorithm. The results reveal that the use of TLBO was outstanding for image processing application involving 3D registration. [Figure not available: see fulltext.

  8. 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.

  9. 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.

  10. Automated Detection of Geomorphic Features in LiDAR Point Clouds of Various Spatial Density

    NASA Astrophysics Data System (ADS)

    Dorninger, Peter; Székely, Balázs; Zámolyi, András.; Nothegger, Clemens

    2010-05-01

    relevant results. Consequently, it could be verified that a topographic surface can be properly represented by a set of distinct planar structures. Therefore, the subsequent interpretation of those planes with respect to geomorphic characteristics is acceptable. The additional in situ geological measurements verified some of our findings in the sense that similar primary directions could be found that were derived from the LiDAR data set and (Zámolyi et al., 2010, this volume). References: P. Dorninger, N. Pfeifer: "A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds"; Sensors, 8 (2008), 11; 7323 - 7343. C. Nothegger, P. Dorninger: "3D Filtering of High-Resolution Terrestrial Laser Scanner Point Clouds for Cultural Heritage Documentation"; Photogrammetrie, Fernerkundung, Geoinformation, 1 (2009), 53 - 63. A. Zámolyi, B. Székely, G. Molnár, A. Roncat, P. Dorninger, A. Pocsai, M. Wyszyski, P. Drexel: "Comparison of LiDAR derived directional topographic features with geologic field evidence: a case study of Doren landslide (Vorarlberg, Austria)"; EGU General Assembly 2010, Vienna, Austria

  11. 3D reconstruction with two webcams and a laser line projector

    NASA Astrophysics Data System (ADS)

    Li, Dongdong; Hui, Bingwei; Qiu, Shaohua; Wen, Gongjian

    2014-09-01

    Three-dimensional (3D) reconstruction is one of the most attractive research topics in photogrammetry and computer vision. Nowadays 3D reconstruction with simple and consumable equipment plays an important role. In this paper, a 3D reconstruction desktop system is built based on binocular stereo vision using a laser scanner. The hardware requirements are a simple commercial hand-held laser line projector and two common webcams for image acquisition. Generally, 3D reconstruction based on passive triangulation methods requires point correspondences among various viewpoints. The development of matching algorithms remains a challenging task in computer vision. In our proposal, with the help of a laser line projector, stereo correspondences are established robustly from epipolar geometry and the laser shadow on the scanned object. To establish correspondences more conveniently, epipolar rectification is employed using Bouguet's method after stereo calibration with a printed chessboard. 3D coordinates of the observed points are worked out with rayray triangulation and reconstruction outliers are removed with the planarity constraint of the laser plane. Dense 3D point clouds are derived from multiple scans under different orientations. Each point cloud is derived by sweeping the laser plane across the object requiring 3D reconstruction. The Iterative Closest Point algorithm is employed to register the derived point clouds. Rigid body transformation between neighboring scans is obtained to get the complete 3D point cloud. Finally polygon meshes are reconstructed from the derived point cloud and color images are used in texture mapping to get a lifelike 3D model. Experiments show that our reconstruction method is simple and efficient.

  12. Point source atom interferometry with a cloud of finite size

    NASA Astrophysics Data System (ADS)

    Hoth, Gregory W.; Pelle, Bruno; Riedl, Stefan; Kitching, John; Donley, Elizabeth A.

    2016-08-01

    We demonstrate a two axis gyroscope by the use of light pulse atom interferometry with an expanding cloud of atoms in the regime where the cloud has expanded by 1.1-5 times its initial size during the interrogation. Rotations are measured by analyzing spatial fringe patterns in the atom population obtained by imaging the final cloud. The fringes arise from a correlation between an atom's initial velocity and its final position. This correlation is naturally created by the expansion of the cloud, but it also depends on the initial atomic distribution. We show that the frequency and contrast of these spatial fringes depend on the details of the initial distribution and develop an analytical model to explain this dependence. We also discuss several challenges that must be overcome to realize a high-performance gyroscope with this technique.

  13. Automating 3D reconstruction using a probabilistic grammar

    NASA Astrophysics Data System (ADS)

    Xiong, Hanwei; Xu, Jun; Xu, Chenxi; Pan, Ming

    2015-10-01

    3D reconstruction of objects from point clouds with a laser scanner is still a laborious task in many applications. Automating 3D process is an ongoing research topic and suffers from the complex structure of the data. The main difficulty is due to lack of knowledge of real world objects structure. In this paper, we accumulate such structure knowledge by a probabilistic grammar learned from examples in the same category. The rules of the grammar capture compositional structures at different levels, and a feature dependent probability function is attached for every rule. The learned grammar can be used to parse new 3D point clouds, organize segment patches in a hierarchal way, and assign them meaningful labels. The parsed semantics can be used to guide the reconstruction algorithms automatically. Some examples are given to explain the method.

  14. Formation of Dirac point and the topological surface states inside the strained gap for mixed 3D Hg1-xCdx Te

    NASA Astrophysics Data System (ADS)

    Marchewka, Michał

    2016-10-01

    In this paper the results of the numerical calculation obtained for the three-dimensional (3D) strained Hg1-xCdx Te layers for the x-Cd composition from 0.1 to 0.155 and a different mismatch of the lattice constant are presented. For the investigated region of the Cd composition (x value) the negative energy gap (Eg =Γ8 -Γ6) in the Hg1-xCdx Te 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. The numerical calculation based on the finite difference method applied for the 8×8 kp model with the in-plane tensile strain for (001) growth oriented structure shows that the Dirac cone inside the induced insulating band gap for non zero of the Cd composition and a bigger strain caused by the bigger lattice mismatch (than for the 3D HgTe TI) can be obtained. It was also shown how different x-Cd compounds move the Dirac cone from the valence band into the band gap. The presented results show that 75 nm wide 3D Hg1-xCdx Te structures with x ≈ 0.155 and 1.6% lattice mismatch make the system a true topological insulator with the dispersion of the topological surface states similar to those ones obtained for the strained CdTe/HgTe QW.

  15. Objective and subjective quality assessment of geometry compression of reconstructed 3D humans in a 3D virtual room

    NASA Astrophysics Data System (ADS)

    Mekuria, Rufael; Cesar, Pablo; Doumanis, Ioannis; Frisiello, Antonella

    2015-09-01

    Compression of 3D object based video is relevant for 3D Immersive applications. Nevertheless, the perceptual aspects of the degradation introduced by codecs for meshes and point clouds are not well understood. In this paper we evaluate the subjective and objective degradations introduced by such codecs in a state of art 3D immersive virtual room. In the 3D immersive virtual room, users are captured with multiple cameras, and their surfaces are reconstructed as photorealistic colored/textured 3D meshes or point clouds. To test the perceptual effect of compression and transmission, we render degraded versions with different frame rates in different contexts (near/far) in the scene. A quantitative subjective study with 16 users shows that negligible distortion of decoded surfaces compared to the original reconstructions can be achieved in the 3D virtual room. In addition, a qualitative task based analysis in a full prototype field trial shows increased presence, emotion, user and state recognition of the reconstructed 3D Human representation compared to animated computer avatars.

  16. Improving automated 3D reconstruction methods via vision metrology

    NASA Astrophysics Data System (ADS)

    Toschi, Isabella; Nocerino, Erica; Hess, Mona; Menna, Fabio; Sargeant, Ben; MacDonald, Lindsay; Remondino, Fabio; Robson, Stuart

    2015-05-01

    This paper aims to provide a procedure for improving automated 3D reconstruction methods via vision metrology. The 3D reconstruction problem is generally addressed using two different approaches. On the one hand, vision metrology (VM) systems try to accurately derive 3D coordinates of few sparse object points for industrial measurement and inspection applications; on the other, recent dense image matching (DIM) algorithms are designed to produce dense point clouds for surface representations and analyses. This paper strives to demonstrate a step towards narrowing the gap between traditional VM and DIM approaches. Efforts are therefore intended to (i) test the metric performance of the automated photogrammetric 3D reconstruction procedure, (ii) enhance the accuracy of the final results and (iii) obtain statistical indicators of the quality achieved in the orientation step. VM tools are exploited to integrate their main functionalities (centroid measurement, photogrammetric network adjustment, precision assessment, etc.) into the pipeline of 3D dense reconstruction. Finally, geometric analyses and accuracy evaluations are performed on the raw output of the matching (i.e. the point clouds) by adopting a metrological approach. The latter is based on the use of known geometric shapes and quality parameters derived from VDI/VDE guidelines. Tests are carried out by imaging the calibrated Portable Metric Test Object, designed and built at University College London (UCL), UK. It allows assessment of the performance of the image orientation and matching procedures within a typical industrial scenario, characterised by poor texture and known 3D/2D shapes.

  17. Duality between the dynamics of line-like brushes of point defects in 2D and strings in 3D in liquid crystals

    NASA Astrophysics Data System (ADS)

    Digal, Sanatan; Ray, Rajarshi; Saumia, P. S.; Srivastava, Ajit M.

    2013-10-01

    We analyze the dynamics of dark brushes connecting point vortices of strength ±1 formed in the isotropic-nematic phase transition of a thin layer of nematic liquid crystals, using a crossed polarizer set up. The evolution of the brushes is seen to be remarkably similar to the evolution of line defects in a three-dimensional nematic liquid crystal system. Even phenomena like the intercommutativity of strings are routinely observed in the dynamics of brushes. We test the hypothesis of a duality between the two systems by determining exponents for the coarsening of total brush length with time as well as shrinking of the size of an isolated loop. Our results show scaling behavior for the brush length as well as the loop size with corresponding exponents in good agreement with the 3D case of string defects.

  18. 3D Radiative Transfer Effects in Multi-Angle/Multi-Spectral Radio-Polarimetric Signals from a Mixture of Clouds and Aerosols Viewed by a Non-Imaging Sensor

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

    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.

  19. Saturation point representation of cloud-top entrainment instability

    NASA Technical Reports Server (NTRS)

    Boers, Reinout

    1991-01-01

    Cloud-top entrainment instability was investigated using a mixing line analysis. Mixing time scales are closely related to the actual size of the parcel, so that local instabilities are largely dependent on the scales of mixing near the cloud top. Given a fixed transport velocity, variation over a small range of parcel length scales (parcel mixing velocities) turns an energy-producing mixing process into an energy-consuming mixing process. It is suggested that a single criterion for cloud-top entrainment instability will not be found due to the role of at least three factors operating more or less independently; the stability of the mixing line, the entrainment speed, and the strength of the internal boundary-layer circulation.

  20. Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood

    NASA Astrophysics Data System (ADS)

    Arastounia, M.; Lichti, D. D.

    2014-06-01

    According to the Department of Energy of the USA, today's electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact with the conductive elements of the electrical substations. This can be prevented by covering the conductive electrical objects with insulating materials. The design of these custom-built insulating covers requires a 3D as-built plan of the substation. This research aims to develop automated methods to create such a 3D as-built plan using terrestrial LiDAR data for which objects first need to be recognized in the LiDAR point clouds. This paper reports on the application of a new algorithm for the segmentation of planar surfaces found at electrical substations. The proposed approach is a region growing method that aggregates points based on their proximity to each other and their neighbourhood dispersion direction. PCA (principal components analysis) is also employed to segment planar surfaces in the electrical substation. In this research two different laser scanners, Leica HDS 6100 and Faro Focus3D, were utilized to scan an electrical substation in Airdrie, a city located in north of Calgary, Canada. In this research, three subsets incorporating one subset of Leica dataset with approximately 1.7 million points and two subsets of the Faro dataset with 587 and 79 thousand points were utilized. The performance of our proposed method is compared with the performance of PCA by performing check point analysis and investigation of computational speed. Both methods managed to detect a great proportion of planar points (about 70%). However, the proposed method slightly outperformed PCA. 95% of the points that were segmented by both methods as planar points did actually lie on a planar surface. This exhibits the high ability of both methods to identify planar points. The results also indicate that the computational speed of our method is

  1. Georeferencing UAS Derivatives Through Point Cloud Registration with Archived Lidar Datasets

    NASA Astrophysics Data System (ADS)

    Magtalas, M. S. L. Y.; Aves, J. C. L.; Blanco, A. C.

    2016-10-01

    Georeferencing gathered images is a common step before performing spatial analysis and other processes on acquired datasets using unmanned aerial systems (UAS). Methods of applying spatial information to aerial images or their derivatives is through onboard GPS (Global Positioning Systems) geotagging, or through tying of models through GCPs (Ground Control Points) acquired in the field. Currently, UAS (Unmanned Aerial System) derivatives are limited to meter-levels of accuracy when their generation is unaided with points of known position on the ground. The use of ground control points established using survey-grade GPS or GNSS receivers can greatly reduce model errors to centimeter levels. However, this comes with additional costs not only with instrument acquisition and survey operations, but also in actual time spent in the field. This study uses a workflow for cloud-based post-processing of UAS data in combination with already existing LiDAR data. The georeferencing of the UAV point cloud is executed using the Iterative Closest Point algorithm (ICP). It is applied through the open-source CloudCompare software (Girardeau-Montaut, 2006) on a `skeleton point cloud'. This skeleton point cloud consists of manually extracted features consistent on both LiDAR and UAV data. For this cloud, roads and buildings with minimal deviations given their differing dates of acquisition are considered consistent. Transformation parameters are computed for the skeleton cloud which could then be applied to the whole UAS dataset. In addition, a separate cloud consisting of non-vegetation features automatically derived using CANUPO classification algorithm (Brodu and Lague, 2012) was used to generate a separate set of parameters. Ground survey is done to validate the transformed cloud. An RMSE value of around 16 centimeters was found when comparing validation data to the models georeferenced using the CANUPO cloud and the manual skeleton cloud. Cloud-to-cloud distance computations of

  2. On detailed 3D reconstruction of large indoor environments

    NASA Astrophysics Data System (ADS)

    Bondarev, Egor

    2015-03-01

    In this paper we present techniques for highly detailed 3D reconstruction of extra large indoor environments. We discuss the benefits and drawbacks of low-range, far-range and hybrid sensing and reconstruction approaches. The proposed techniques for low-range and hybrid reconstruction, enabling the reconstruction density of 125 points/cm3 on large 100.000 m3 models, are presented in detail. The techniques tackle the core challenges for the above requirements, such as a multi-modal data fusion (fusion of a LIDAR data with a Kinect data), accurate sensor pose estimation, high-density scanning and depth data noise filtering. Other important aspects for extra large 3D indoor reconstruction are the point cloud decimation and real-time rendering. In this paper, we present a method for planar-based point cloud decimation, allowing for reduction of a point cloud size by 80-95%. Besides this, we introduce a method for online rendering of extra large point clouds enabling real-time visualization of huge cloud spaces in conventional web browsers.

  3. Recording Approach of Heritage Sites Based on Merging Point Clouds from High Resolution Photogrammetry and Terrestrial Laser Scanning

    NASA Astrophysics Data System (ADS)

    Grussenmeyer, P.; Alby, E.; Landes, T.; Koehl, M.; Guillemin, S.; Hullo, J. F.; Assali, P.; Smigiel, E.

    2012-07-01

    Different approaches and tools are required in Cultural Heritage Documentation to deal with the complexity of monuments and sites. The documentation process has strongly changed in the last few years, always driven by technology. Accurate documentation is closely relied to advances of technology (imaging sensors, high speed scanning, automation in recording and processing data) for the purposes of conservation works, management, appraisal, assessment of the structural condition, archiving, publication and research (Patias et al., 2008). We want to focus in this paper on the recording aspects of cultural heritage documentation, especially the generation of geometric and photorealistic 3D models for accurate reconstruction and visualization purposes. The selected approaches are based on the combination of photogrammetric dense matching and Terrestrial Laser Scanning (TLS) techniques. Both techniques have pros and cons and recent advances have changed the way of the recording approach. The choice of the best workflow relies on the site configuration, the performances of the sensors, and criteria as geometry, accuracy, resolution, georeferencing, texture, and of course processing time. TLS techniques (time of flight or phase shift systems) are widely used for recording large and complex objects and sites. Point cloud generation from images by dense stereo or multi-view matching can be used as an alternative or as a complementary method to TLS. Compared to TLS, the photogrammetric solution is a low cost one, as the acquisition system is limited to a high-performance digital camera and a few accessories only. Indeed, the stereo or multi-view matching process offers a cheap, flexible and accurate solution to get 3D point clouds. Moreover, the captured images might also be used for models texturing. Several software packages are available, whether web-based, open source or commercial. The main advantage of this photogrammetric or computer vision based technology is to

  4. The kinematical properties of superbubbles and H II regions of the Large Magellanic Cloud derived from the 3D Hα Survey

    NASA Astrophysics Data System (ADS)

    Ambrocio-Cruz, P.; Le Coarer, E.; Rosado, M.; Russeil, D.; Amram, P.; Laval, A.; Epinat, B.; Ramírez, M.; Odonne, M.; Goldes, G.

    2016-04-01

    We report the results of a kinematical Hα survey of the Large Magellanic Cloud (LMC) presented in the form of a kinematical and photometric catalogue of 210 H II regions. The observations have been obtained with a scanning Fabry-Perot interferometer that produced data cubes corresponding to 66 different pointings over this galaxy, each with a field of view of 38 arcmin, covering almost the whole extent of the LMC. We find a bimodal distribution of the Hα luminosity of LMC H II regions. We also derive the local star formation and star formation rate (SFR) per unit area of the nebulae, concluding that star formation in the LMC has proceeded until the present time at an average rate of roughly 0.11 M⊙ yr-1. Also, we do not find any correlation between the SFR or ΣSFR with ΔV (full width at half-maximum for a single Gaussian profile and the difference in velocities for multiple-components velocity profiles), the diameter, the distance to the kinematical centre of the LMC and age of the nebulae. Over most of the LMC ΔV appears to be of the order of 30 km s-1. However, in a few regions the ΔV of the velocity profiles is as large as 50-100 kms-1, corresponding to identified supernova remnants and superbubbles undergoing expansion motions.

  5. Drogue tracking using 3D flash lidar for autonomous aerial refueling

    NASA Astrophysics Data System (ADS)

    Chen, Chao-I.; Stettner, Roger

    2011-06-01

    Autonomous aerial refueling (AAR) is an important capability for an unmanned aerial vehicle (UAV) to increase its flying range and endurance without increasing its size. This paper presents a novel tracking method that utilizes both 2D intensity and 3D point-cloud data acquired with a 3D Flash LIDAR sensor to establish relative position and orientation between the receiver vehicle and drogue during an aerial refueling process. Unlike classic, vision-based sensors, a 3D Flash LIDAR sensor can provide 3D point-cloud data in real time without motion blur, in the day or night, and is capable of imaging through fog and clouds. The proposed method segments out the drogue through 2D analysis and estimates the center of the drogue from 3D point-cloud data for flight trajectory determination. A level-set front propagation routine is first employed to identify the target of interest and establish its silhouette information. Sufficient domain knowledge, such as the size of the drogue and the expected operable distance, is integrated into our approach to quickly eliminate unlikely target candidates. A statistical analysis along with a random sample consensus (RANSAC) is performed on the target to reduce noise and estimate the center of the drogue after all 3D points on the drogue are identified. The estimated center and drogue silhouette serve as the seed points to efficiently locate the target in the next frame.

  6. Dynamic occlusion detection and inpainting of in situ captured terrestrial laser scanning point clouds sequence

    NASA Astrophysics Data System (ADS)

    Chen, Chi; Yang, Bisheng

    2016-09-01

    Laser point clouds captured using terrestrial laser scanning (TLS) in an uncontrollable urban outdoor or indoor scene suffer from irregular shaped data blanks caused by dynamic occlusion that temporarily exists, i.e., moving objects, such as pedestrians or cars, resulting in integrality and quality losses of the scene data. This paper proposes a novel automatic dynamic occlusion detection and inpainting method for sequential TLS point clouds captured from one scan position. In situ collected laser point clouds sequences are indexed by establishing a novel panoramic space partition that assigns a three dimensional voxel to each laser point according to the scanning setups. Then two stationary background models are constructed at the ray voxel level using the laser reflectance intensity and geometrical attributes of the point set inside each voxel across the TLS sequence. Finally, the background models are combined to detect the points on the dynamic object, and the ray voxels of the detected dynamic points are tracked for further inpainting by replacing the ray voxels with the corresponding background voxels from another scan. The resulting scene is free of dynamic occlusions. Experiments validated the effectiveness of the proposed method for indoor and outdoor TLS point clouds captured by a commercial terrestrial scanner. The proposed method achieves high precision and recall rate for dynamic occlusion detection and produces clean inpainted point clouds for further processing.

  7. 3D reconstruction and visualization of plant leaves

    NASA Astrophysics Data System (ADS)

    Gu, Xiaomeng; Xu, Lihong; Li, Dawei; Zhang, Peng

    2015-03-01

    In this paper, a three-dimensional reconstruction method, which is based on point clouds and texture images, is used to realize the visualization of leaves of greenhouse crops. We take Epipremnum aureum as the object for study and focus on applying the triangular meshing method to organize and categorize scattered point cloud input data of leaves, and then construct a triangulated surface with interconnection topology to simulate the real surface of the object. At last we texture-map the leaf surface with real images to present a life-like 3D model which can be used to simulate the growth of greenhouse plants.

  8. A point-cloud-based multiview stereo algorithm for free-viewpoint video.

    PubMed

    Liu, Yebin; Dai, Qionghai; Xu, Wenli

    2010-01-01

    This paper presents a robust multiview stereo (MVS) algorithm for free-viewpoint video. Our MVS scheme is totally point-cloud-based and consists of three stages: point cloud extraction, merging, and meshing. To guarantee reconstruction accuracy, point clouds are first extracted according to a stereo matching metric which is robust to noise, occlusion, and lack of texture. Visual hull information, frontier points, and implicit points are then detected and fused with point fidelity information in the merging and meshing steps. All aspects of our method are designed to counteract potential challenges in MVS data sets for accurate and complete model reconstruction. Experimental results demonstrate that our technique produces the most competitive performance among current algorithms under sparse viewpoint setups according to both static and motion MVS data sets.

  9. Automated extraction of urban trees from mobile LiDAR point clouds

    NASA Astrophysics Data System (ADS)

    Fan, W.; Chenglu, W.; Jonathan, L.

    2016-03-01

    This paper presents an automatic algorithm to localize and extract urban trees from mobile LiDAR point clouds. First, in order to reduce the number of points to be processed, the ground points are filtered out from the raw point clouds, and the un-ground points are segmented into supervoxels. Then, a novel localization method is proposed to locate the urban trees accurately. Next, a segmentation method by localization is proposed to achieve objects. Finally, the features of objects are extracted, and the feature vectors are classified by random forests trained on manually labeled objects. The proposed method has been tested on a point cloud dataset. The results prove that our algorithm efficiently extracts the urban trees.

  10. An interactive mapping tool for visualizing lacunarity of laser scanned point clouds

    NASA Astrophysics Data System (ADS)

    Kania, Adam; Székely, Balázs

    2016-04-01

    Lacunarity, a measure of the spatial distribution of the empty space in a certain model or real space over large spatial scales, is found to be a useful descriptive quantity in many fields using imagery, including, among others, geology, dentistry, neurology. Its application in ecology was suggested more than 20 years ago. The main problem of its application was the lack of appropriate high resolution data. Nowadays, full-waveform laser scanning, also known as FWF LiDAR, provides the tool for mapping the vegetation in unprecedented details and accuracy. Consequently, the lacunarity concept can be revitalized, in order to study the structure of the vegetation in this sense as well. Calculation of lacunarity, even if it is done in two dimensions (2D), is still has its problems: on one hand it is a number-crunching procedure, on the other hand, it produces 4D results: at each 3D point it returns a set of data that are function of scale. These data sets are difficult to visualize, to evaluate, and to compare. In order to solve this problem, an interactive mapping tool has been conceptualized that is designed to manipulate and visualize the data, lets the user set parameters for best visualization or comparison results. The system is able to load large amounts of data, visualize them as lacunarity curves, or map view as horizontal slices or in 3D point clouds coloured according to the user's choice. Lacunarity maps are presented as a series of (usually) horizontal profiles, e.g. rasters, which cells contain color-mapped values of selected lacunarity of the point cloud. As lacunarity is usually analysed in a series of successive windows sizes, the tool can show a series of rasters with sequentially animated lacunarity maps calculated for various window sizes. A very fast switching of colour schemes is possible to facilitate rapid visual feedback to better understand underlying data patterns exposed by lacunarity functions. In the comparison mode, two sites (or two areas

  11. A 3D measurement method based on multi-view fringe projection by using a turntable

    NASA Astrophysics Data System (ADS)

    Song, Li-mei; Gao, Yan-yan; Zhu, Xin-jun; Guo, Qing-hua; Xi, Jiang-tao

    2016-09-01

    In order to get the entire data in the optical measurement, a multi-view three-dimensional (3D) measurement method based on turntable is proposed. In the method, a turntable is used to rotate the object and obtain multi-view point cloud data, and then multi-view point cloud data are registered and integrated into a 3D model. The measurement results are compared with that of the sticking marked point method. Experimental results show that the measurement process of the proposed method is simpler, and the scanning speed and accuracy are improved.

  12. Using LIDAR and UAV-derived point clouds to evaluate surface roughness in a gravel-bed braided river (Vénéon river, French Alps)

    NASA Astrophysics Data System (ADS)

    Vázquez Tarrío, Daniel; Borgniet, Laurent; Recking, Alain; Liebault, Frédéric; Vivier, Marie

    2016-04-01

    The present research is focused on the Vénéon river at Plan du Lac (Massif des Ecrins, France), an alpine braided gravel bed stream with a glacio-nival hydrological regime. It drains a catchment area of 316 km2. The present research is focused in a 2.5 km braided reach placed immediately upstream of a small hydropower dam. An airbone LIDAR survey was accomplished in October, 2014 by EDF (the company managing the small hydropower dam), and data coming from this LIDAR survey were available for the present research. Point density of the LIDAR-derived 3D-point cloud was between 20-50 points/m2, with a vertical precision of 2-3 cm over flat surfaces. Moreover, between April and Juin, 2015, we carried out a photogrammetrical campaign based in aerial images taken with an UAV-drone. The UAV-derived point-cloud has a point density of 200-300 points/m2, and a vertical precision over flat control surfaces comparable to that of the LIDAR point cloud (2-3 cm). Simultaneously to the UAV campaign, we took several Wolman samples with the aim of characterizing the grain size distribution of bed sediment. Wolman samples were taken following a geomorphological criterion (unit bars, head/tail of compound bars). Furthermore, some of the Wolman samples were repeated with the aim of defining the uncertainty of our sampling protocol. LIDAR and UAV-derived point clouds were treated in order to check whether both point-clouds were correctly co-aligned. After that, we estimated bed roughness using the detrended standard deviation of heights, in a 40-cm window. For all this data treatment we used CloudCompare. Then, we measured the distribution of roughness in the same geomorphological units where we took the Wolman samples, and we compared with the grain size distributions measured in the field: differences between UAV-point cloud roughness distributions and measured-grain size distribution (~1-2 cm) are in the same order of magnitude of the differences found between the repeated Wolman

  13. [The processing of point clouds for brain deformation existing in image guided neurosurgery system].

    PubMed

    Yao, Xufeng; Lin, Yixun; Song, Zhijian

    2008-08-01

    The finite element method (FEM) plays an important role in solving the brain deformation problem in the image guided neurosurgery system. The position of the brain cortex during the surgery provides the boundary condition for the FEM model. In this paper, the information of brain cortex is represented by the unstructured points and the boundary condition is achieved by the processing of unstructured points. The processing includes the mapping of texture, segmentation, simplification and denoising. The method of k-nearest clustering based on local surface properties is used to simplify and denoise the unstructured point clouds. The results of experiment prove the efficiency of point clouds processing.

  14. Attempt of UAV oblique images and MLS point clouds for 4D modelling of roadside pole-like objects

    NASA Astrophysics Data System (ADS)

    Lin, Yi; West, Geoff

    2014-11-01

    The state-of-the-art remote sensing technologies, namely Unmanned Aerial Vehicle (UAV) based oblique imaging and Mobile Laser Scanning (MLS) show great potential for spatial information acquisition. This study investigated the combination of the two data sources for 4D modelling of roadside pole-like objects. The data for the analysis were collected by the Microdrone md4-200 UAV imaging system and the Sensei MLS system developed by the Finnish Geodetic Institute. Pole extraction, 3D structural parameter derivation and texture segmentation were deployed on the oblique images and point clouds, and their results were fused to yield the 4D models for one example of pole-like objects, namely lighting poles. The combination techniques proved promising.

  15. Local surface sampling step estimation for extracting boundaries of planar point clouds

    NASA Astrophysics Data System (ADS)

    Brie, David; Bombardier, Vincent; Baeteman, Grégory; Bennis, Abdelhamid

    2016-09-01

    This paper presents a new approach to estimate the surface sampling step of planar point clouds acquired by Terrestrial Laser Scanner (TLS) which is varying with the distance to the surface and the angular positions. The local surface sampling step is obtained by doing a first order Taylor expansion of planar point coordinates. Then, it is shown how to use it in Delaunay-based boundary point extraction. The resulting approach, which is implemented in the ModiBuilding software, is applied to two facade point clouds of a building. The first is acquired with a single station and the second with two stations. In both cases, the proposed approach performs very accurately and appears to be robust to the variations of the point cloud density.

  16. Object-Based Coregistration of Terrestrial Photogrammetric and ALS Point Clouds in Forested Areas

    NASA Astrophysics Data System (ADS)

    Polewski, P.; Erickson, A.; Yao, W.; Coops, N.; Krzystek, P.; Stilla, U.

    2016-06-01

    Airborne Laser Scanning (ALS) and terrestrial photogrammetry are methods applicable for mapping forested environments. While ground-based techniques provide valuable information about the forest understory, the measured point clouds are normally expressed in a local coordinate system, whose transformation into a georeferenced system requires additional effort. In contrast, ALS point clouds are usually georeferenced, yet the point density near the ground may be poor under dense overstory conditions. In this work, we propose to combine the strengths of the two data sources by co-registering the respective point clouds, thus enriching the georeferenced ALS point cloud with detailed understory information in a fully automatic manner. Due to markedly different sensor characteristics, coregistration methods which expect a high geometric similarity between keypoints are not suitable in this setting. Instead, our method focuses on the object (tree stem) level. We first calculate approximate stem positions in the terrestrial and ALS point clouds and construct, for each stem, a descriptor which quantifies the 2D and vertical distances to other stem centers (at ground height). Then, the similarities between all descriptor pairs from the two point clouds are calculated, and standard graph maximum matching techniques are employed to compute corresponding stem pairs (tiepoints). Finally, the tiepoint subset yielding the optimal rigid transformation between the terrestrial and ALS coordinate systems is determined. We test our method on simulated tree positions and a plot situated in the northern interior of the Coast Range in western Oregon, USA, using ALS data (76 x 121 m2) and a photogrammetric point cloud (33 x 35 m2) derived from terrestrial photographs taken with a handheld camera. Results on both simulated and real data show that the proposed stem descriptors are discriminative enough to derive good correspondences. Specifically

  17. The effect of convective life cycle stage on microwave brightness temperature/rainrate relations as determined from 3-D cloud model results

    NASA Technical Reports Server (NTRS)

    Adler, Robert F.; Tao, Wei-Kuo; Simpson, Joanne; Prasad, N.; Yeh, H.-Y. M.

    1990-01-01

    The relationship between the rain rate and the brightness temperature (Tb) was investigated using a cloud model/microwave radiative transfer model combination to obtain the rain-rate/Tb relations for four different frequencies: 10, 19, 37, and 86 GHz. The results at 19, 37, and 86 GHz were found to be significantly affected by ice in the modeled convective system, while the results at 10 GHz showed very little effect. Nonprecipitating cloud water was found to affect Tb in two ways. First, at low rain rates, the presence of significant cloud water produced higher Tb values than in cases with little cloud water. The second effects occurs at 19, 37, and 86 GHz at higher rainrates associated with significant ice formation; the scattering by ice lowered the Tb.

  18. Accuracy analysis of height difference models derived from terrestrial laser scanning point clouds

    NASA Astrophysics Data System (ADS)

    Glira, Philipp; Briese, Christian; Pfeifer, Norbert; Dusik, Jana; Hilger, Ludwig; Neugirg, Fabian; Baewert, Henning

    2014-05-01

    In many research areas the temporal development of the earth surface topography is investigated for geomorphological analysis (e.g. landslide monitoring