Sample records for point cloud registration

  1. 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.

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

    NASA Astrophysics Data System (ADS)

    Wang, Shuai; Sun, Huayan; Guo, Huichao

    2018-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  4. 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.

  5. FPFH-based graph matching for 3D point cloud registration

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  6. Registration of Laser Scanning Point Clouds: A Review

    PubMed Central

    Cheng, Liang; Chen, Song; Xu, Hao; Wu, Yang; Li, Manchun

    2018-01-01

    The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles. PMID:29883397

  7. Registration of Laser Scanning Point Clouds: A Review.

    PubMed

    Cheng, Liang; Chen, Song; Liu, Xiaoqiang; Xu, Hao; Wu, Yang; Li, Manchun; Chen, Yanming

    2018-05-21

    The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

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

    PubMed

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

    2016-12-30

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

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

    PubMed Central

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

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

    PubMed

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

    2017-08-11

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

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

    PubMed Central

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Ge, Xuming

    2017-08-01

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

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

    PubMed

    Petricek, Tomas; Svoboda, Tomas

    2017-01-01

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

  15. 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.

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

    PubMed Central

    Yan, Li; Xie, Hong; Chen, Changjun

    2017-01-01

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

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

    PubMed

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

    2017-08-29

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

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

    NASA Astrophysics Data System (ADS)

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

    2018-03-01

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

  19. 3D point cloud analysis of structured light registration in computer-assisted navigation in spinal surgeries

    NASA Astrophysics Data System (ADS)

    Gupta, Shaurya; Guha, Daipayan; Jakubovic, Raphael; Yang, Victor X. D.

    2017-02-01

    Computer-assisted navigation is used by surgeons in spine procedures to guide pedicle screws to improve placement accuracy and in some cases, to better visualize patient's underlying anatomy. Intraoperative registration is performed to establish a correlation between patient's anatomy and the pre/intra-operative image. Current algorithms rely on seeding points obtained directly from the exposed spinal surface to achieve clinically acceptable registration accuracy. Registration of these three dimensional surface point-clouds are prone to various systematic errors. The goal of this study was to evaluate the robustness of surgical navigation systems by looking at the relationship between the optical density of an acquired 3D point-cloud and the corresponding surgical navigation error. A retrospective review of a total of 48 registrations performed using an experimental structured light navigation system developed within our lab was conducted. For each registration, the number of points in the acquired point cloud was evaluated relative to whether the registration was acceptable, the corresponding system reported error and target registration error. It was demonstrated that the number of points in the point cloud neither correlates with the acceptance/rejection of a registration or the system reported error. However, a negative correlation was observed between the number of the points in the point-cloud and the corresponding sagittal angular error. Thus, system reported total registration points and accuracy are insufficient to gauge the accuracy of a navigation system and the operating surgeon must verify and validate registration based on anatomical landmarks prior to commencing surgery.

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

    NASA Astrophysics Data System (ADS)

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

    2017-12-01

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

  1. Automatic Registration of Terrestrial Laser Scanner Point Clouds Using Natural Planar Surfaces

    NASA Astrophysics Data System (ADS)

    Theiler, P. W.; Schindler, K.

    2012-07-01

    Terrestrial laser scanners have become a standard piece of surveying equipment, used in diverse fields like geomatics, manufacturing and medicine. However, the processing of today's large point clouds is time-consuming, cumbersome and not automated enough. A basic step of post-processing is the registration of scans from different viewpoints. At present this is still done using artificial targets or tie points, mostly by manual clicking. The aim of this registration step is a coarse alignment, which can then be improved with the existing algorithm for fine registration. The focus of this paper is to provide such a coarse registration in a fully automatic fashion, and without placing any target objects in the scene. The basic idea is to use virtual tie points generated by intersecting planar surfaces in the scene. Such planes are detected in the data with RANSAC and optimally fitted using least squares estimation. Due to the huge amount of recorded points, planes can be determined very accurately, resulting in well-defined tie points. Given two sets of potential tie points recovered in two different scans, registration is performed by searching for the assignment which preserves the geometric configuration of the largest possible subset of all tie points. Since exhaustive search over all possible assignments is intractable even for moderate numbers of points, the search is guided by matching individual pairs of tie points with the help of a novel descriptor based on the properties of a point's parent planes. Experiments show that the proposed method is able to successfully coarse register TLS point clouds without the need for artificial targets.

  2. A novel point cloud registration using 2D image features

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  3. Automatic registration of terrestrial point clouds based on panoramic reflectance images and efficient BaySAC

    NASA Astrophysics Data System (ADS)

    Kang, Zhizhong

    2013-10-01

    This paper presents a new approach to automatic registration of terrestrial laser scanning (TLS) point clouds utilizing a novel robust estimation method by an efficient BaySAC (BAYes SAmpling Consensus). The proposed method directly generates reflectance images from 3D point clouds, and then using SIFT algorithm extracts keypoints to identify corresponding image points. The 3D corresponding points, from which transformation parameters between point clouds are computed, are acquired by mapping the 2D ones onto the point cloud. To remove false accepted correspondences, we implement a conditional sampling method to select the n data points with the highest inlier probabilities as a hypothesis set and update the inlier probabilities of each data point using simplified Bayes' rule for the purpose of improving the computation efficiency. The prior probability is estimated by the verification of the distance invariance between correspondences. The proposed approach is tested on four data sets acquired by three different scanners. The results show that, comparing with the performance of RANSAC, BaySAC leads to less iterations and cheaper computation cost when the hypothesis set is contaminated with more outliers. The registration results also indicate that, the proposed algorithm can achieve high registration accuracy on all experimental datasets.

  4. LiDAR Point Cloud and Stereo Image Point Cloud Fusion

    DTIC Science & Technology

    2013-09-01

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

  5. 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

  6. Automated Coarse Registration of Point Clouds in 3d Urban Scenes Using Voxel Based Plane Constraint

    NASA Astrophysics Data System (ADS)

    Xu, Y.; Boerner, R.; Yao, W.; Hoegner, L.; Stilla, U.

    2017-09-01

    For obtaining a full coverage of 3D scans in a large-scale urban area, the registration between point clouds acquired via terrestrial laser scanning (TLS) is normally mandatory. However, due to the complex urban environment, the automatic registration of different scans is still a challenging problem. In this work, we propose an automatic marker free method for fast and coarse registration between point clouds using the geometric constrains of planar patches under a voxel structure. Our proposed method consists of four major steps: the voxelization of the point cloud, the approximation of planar patches, the matching of corresponding patches, and the estimation of transformation parameters. In the voxelization step, the point cloud of each scan is organized with a 3D voxel structure, by which the entire point cloud is partitioned into small individual patches. In the following step, we represent points of each voxel with the approximated plane function, and select those patches resembling planar surfaces. Afterwards, for matching the corresponding patches, a RANSAC-based strategy is applied. Among all the planar patches of a scan, we randomly select a planar patches set of three planar surfaces, in order to build a coordinate frame via their normal vectors and their intersection points. The transformation parameters between scans are calculated from these two coordinate frames. The planar patches set with its transformation parameters owning the largest number of coplanar patches are identified as the optimal candidate set for estimating the correct transformation parameters. The experimental results using TLS datasets of different scenes reveal that our proposed method can be both effective and efficient for the coarse registration task. Especially, for the fast orientation between scans, our proposed method can achieve a registration error of less than around 2 degrees using the testing datasets, and much more efficient than the classical baseline methods.

  7. 2.5D multi-view gait recognition based on point cloud registration.

    PubMed

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

    2014-03-28

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

  8. 2.5D Multi-View Gait Recognition Based on Point Cloud Registration

    PubMed Central

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

    2014-01-01

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

  9. Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations

    PubMed Central

    Yao, Lianbi; Wu, Hangbin; Li, Yayun; Meng, Bin; Qian, Jinfei; Liu, Chun; Fan, Hongchao

    2017-01-01

    A mobile mapping system (MMS) is usually utilized to collect environmental data on and around urban roads. Laser scanners and panoramic cameras are the main sensors of an MMS. This paper presents a new method for the registration of the point clouds and panoramic images based on sensor constellation. After the sensor constellation was analyzed, a feature point, the intersection of the connecting line between the global positioning system (GPS) antenna and the panoramic camera with a horizontal plane, was utilized to separate the point clouds into blocks. The blocks for the central and sideward laser scanners were extracted with the segmentation feature points. Then, the point clouds located in the blocks were separated from the original point clouds. Each point in the blocks was used to find the accurate corresponding pixel in the relative panoramic images via a collinear function, and the position and orientation relationship amongst different sensors. A search strategy is proposed for the correspondence of laser scanners and lenses of panoramic cameras to reduce calculation complexity and improve efficiency. Four cases of different urban road types were selected to verify the efficiency and accuracy of the proposed method. Results indicate that most of the point clouds (with an average of 99.7%) were successfully registered with the panoramic images with great efficiency. Geometric evaluation results indicate that horizontal accuracy was approximately 0.10–0.20 m, and vertical accuracy was approximately 0.01–0.02 m for all cases. Finally, the main factors that affect registration accuracy, including time synchronization amongst different sensors, system positioning and vehicle speed, are discussed. PMID:28398256

  10. Registration of Vehicle-Borne Point Clouds and Panoramic Images Based on Sensor Constellations.

    PubMed

    Yao, Lianbi; Wu, Hangbin; Li, Yayun; Meng, Bin; Qian, Jinfei; Liu, Chun; Fan, Hongchao

    2017-04-11

    A mobile mapping system (MMS) is usually utilized to collect environmental data on and around urban roads. Laser scanners and panoramic cameras are the main sensors of an MMS. This paper presents a new method for the registration of the point clouds and panoramic images based on sensor constellation. After the sensor constellation was analyzed, a feature point, the intersection of the connecting line between the global positioning system (GPS) antenna and the panoramic camera with a horizontal plane, was utilized to separate the point clouds into blocks. The blocks for the central and sideward laser scanners were extracted with the segmentation feature points. Then, the point clouds located in the blocks were separated from the original point clouds. Each point in the blocks was used to find the accurate corresponding pixel in the relative panoramic images via a collinear function, and the position and orientation relationship amongst different sensors. A search strategy is proposed for the correspondence of laser scanners and lenses of panoramic cameras to reduce calculation complexity and improve efficiency. Four cases of different urban road types were selected to verify the efficiency and accuracy of the proposed method. Results indicate that most of the point clouds (with an average of 99.7%) were successfully registered with the panoramic images with great efficiency. Geometric evaluation results indicate that horizontal accuracy was approximately 0.10-0.20 m, and vertical accuracy was approximately 0.01-0.02 m for all cases. Finally, the main factors that affect registration accuracy, including time synchronization amongst different sensors, system positioning and vehicle speed, are discussed.

  11. 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

  12. A three dimensional point cloud registration method based on rotation matrix eigenvalue

    NASA Astrophysics Data System (ADS)

    Wang, Chao; Zhou, Xiang; Fei, Zixuan; Gao, Xiaofei; Jin, Rui

    2017-09-01

    We usually need to measure an object at multiple angles in the traditional optical three-dimensional measurement method, due to the reasons for the block, and then use point cloud registration methods to obtain a complete threedimensional shape of the object. The point cloud registration based on a turntable is essential to calculate the coordinate transformation matrix between the camera coordinate system and the turntable coordinate system. We usually calculate the transformation matrix by fitting the rotation center and the rotation axis normal of the turntable in the traditional method, which is limited by measuring the field of view. The range of exact feature points used for fitting the rotation center and the rotation axis normal is approximately distributed within an arc less than 120 degrees, resulting in a low fit accuracy. In this paper, we proposes a better method, based on the invariant eigenvalue principle of rotation matrix in the turntable coordinate system and the coordinate transformation matrix of the corresponding coordinate points. First of all, we control the rotation angle of the calibration plate with the turntable to calibrate the coordinate transformation matrix of the corresponding coordinate points by using the least squares method. And then we use the feature decomposition to calculate the coordinate transformation matrix of the camera coordinate system and the turntable coordinate system. Compared with the traditional previous method, it has a higher accuracy, better robustness and it is not affected by the camera field of view. In this method, the coincidence error of the corresponding points on the calibration plate after registration is less than 0.1mm.

  13. Speeding up Coarse Point Cloud Registration by Threshold-Independent Baysac Match Selection

    NASA Astrophysics Data System (ADS)

    Kang, Z.; Lindenbergh, R.; Pu, S.

    2016-06-01

    This paper presents an algorithm for the automatic registration of terrestrial point clouds by match selection using an efficiently conditional sampling method -- threshold-independent BaySAC (BAYes SAmpling Consensus) and employs the error metric of average point-to-surface residual to reduce the random measurement error and then approach the real registration error. BaySAC and other basic sampling algorithms usually need to artificially determine a threshold by which inlier points are identified, which leads to a threshold-dependent verification process. Therefore, we applied the LMedS method to construct the cost function that is used to determine the optimum model to reduce the influence of human factors and improve the robustness of the model estimate. Point-to-point and point-to-surface error metrics are most commonly used. However, point-to-point error in general consists of at least two components, random measurement error and systematic error as a result of a remaining error in the found rigid body transformation. Thus we employ the measure of the average point-to-surface residual to evaluate the registration accuracy. The proposed approaches, together with a traditional RANSAC approach, are tested on four data sets acquired by three different scanners in terms of their computational efficiency and quality of the final registration. The registration results show the st.dev of the average point-to-surface residuals is reduced from 1.4 cm (plain RANSAC) to 0.5 cm (threshold-independent BaySAC). The results also show that, compared to the performance of RANSAC, our BaySAC strategies lead to less iterations and cheaper computational cost when the hypothesis set is contaminated with more outliers.

  14. Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS.

    PubMed

    Chen, Maolin; Wang, Siying; Wang, Mingwei; Wan, Youchuan; He, Peipei

    2017-01-20

    Automatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and height differences, which is a novel sensor combination mode for terrestrial laser scanning. The approximate distance between two neighboring scan positions is firstly calculated with smartphone GPS coordinates. Then, 2D distribution entropy is used to measure the distribution coherence between the two scans and search for the optimal initial transformation parameters. To this end, we propose a method called Iterative Minimum Entropy (IME) to correct initial transformation parameters based on two criteria: the difference between the average and minimum entropy and the deviation from the minimum entropy to the expected entropy. Finally, the presented method is evaluated using two data sets that contain tens of millions of points from panoramic and non-panoramic, vegetation-dominated and building-dominated cases and can achieve high accuracy and efficiency.

  15. 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.

  16. 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.

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

    PubMed Central

    Yang, Qiyao; Wang, Zhiguo; Zhang, Guoxu

    2017-01-01

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

  18. 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.

  19. Comparison of Point Cloud Registration Algorithms for Better Result Assessment - Towards AN Open-Source Solution

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

    Terrestrial and airborne laser scanning, photogrammetry and more generally 3D recording techniques are used in a wide range of applications. After recording several individual 3D datasets known in local systems, one of the first crucial processing steps is the registration of these data into a common reference frame. To perform such a 3D transformation, commercial and open source software as well as programs from the academic community are available. Due to some lacks in terms of computation transparency and quality assessment in these solutions, it has been decided to develop an open source algorithm which is presented in this paper. It is dedicated to the simultaneous registration of multiple point clouds as well as their georeferencing. The idea is to use this algorithm as a start point for further implementations, involving the possibility of combining 3D data from different sources. Parallel to the presentation of the global registration methodology which has been employed, the aim of this paper is to confront the results achieved this way with the above-mentioned existing solutions. For this purpose, first results obtained with the proposed algorithm to perform the global registration of ten laser scanning point clouds are presented. An analysis of the quality criteria delivered by two selected software used in this study and a reflexion about these criteria is also performed to complete the comparison of the obtained results. The final aim of this paper is to validate the current efficiency of the proposed method through these comparisons.

  20. Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping.

    PubMed

    Cui, Tingting; Ji, Shunping; Shan, Jie; Gong, Jianya; Liu, Kejian

    2016-12-31

    For multi-sensor integrated systems, such as the mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable.

  1. Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping

    PubMed Central

    Cui, Tingting; Ji, Shunping; Shan, Jie; Gong, Jianya; Liu, Kejian

    2016-01-01

    For multi-sensor integrated systems, such as the mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable. PMID:28042855

  2. Point Cloud Management Through the Realization of the Intelligent Cloud Viewer Software

    NASA Astrophysics Data System (ADS)

    Costantino, D.; Angelini, M. G.; Settembrini, F.

    2017-05-01

    The paper presents a software dedicated to the elaboration of point clouds, called Intelligent Cloud Viewer (ICV), made in-house by AESEI software (Spin-Off of Politecnico di Bari), allowing to view point cloud of several tens of millions of points, also on of "no" very high performance systems. The elaborations are carried out on the whole point cloud and managed by means of the display only part of it in order to speed up rendering. It is designed for 64-bit Windows and is fully written in C ++ and integrates different specialized modules for computer graphics (Open Inventor by SGI, Silicon Graphics Inc), maths (BLAS, EIGEN), computational geometry (CGAL, Computational Geometry Algorithms Library), registration and advanced algorithms for point clouds (PCL, Point Cloud Library), advanced data structures (BOOST, Basic Object Oriented Supporting Tools), etc. ICV incorporates a number of features such as, for example, cropping, transformation and georeferencing, matching, registration, decimation, sections, distances calculation between clouds, etc. It has been tested on photographic and TLS (Terrestrial Laser Scanner) data, obtaining satisfactory results. The potentialities of the software have been tested by carrying out the photogrammetric survey of the Castel del Monte which was already available in previous laser scanner survey made from the ground by the same authors. For the aerophotogrammetric survey has been adopted a flight height of approximately 1000ft AGL (Above Ground Level) and, overall, have been acquired over 800 photos in just over 15 minutes, with a covering not less than 80%, the planned speed of about 90 knots.

  3. 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

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

    NASA Astrophysics Data System (ADS)

    Lorsakul, Auranuch; Suthakorn, Jackrit; Sinthanayothin, Chanjira

    2008-03-01

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

  5. Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method

    PubMed Central

    Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

    2016-01-01

    A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis. PMID:28029121

  6. Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method.

    PubMed

    Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

    2016-12-24

    A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.

  7. Iterative Most-Likely Point Registration (IMLP): A Robust Algorithm for Computing Optimal Shape Alignment

    PubMed Central

    Billings, Seth D.; Boctor, Emad M.; Taylor, Russell H.

    2015-01-01

    We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP’s probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes. PMID:25748700

  8. 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

  9. Automatic co-registration of 3D multi-sensor point clouds

    NASA Astrophysics Data System (ADS)

    Persad, Ravi Ancil; Armenakis, Costas

    2017-08-01

    We propose an approach for the automatic coarse alignment of 3D point clouds which have been acquired from various platforms. The method is based on 2D keypoint matching performed on height map images of the point clouds. Initially, a multi-scale wavelet keypoint detector is applied, followed by adaptive non-maxima suppression. A scale, rotation and translation-invariant descriptor is then computed for all keypoints. The descriptor is built using the log-polar mapping of Gabor filter derivatives in combination with the so-called Rapid Transform. In the final step, source and target height map keypoint correspondences are determined using a bi-directional nearest neighbour similarity check, together with a threshold-free modified-RANSAC. Experiments with urban and non-urban scenes are presented and results show scale errors ranging from 0.01 to 0.03, 3D rotation errors in the order of 0.2° to 0.3° and 3D translation errors from 0.09 m to 1.1 m.

  10. Automatic Recognition of Indoor Navigation Elements from Kinect Point Clouds

    NASA Astrophysics Data System (ADS)

    Zeng, L.; Kang, Z.

    2017-09-01

    This paper realizes automatically the navigating elements defined by indoorGML data standard - door, stairway and wall. The data used is indoor 3D point cloud collected by Kinect v2 launched in 2011 through the means of ORB-SLAM. By contrast, it is cheaper and more convenient than lidar, but the point clouds also have the problem of noise, registration error and large data volume. Hence, we adopt a shape descriptor - histogram of distances between two randomly chosen points, proposed by Osada and merges with other descriptor - in conjunction with random forest classifier to recognize the navigation elements (door, stairway and wall) from Kinect point clouds. This research acquires navigation elements and their 3-d location information from each single data frame through segmentation of point clouds, boundary extraction, feature calculation and classification. Finally, this paper utilizes the acquired navigation elements and their information to generate the state data of the indoor navigation module automatically. The experimental results demonstrate a high recognition accuracy of the proposed method.

  11. A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window

    NASA Astrophysics Data System (ADS)

    Xin, Meiting; Li, Bing; Yan, Xiao; Chen, Lei; Wei, Xiang

    2018-02-01

    A robust coarse-to-fine registration method based on the backpropagation (BP) neural network and shift window technology is proposed in this study. Specifically, there are three steps: coarse alignment between the model data and measured data, data simplification based on the BP neural network and point reservation in the contour region of point clouds, and fine registration with the reweighted iterative closest point algorithm. In the process of rough alignment, the initial rotation matrix and the translation vector between the two datasets are obtained. After performing subsequent simplification operations, the number of points can be reduced greatly. Therefore, the time and space complexity of the accurate registration can be significantly reduced. The experimental results show that the proposed method improves the computational efficiency without loss of accuracy.

  12. 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.

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

    NASA Astrophysics Data System (ADS)

    Stöcker, Claudia; Eltner, Anette

    2016-04-01

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

  14. Large-Scale Point-Cloud Visualization through Localized Textured Surface Reconstruction.

    PubMed

    Arikan, Murat; Preiner, Reinhold; Scheiblauer, Claus; Jeschke, Stefan; Wimmer, Michael

    2014-09-01

    In this paper, we introduce a novel scene representation for the visualization of large-scale point clouds accompanied by a set of high-resolution photographs. Many real-world applications deal with very densely sampled point-cloud data, which are augmented with photographs that often reveal lighting variations and inaccuracies in registration. Consequently, the high-quality representation of the captured data, i.e., both point clouds and photographs together, is a challenging and time-consuming task. We propose a two-phase approach, in which the first (preprocessing) phase generates multiple overlapping surface patches and handles the problem of seamless texture generation locally for each patch. The second phase stitches these patches at render-time to produce a high-quality visualization of the data. As a result of the proposed localization of the global texturing problem, our algorithm is more than an order of magnitude faster than equivalent mesh-based texturing techniques. Furthermore, since our preprocessing phase requires only a minor fraction of the whole data set at once, we provide maximum flexibility when dealing with growing data sets.

  15. Feature-based three-dimensional registration for repetitive geometry in machine vision

    PubMed Central

    Gong, Yuanzheng; Seibel, Eric J.

    2016-01-01

    As an important step in three-dimensional (3D) machine vision, 3D registration is a process of aligning two or multiple 3D point clouds that are collected from different perspectives together into a complete one. The most popular approach to register point clouds is to minimize the difference between these point clouds iteratively by Iterative Closest Point (ICP) algorithm. However, ICP does not work well for repetitive geometries. To solve this problem, a feature-based 3D registration algorithm is proposed to align the point clouds that are generated by vision-based 3D reconstruction. By utilizing texture information of the object and the robustness of image features, 3D correspondences can be retrieved so that the 3D registration of two point clouds is to solve a rigid transformation. The comparison of our method and different ICP algorithms demonstrates that our proposed algorithm is more accurate, efficient and robust for repetitive geometry registration. Moreover, this method can also be used to solve high depth uncertainty problem caused by little camera baseline in vision-based 3D reconstruction. PMID:28286703

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  17. Multibeam 3D Underwater SLAM with Probabilistic Registration.

    PubMed

    Palomer, Albert; Ridao, Pere; Ribas, David

    2016-04-20

    This paper describes a pose-based underwater 3D Simultaneous Localization and Mapping (SLAM) using a multibeam echosounder to produce high consistency underwater maps. The proposed algorithm compounds swath profiles of the seafloor with dead reckoning localization to build surface patches (i.e., point clouds). An Iterative Closest Point (ICP) with a probabilistic implementation is then used to register the point clouds, taking into account their uncertainties. The registration process is divided in two steps: (1) point-to-point association for coarse registration and (2) point-to-plane association for fine registration. The point clouds of the surfaces to be registered are sub-sampled in order to decrease both the computation time and also the potential of falling into local minima during the registration. In addition, a heuristic is used to decrease the complexity of the association step of the ICP from O(n2) to O(n) . The performance of the SLAM framework is tested using two real world datasets: First, a 2.5D bathymetric dataset obtained with the usual down-looking multibeam sonar configuration, and second, a full 3D underwater dataset acquired with a multibeam sonar mounted on a pan and tilt unit.

  18. Registration of Laser Scanning Point Clouds and Aerial Images Using either Artificial or Natural Tie Features

    NASA Astrophysics Data System (ADS)

    Rönnholm, P.; Haggrén, H.

    2012-07-01

    Integration of laser scanning data and photographs is an excellent combination regarding both redundancy and complementary. Applications of integration vary from sensor and data calibration to advanced classification and scene understanding. In this research, only airborne laser scanning and aerial images are considered. Currently, the initial registration is solved using direct orientation sensors GPS and inertial measurements. However, the accuracy is not usually sufficient for reliable integration of data sets, and thus the initial registration needs to be improved. A registration of data from different sources requires searching and measuring of accurate tie features. Usually, points, lines or planes are preferred as tie features. Therefore, the majority of resent methods rely highly on artificial objects, such as buildings, targets or road paintings. However, in many areas no such objects are available. For example in forestry areas, it would be advantageous to be able to improve registration between laser data and images without making additional ground measurements. Therefore, there is a need to solve registration using only natural features, such as vegetation and ground surfaces. Using vegetation as tie features is challenging, because the shape and even location of vegetation can change because of wind, for example. The aim of this article was to compare registration accuracies derived by using either artificial or natural tie features. The test area included urban objects as well as trees and other vegetation. In this area, two registrations were performed, firstly, using mainly built objects and, secondly, using only vegetation and ground surface. The registrations were solved applying the interactive orientation method. As a result, using artificial tie features leaded to a successful registration in all directions of the coordinate system axes. In the case of using natural tie features, however, the detection of correct heights was difficult causing

  19. 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.

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

    PubMed Central

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

    2009-01-01

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

  1. Continuously Deformation Monitoring of Subway Tunnel Based on Terrestrial Point Clouds

    NASA Astrophysics Data System (ADS)

    Kang, Z.; Tuo, L.; Zlatanova, S.

    2012-07-01

    The deformation monitoring of subway tunnel is of extraordinary necessity. Therefore, a method for deformation monitoring based on terrestrial point clouds is proposed in this paper. First, the traditional adjacent stations registration is replaced by sectioncontrolled registration, so that the common control points can be used by each station and thus the error accumulation avoided within a section. Afterwards, the central axis of the subway tunnel is determined through RANSAC (Random Sample Consensus) algorithm and curve fitting. Although with very high resolution, laser points are still discrete and thus the vertical section is computed via the quadric fitting of the vicinity of interest, instead of the fitting of the whole model of a subway tunnel, which is determined by the intersection line rotated about the central axis of tunnel within a vertical plane. The extraction of the vertical section is then optimized using RANSAC for the purpose of filtering out noises. Based on the extracted vertical sections, the volume of tunnel deformation is estimated by the comparison between vertical sections extracted at the same position from different epochs of point clouds. Furthermore, the continuously extracted vertical sections are deployed to evaluate the convergent tendency of the tunnel. The proposed algorithms are verified using real datasets in terms of accuracy and computation efficiency. The experimental result of fitting accuracy analysis shows the maximum deviation between interpolated point and real point is 1.5 mm, and the minimum one is 0.1 mm; the convergent tendency of the tunnel was detected by the comparison of adjacent fitting radius. The maximum error is 6 mm, while the minimum one is 1 mm. The computation cost of vertical section abstraction is within 3 seconds/section, which proves high efficiency..

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

    NASA Astrophysics Data System (ADS)

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

    2018-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Cura, Rémi; Perret, Julien; Paparoditis, Nicolas

    2017-05-01

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

  5. Registration of opthalmic images using control points

    NASA Astrophysics Data System (ADS)

    Heneghan, Conor; Maguire, Paul

    2003-03-01

    A method for registering pairs of digital ophthalmic images of the retina is presented using anatomical features as control points present in both images. The anatomical features chosen are blood vessel crossings and bifurcations. These control points are identified by a combination of local contrast enhancement, and morphological processing. In general, the matching between control points is unknown, however, so an automated algorithm is used to determine the matching pairs of control points in the two images as follows. Using two control points from each image, rigid global transform (RGT) coefficients are calculated for all possible combinations of control point pairs, and the set of RGT coefficients is identified. Once control point pairs are established, registration of two images can be achieved by using linear regression to optimize an RGT, bilinear or second order polynomial global transform. An example of cross-modal image registration using an optical image and a fluorescein angiogram of an eye is presented to illustrate the technique.

  6. 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

  7. Temporally consistent segmentation of point clouds

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

  8. Model for Semantically Rich Point Cloud Data

    NASA Astrophysics Data System (ADS)

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

    2017-10-01

    This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.

  9. Three-Dimensional Registration for Handheld Profiling Systems Based on Multiple Shot Structured Light

    PubMed Central

    Ayaz, Shirazi Muhammad; Kim, Min Young

    2018-01-01

    In this article, a multi-view registration approach for the 3D handheld profiling system based on the multiple shot structured light technique is proposed. The multi-view registration approach is categorized into coarse registration and point cloud refinement using the iterative closest point (ICP) algorithm. Coarse registration of multiple point clouds was performed using relative orientation and translation parameters estimated via homography-based visual navigation. The proposed system was evaluated using an artificial human skull and a paper box object. For the quantitative evaluation of the accuracy of a single 3D scan, a paper box was reconstructed, and the mean errors in its height and breadth were found to be 9.4 μm and 23 μm, respectively. A comprehensive quantitative evaluation and comparison of proposed algorithm was performed with other variants of ICP. The root mean square error for the ICP algorithm to register a pair of point clouds of the skull object was also found to be less than 1 mm. PMID:29642552

  10. 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.

  11. Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications

    PubMed Central

    Moussa, Adel; El-Sheimy, Naser; Habib, Ayman

    2017-01-01

    Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research. PMID:29057847

  12. Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications.

    PubMed

    Al-Rawabdeh, Abdulla; Moussa, Adel; Foroutan, Marzieh; El-Sheimy, Naser; Habib, Ayman

    2017-10-18

    Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research.

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

    NASA Astrophysics Data System (ADS)

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

    2018-03-01

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

  14. Self-Similar Spin Images for Point Cloud Matching

    NASA Astrophysics Data System (ADS)

    Pulido, Daniel

    The rapid growth of Light Detection And Ranging (Lidar) technologies that collect, process, and disseminate 3D point clouds have allowed for increasingly accurate spatial modeling and analysis of the real world. Lidar sensors can generate massive 3D point clouds of a collection area that provide highly detailed spatial and radiometric information. However, a Lidar collection can be expensive and time consuming. Simultaneously, the growth of crowdsourced Web 2.0 data (e.g., Flickr, OpenStreetMap) have provided researchers with a wealth of freely available data sources that cover a variety of geographic areas. Crowdsourced data can be of varying quality and density. In addition, since it is typically not collected as part of a dedicated experiment but rather volunteered, when and where the data is collected is arbitrary. The integration of these two sources of geoinformation can provide researchers the ability to generate products and derive intelligence that mitigate their respective disadvantages and combine their advantages. Therefore, this research will address the problem of fusing two point clouds from potentially different sources. Specifically, we will consider two problems: scale matching and feature matching. Scale matching consists of computing feature metrics of each point cloud and analyzing their distributions to determine scale differences. Feature matching consists of defining local descriptors that are invariant to common dataset distortions (e.g., rotation and translation). Additionally, after matching the point clouds they can be registered and processed further (e.g., change detection). The objective of this research is to develop novel methods to fuse and enhance two point clouds from potentially disparate sources (e.g., Lidar and crowdsourced Web 2.0 datasets). The scope of this research is to investigate both scale and feature matching between two point clouds. The specific focus of this research will be in developing a novel local descriptor

  15. Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

    PubMed Central

    Zhu, Ningning; Jia, Yonghong; Ji, Shunping

    2018-01-01

    We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual interventions in control point-based registration methods. First, the rigorous registration model between the LiDAR points and the panoramic/fish-eye image was built. Second, skyline pixels from panoramic/fish-eye images and skyline points from the MMS’s LiDAR points were extracted, relying on the difference in the pixel values and the registration model, respectively. Third, a brute force optimization method was used to search for optimal matching parameters between skyline pixels and skyline points. In the experiments, the original registration method and the control point registration method were used to compare the accuracy of our method with a sequence of panoramic/fish-eye images. The result showed: (1) the panoramic/fish-eye image registration model is effective and can achieve high-precision registration of the image and the MMS’s LiDAR points; (2) the skyline-based registration method can automatically optimize the initial attitude parameters, realizing a high-precision registration of a panoramic/fish-eye image and the MMS’s LiDAR points; and (3) the attitude correction values of the sequences of panoramic/fish-eye images are different, and the values must be solved one by one. PMID:29883431

  16. Motion-Compensated Compression of Dynamic Voxelized Point Clouds.

    PubMed

    De Queiroz, Ricardo L; Chou, Philip A

    2017-05-24

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

  17. Classification by Using Multispectral Point Cloud Data

    NASA Astrophysics Data System (ADS)

    Liao, C. T.; Huang, H. H.

    2012-07-01

    Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

  18. 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

  19. 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

  20. Cortical Surface Registration for Image-Guided Neurosurgery Using Laser-Range Scanning

    PubMed Central

    Sinha, Tuhin K.; Cash, David M.; Galloway, Robert L.; Weil, Robert J.

    2013-01-01

    In this paper, a method of acquiring intraoperative data using a laser range scanner (LRS) is presented within the context of model-updated image-guided surgery. Registering textured point clouds generated by the LRS to tomographic data is explored using established point-based and surface techniques as well as a novel method that incorporates geometry and intensity information via mutual information (SurfaceMI). Phantom registration studies were performed to examine accuracy and robustness for each framework. In addition, an in vivo registration is performed to demonstrate feasibility of the data acquisition system in the operating room. Results indicate that SurfaceMI performed better in many cases than point-based (PBR) and iterative closest point (ICP) methods for registration of textured point clouds. Mean target registration error (TRE) for simulated deep tissue targets in a phantom were 1.0 ± 0.2, 2.0 ± 0.3, and 1.2 ± 0.3 mm for PBR, ICP, and SurfaceMI, respectively. With regard to in vivo registration, the mean TRE of vessel contour points for each framework was 1.9 ± 1.0, 0 9 ± 0.6, and 1.3 ± 0.5 for PBR, ICP, and SurfaceMI, respectively. The methods discussed in this paper in conjunction with the quantitative data provide impetus for using LRS technology within the model-updated image-guided surgery framework. PMID:12906252

  1. 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.

  2. Inverse consistent non-rigid image registration based on robust point set matching

    PubMed Central

    2014-01-01

    Background Robust point matching (RPM) has been extensively used in non-rigid registration of images to robustly register two sets of image points. However, except for the location at control points, RPM cannot estimate the consistent correspondence between two images because RPM is a unidirectional image matching approach. Therefore, it is an important issue to make an improvement in image registration based on RPM. Methods In our work, a consistent image registration approach based on the point sets matching is proposed to incorporate the property of inverse consistency and improve registration accuracy. Instead of only estimating the forward transformation between the source point sets and the target point sets in state-of-the-art RPM algorithms, the forward and backward transformations between two point sets are estimated concurrently in our algorithm. The inverse consistency constraints are introduced to the cost function of RPM and the fuzzy correspondences between two point sets are estimated based on both the forward and backward transformations simultaneously. A modified consistent landmark thin-plate spline registration is discussed in detail to find the forward and backward transformations during the optimization of RPM. The similarity of image content is also incorporated into point matching in order to improve image matching. Results Synthetic data sets, medical images are employed to demonstrate and validate the performance of our approach. The inverse consistent errors of our algorithm are smaller than RPM. Especially, the topology of transformations is preserved well for our algorithm for the large deformation between point sets. Moreover, the distance errors of our algorithm are similar to that of RPM, and they maintain a downward trend as whole, which demonstrates the convergence of our algorithm. The registration errors for image registrations are evaluated also. Again, our algorithm achieves the lower registration errors in same iteration number

  3. Point clouds in BIM

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  4. Augmented reality system using lidar point cloud data for displaying dimensional information of objects on mobile phones

    NASA Astrophysics Data System (ADS)

    Gupta, S.; Lohani, B.

    2014-05-01

    Mobile augmented reality system is the next generation technology to visualise 3D real world intelligently. The technology is expanding at a fast pace to upgrade the status of a smart phone to an intelligent device. The research problem identified and presented in the current work is to view actual dimensions of various objects that are captured by a smart phone in real time. The methodology proposed first establishes correspondence between LiDAR point cloud, that are stored in a server, and the image t hat is captured by a mobile. This correspondence is established using the exterior and interior orientation parameters of the mobile camera and the coordinates of LiDAR data points which lie in the viewshed of the mobile camera. A pseudo intensity image is generated using LiDAR points and their intensity. Mobile image and pseudo intensity image are then registered using image registration method SIFT thereby generating a pipeline to locate a point in point cloud corresponding to a point (pixel) on the mobile image. The second part of the method uses point cloud data for computing dimensional information corresponding to the pairs of points selected on mobile image and fetch the dimensions on top of the image. This paper describes all steps of the proposed method. The paper uses an experimental setup to mimic the mobile phone and server system and presents some initial but encouraging results

  5. 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.

  6. The Registration and Segmentation of Heterogeneous Laser Scanning Data

    NASA Astrophysics Data System (ADS)

    Al-Durgham, Mohannad M.

    Light Detection And Ranging (LiDAR) mapping has been emerging over the past few years as a mainstream tool for the dense acquisition of three dimensional point data. Besides the conventional mapping missions, LiDAR systems have proven to be very useful for a wide spectrum of applications such as forestry, structural deformation analysis, urban mapping, and reverse engineering. The wide application scope of LiDAR lead to the development of many laser scanning technologies that are mountable on multiple platforms (i.e., airborne, mobile terrestrial, and tripod mounted), this caused variations in the characteristics and quality of the generated point clouds. As a result of the increased popularity and diversity of laser scanners, one should address the heterogeneous LiDAR data post processing (i.e., registration and segmentation) problems adequately. Current LiDAR integration techniques do not take into account the varying nature of laser scans originating from various platforms. In this dissertation, the author proposes a methodology designed particularly for the registration and segmentation of heterogeneous LiDAR data. A data characterization and filtering step is proposed to populate the points' attributes and remove non-planar LiDAR points. Then, a modified version of the Iterative Closest Point (ICP), denoted by the Iterative Closest Projected Point (ICPP) is designed for the registration of heterogeneous scans to remove any misalignments between overlapping strips. Next, a region-growing-based heterogeneous segmentation algorithm is developed to ensure the proper extraction of planar segments from the point clouds. Validation experiments show that the proposed heterogeneous registration can successfully align airborne and terrestrial datasets despite the great differences in their point density and their noise level. In addition, similar testes have been conducted to examine the heterogeneous segmentation and it is shown that one is able to identify common

  7. Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features

    NASA Astrophysics Data System (ADS)

    Li, Jianping; Yang, Bisheng; Chen, Chi; Huang, Ronggang; Dong, Zhen; Xiao, Wen

    2018-02-01

    Inaccurate exterior orientation parameters (EoPs) between sensors obtained by pre-calibration leads to failure of registration between panoramic image sequence and mobile laser scanning data. To address this challenge, this paper proposes an automatic registration method based on semantic features extracted from panoramic images and point clouds. Firstly, accurate rotation parameters between the panoramic camera and the laser scanner are estimated using GPS and IMU aided structure from motion (SfM). The initial EoPs of panoramic images are obtained at the same time. Secondly, vehicles in panoramic images are extracted by the Faster-RCNN as candidate primitives to be matched with potential corresponding primitives in point clouds according to the initial EoPs. Finally, translation between the panoramic camera and the laser scanner is refined by maximizing the overlapping area of corresponding primitive pairs based on the Particle Swarm Optimization (PSO), resulting in a finer registration between panoramic image sequences and point clouds. Two challenging urban scenes were experimented to assess the proposed method, and the final registration errors of these two scenes were both less than three pixels, which demonstrates a high level of automation, robustness and accuracy.

  8. Pointo - a Low Cost Solution to Point Cloud Processing

    NASA Astrophysics Data System (ADS)

    Houshiar, H.; Winkler, S.

    2017-11-01

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

  9. 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.

  10. Investigating the Accuracy of Point Clouds Generated for Rock Surfaces

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  11. 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.

  12. Characterizing Sorghum Panicles using 3D Point Clouds

    NASA Astrophysics Data System (ADS)

    Lonesome, M.; Popescu, S. C.; Horne, D. W.; Pugh, N. A.; Rooney, W.

    2017-12-01

    To address demands of population growth and impacts of global climate change, plant breeders must increase crop yield through genetic improvement. However, plant phenotyping, the characterization of a plant's physical attributes, remains a primary bottleneck in modern crop improvement programs. 3D point clouds generated from terrestrial laser scanning (TLS) and unmanned aerial systems (UAS) based structure from motion (SfM) are a promising data source to increase the efficiency of screening plant material in breeding programs. This study develops and evaluates methods for characterizing sorghum (Sorghum bicolor) panicles (heads) in field plots from both TLS and UAS-based SfM point clouds. The TLS point cloud over experimental sorghum field at Texas A&M farm in Burleston County TX were collected using a FARO Focus X330 3D laser scanner. SfM point cloud was generated from UAS imagery captured using a Phantom 3 Professional UAS at 10m altitude and 85% image overlap. The panicle detection method applies point cloud reflectance, height and point density attributes characteristic of sorghum panicles to detect them and estimate their dimensions (panicle length and width) through image classification and clustering procedures. We compare the derived panicle counts and panicle sizes with field-based and manually digitized measurements in selected plots and study the strengths and limitations of each data source for sorghum panicle characterization.

  13. 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

  14. Large-scale urban point cloud labeling and reconstruction

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  15. Multiview point clouds denoising based on interference elimination

    NASA Astrophysics Data System (ADS)

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

    2018-03-01

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

  16. Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

    NASA Astrophysics Data System (ADS)

    Budge, Scott E.; Badamikar, Neeraj S.; Xie, Xuan

    2015-03-01

    Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3-D points are fused at the sensor level, more accurate 3-D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

  17. 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.

  18. Ifcwall Reconstruction from Unstructured Point Clouds

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Kohira, K.; Masuda, H.

    2017-09-01

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

  20. Robust iterative closest point algorithm based on global reference point for rotation invariant registration.

    PubMed

    Du, Shaoyi; Xu, Yiting; Wan, Teng; Hu, Huaizhong; Zhang, Sirui; Xu, Guanglin; Zhang, Xuetao

    2017-01-01

    The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm.

  1. Robust iterative closest point algorithm based on global reference point for rotation invariant registration

    PubMed Central

    Du, Shaoyi; Xu, Yiting; Wan, Teng; Zhang, Sirui; Xu, Guanglin; Zhang, Xuetao

    2017-01-01

    The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm. PMID:29176780

  2. Study of Huizhou architecture component point cloud in surface reconstruction

    NASA Astrophysics Data System (ADS)

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

    2017-06-01

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

  3. 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

    consistency between point clouds and stereo images. Finally, an over-segmentation based graph cut optimization is carried out, taking into account the color, depth and class information to compute the changed area in the image space. The proposed method is invariant to light changes, robust to small co-registration errors between images and point clouds, and can be applied straightforwardly to 3D polyhedral models. This method can be used for 3D street data updating, city infrastructure management and damage monitoring in complex urban scenes.

  4. Semantic Segmentation of Building Elements Using Point Cloud Hashing

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

  5. 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.

  6. 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.

  7. Applicability Analysis of Cloth Simulation Filtering Algorithm for Mobile LIDAR Point Cloud

    NASA Astrophysics Data System (ADS)

    Cai, S.; Zhang, W.; Qi, J.; Wan, P.; Shao, J.; Shen, A.

    2018-04-01

    Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS) has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM), 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature) for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44 %, 0.77 % and1.20 %. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud.

  8. Airborne LIDAR point cloud tower inclination judgment

    NASA Astrophysics Data System (ADS)

    liang, Chen; zhengjun, Liu; jianguo, Qian

    2016-11-01

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

  9. 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.

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

    NASA Astrophysics Data System (ADS)

    Ye, Mengxuan; Wei, Shuangfeng; Zhang, Dongmei

    2016-11-01

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

  11. Direct endoscopic video registration for sinus surgery

    NASA Astrophysics Data System (ADS)

    Mirota, Daniel; Taylor, Russell H.; Ishii, Masaru; Hager, Gregory D.

    2009-02-01

    Advances in computer vision have made possible robust 3D reconstruction of monocular endoscopic video. These reconstructions accurately represent the visible anatomy and, once registered to pre-operative CT data, enable a navigation system to track directly through video eliminating the need for an external tracking system. Video registration provides the means for a direct interface between an endoscope and a navigation system and allows a shorter chain of rigid-body transformations to be used to solve the patient/navigation-system registration. To solve this registration step we propose a new 3D-3D registration algorithm based on Trimmed Iterative Closest Point (TrICP)1 and the z-buffer algorithm.2 The algorithm takes as input a 3D point cloud of relative scale with the origin at the camera center, an isosurface from the CT, and an initial guess of the scale and location. Our algorithm utilizes only the visible polygons of the isosurface from the current camera location during each iteration to minimize the search area of the target region and robustly reject outliers of the reconstruction. We present example registrations in the sinus passage applicable to both sinus surgery and transnasal surgery. To evaluate our algorithm's performance we compare it to registration via Optotrak and present closest distance point to surface error. We show our algorithm has a mean closest distance error of .2268mm.

  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. Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain

    NASA Astrophysics Data System (ADS)

    Marreiros, Filipe M. M.; Wang, Chunliang; Rossitti, Sandro; Smedby, Örjan

    2016-03-01

    In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). The method was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to match vessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-Plate Spline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, while the semilandmark method iteratively relaxes/slides the points. For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were applied to the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where a few control points were manipulated to obtain the desired transformation (T1). Once the correspondences are known, the corresponding points are used to define a new TPS deformation(T2). The errors are measured in the deformed space, by transforming the original points using T1 and T2 and measuring the distance between them. To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and then deformed. Furthermore, anisotropic normally distributed noise was added. The results show that the error estimates (root mean square error and mean error) are below 1 mm, even in the presence of noise and incomplete data.

  14. Filtering Photogrammetric Point Clouds Using Standard LIDAR Filters Towards DTM Generation

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

  15. Classification of Aerial Photogrammetric 3d Point Clouds

    NASA Astrophysics Data System (ADS)

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

    2017-05-01

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

  16. 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.

  17. 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.

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

    NASA Astrophysics Data System (ADS)

    Kuçak, R. A.; Özdemir, E.; Erol, S.

    2017-05-01

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

  19. Knowledge-Based Object Detection in Laser Scanning Point Clouds

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

  20. Analysis of Point Based Image Registration Errors With Applications in Single Molecule Microscopy

    PubMed Central

    Cohen, E. A. K.; Ober, R. J.

    2014-01-01

    We present an asymptotic treatment of errors involved in point-based image registration where control point (CP) localization is subject to heteroscedastic noise; a suitable model for image registration in fluorescence microscopy. Assuming an affine transform, CPs are used to solve a multivariate regression problem. With measurement errors existing for both sets of CPs this is an errors-in-variable problem and linear least squares is inappropriate; the correct method being generalized least squares. To allow for point dependent errors the equivalence of a generalized maximum likelihood and heteroscedastic generalized least squares model is achieved allowing previously published asymptotic results to be extended to image registration. For a particularly useful model of heteroscedastic noise where covariance matrices are scalar multiples of a known matrix (including the case where covariance matrices are multiples of the identity) we provide closed form solutions to estimators and derive their distribution. We consider the target registration error (TRE) and define a new measure called the localization registration error (LRE) believed to be useful, especially in microscopy registration experiments. Assuming Gaussianity of the CP localization errors, it is shown that the asymptotic distribution for the TRE and LRE are themselves Gaussian and the parameterized distributions are derived. Results are successfully applied to registration in single molecule microscopy to derive the key dependence of the TRE and LRE variance on the number of CPs and their associated photon counts. Simulations show asymptotic results are robust for low CP numbers and non-Gaussianity. The method presented here is shown to outperform GLS on real imaging data. PMID:24634573

  1. Joint classification and contour extraction of large 3D point clouds

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

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

  2. Validation of non-rigid point-set registration methods using a porcine bladder pelvic phantom

    NASA Astrophysics Data System (ADS)

    Zakariaee, Roja; Hamarneh, Ghassan; Brown, Colin J.; Spadinger, Ingrid

    2016-01-01

    The problem of accurate dose accumulation in fractionated radiotherapy treatment for highly deformable organs, such as bladder, has garnered increasing interest over the past few years. However, more research is required in order to find a robust and efficient solution and to increase the accuracy over the current methods. The purpose of this study was to evaluate the feasibility and accuracy of utilizing non-rigid (affine or deformable) point-set registration in accumulating dose in bladder of different sizes and shapes. A pelvic phantom was built to house an ex vivo porcine bladder with fiducial landmarks adhered onto its surface. Four different volume fillings of the bladder were used (90, 180, 360 and 480 cc). The performance of MATLAB implementations of five different methods were compared, in aligning the bladder contour point-sets. The approaches evaluated were coherent point drift (CPD), gaussian mixture model, shape context, thin-plate spline robust point matching (TPS-RPM) and finite iterative closest point (ICP-finite). The evaluation metrics included registration runtime, target registration error (TRE), root-mean-square error (RMS) and Hausdorff distance (HD). The reference (source) dataset was alternated through all four points-sets, in order to study the effect of reference volume on the registration outcomes. While all deformable algorithms provided reasonable registration results, CPD provided the best TRE values (6.4 mm), and TPS-RPM yielded the best mean RMS and HD values (1.4 and 6.8 mm, respectively). ICP-finite was the fastest technique and TPS-RPM, the slowest.

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

    PubMed

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

    2015-11-01

    To accurately and efficiently reconstruct a continuous surface from noisy point clouds captured by a surface photogrammetry system (VisionRT). The authors have developed a level-set based surface reconstruction method on point clouds captured by a surface photogrammetry system (VisionRT). The proposed method reconstructs an implicit and continuous representation of the underlying patient surface by optimizing a regularized fitting energy, offering extra robustness to noise and missing measurements. By contrast to explicit/discrete meshing-type schemes, their continuous representation is particularly advantageous for subsequent surface registration and motion tracking by eliminating the need for maintaining explicit point correspondences as in discrete models. The authors solve the proposed method with an efficient narrowband evolving scheme. The authors evaluated the proposed method on both phantom and human subject data with two sets of complementary experiments. In the first set of experiment, the authors generated a series of surfaces each with different black patches placed on one chest phantom. The resulting VisionRT measurements from the patched area had different degree of noise and missing levels, since VisionRT has difficulties in detecting dark surfaces. The authors applied the proposed method to point clouds acquired under these different configurations, and quantitatively evaluated reconstructed surfaces by comparing against a high-quality reference surface with respect to root mean squared error (RMSE). In the second set of experiment, the authors applied their method to 100 clinical point clouds acquired from one human subject. In the absence of ground-truth, the authors qualitatively validated reconstructed surfaces by comparing the local geometry, specifically mean curvature distributions, against that of the surface extracted from a high-quality CT obtained from the same patient. On phantom point clouds, their method achieved submillimeter

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

    PubMed Central

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

    2015-01-01

    Purpose: To accurately and efficiently reconstruct a continuous surface from noisy point clouds captured by a surface photogrammetry system (VisionRT). Methods: The authors have developed a level-set based surface reconstruction method on point clouds captured by a surface photogrammetry system (VisionRT). The proposed method reconstructs an implicit and continuous representation of the underlying patient surface by optimizing a regularized fitting energy, offering extra robustness to noise and missing measurements. By contrast to explicit/discrete meshing-type schemes, their continuous representation is particularly advantageous for subsequent surface registration and motion tracking by eliminating the need for maintaining explicit point correspondences as in discrete models. The authors solve the proposed method with an efficient narrowband evolving scheme. The authors evaluated the proposed method on both phantom and human subject data with two sets of complementary experiments. In the first set of experiment, the authors generated a series of surfaces each with different black patches placed on one chest phantom. The resulting VisionRT measurements from the patched area had different degree of noise and missing levels, since VisionRT has difficulties in detecting dark surfaces. The authors applied the proposed method to point clouds acquired under these different configurations, and quantitatively evaluated reconstructed surfaces by comparing against a high-quality reference surface with respect to root mean squared error (RMSE). In the second set of experiment, the authors applied their method to 100 clinical point clouds acquired from one human subject. In the absence of ground-truth, the authors qualitatively validated reconstructed surfaces by comparing the local geometry, specifically mean curvature distributions, against that of the surface extracted from a high-quality CT obtained from the same patient. Results: On phantom point clouds, their method

  5. LIDAR Point Cloud Data Extraction and Establishment of 3D Modeling of Buildings

    NASA Astrophysics Data System (ADS)

    Zhang, Yujuan; Li, Xiuhai; Wang, Qiang; Liu, Jiang; Liang, Xin; Li, Dan; Ni, Chundi; Liu, Yan

    2018-01-01

    This paper takes the method of Shepard’s to deal with the original LIDAR point clouds data, and generate regular grid data DSM, filters the ground point cloud and non ground point cloud through double least square method, and obtains the rules of DSM. By using region growing method for the segmentation of DSM rules, the removal of non building point cloud, obtaining the building point cloud information. Uses the Canny operator to extract the image segmentation is needed after the edges of the building, uses Hough transform line detection to extract the edges of buildings rules of operation based on the smooth and uniform. At last, uses E3De3 software to establish the 3D model of buildings.

  6. 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.

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

    PubMed

    De Queiroz, Ricardo; Chou, Philip A

    2016-06-01

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

  8. 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

  9. Min-Cut Based Segmentation of Airborne LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Ural, S.; Shan, J.

    2012-07-01

    Introducing an organization to the unstructured point cloud before extracting information from airborne lidar data is common in many applications. Aggregating the points with similar features into segments in 3-D which comply with the nature of actual objects is affected by the neighborhood, scale, features and noise among other aspects. In this study, we present a min-cut based method for segmenting the point cloud. We first assess the neighborhood of each point in 3-D by investigating the local geometric and statistical properties of the candidates. Neighborhood selection is essential since point features are calculated within their local neighborhood. Following neighborhood determination, we calculate point features and determine the clusters in the feature space. We adapt a graph representation from image processing which is especially used in pixel labeling problems and establish it for the unstructured 3-D point clouds. The edges of the graph that are connecting the points with each other and nodes representing feature clusters hold the smoothness costs in the spatial domain and data costs in the feature domain. Smoothness costs ensure spatial coherence, while data costs control the consistency with the representative feature clusters. This graph representation formalizes the segmentation task as an energy minimization problem. It allows the implementation of an approximate solution by min-cuts for a global minimum of this NP hard minimization problem in low order polynomial time. We test our method with airborne lidar point cloud acquired with maximum planned post spacing of 1.4 m and a vertical accuracy 10.5 cm as RMSE. We present the effects of neighborhood and feature determination in the segmentation results and assess the accuracy and efficiency of the implemented min-cut algorithm as well as its sensitivity to the parameters of the smoothness and data cost functions. We find that smoothness cost that only considers simple distance parameter does not

  10. 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.

  11. 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

  12. 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.

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

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

    Liu, Wenyang; Cheung, Yam; Sabouri, Pouya

    2015-11-15

    Purpose: To accurately and efficiently reconstruct a continuous surface from noisy point clouds captured by a surface photogrammetry system (VisionRT). Methods: The authors have developed a level-set based surface reconstruction method on point clouds captured by a surface photogrammetry system (VisionRT). The proposed method reconstructs an implicit and continuous representation of the underlying patient surface by optimizing a regularized fitting energy, offering extra robustness to noise and missing measurements. By contrast to explicit/discrete meshing-type schemes, their continuous representation is particularly advantageous for subsequent surface registration and motion tracking by eliminating the need for maintaining explicit point correspondences as in discretemore » models. The authors solve the proposed method with an efficient narrowband evolving scheme. The authors evaluated the proposed method on both phantom and human subject data with two sets of complementary experiments. In the first set of experiment, the authors generated a series of surfaces each with different black patches placed on one chest phantom. The resulting VisionRT measurements from the patched area had different degree of noise and missing levels, since VisionRT has difficulties in detecting dark surfaces. The authors applied the proposed method to point clouds acquired under these different configurations, and quantitatively evaluated reconstructed surfaces by comparing against a high-quality reference surface with respect to root mean squared error (RMSE). In the second set of experiment, the authors applied their method to 100 clinical point clouds acquired from one human subject. In the absence of ground-truth, the authors qualitatively validated reconstructed surfaces by comparing the local geometry, specifically mean curvature distributions, against that of the surface extracted from a high-quality CT obtained from the same patient. Results: On phantom point clouds, their

  14. 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.

  15. Cloud-point detection using a portable thickness shear mode crystal resonator

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

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

    1997-08-01

    The Thickness Shear Mode (TSM) crystal resonator monitors the crude oil by propagating a shear wave into the oil. The coupling of the shear wave and the crystal vibrations is a function of the viscosity of the oil. By driving the crystal with circuitry that incorporates feedback, it is possible to determine the change from Newtonian to non-Newtonian viscosity at the cloud point. A portable prototype TSM Cloud Point Detector (CPD) has performed flawlessly during field and lab tests proving the technique is less subjective or operator dependent than the ASTM standard. The TSM CPD, in contrast to standard viscositymore » techniques, makes the measurement in a closed container capable of maintaining up to 100 psi. The closed container minimizes losses of low molecular weight volatiles, allowing samples (25 ml) to be retested with the addition of chemicals. By cycling/thermal soaking the sample, the effects of thermal history can be investigated and eliminated as a source of confusion. The CPD is portable, suitable for shipping the field offices for use by personnel without special training or experience in cloud point measurements. As such, it can make cloud point data available without the delays and inconvenience of sending samples to special labs. The crystal resonator technology can be adapted to in-line monitoring of cloud point and deposition detection.« less

  16. Classification of Mobile Laser Scanning Point Clouds from Height Features

    NASA Astrophysics Data System (ADS)

    Zheng, M.; Lemmens, M.; van Oosterom, P.

    2017-09-01

    The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features - two height components and one reflectance value, and achieved an overall accuracy of 73 %, which is really encouraging for further refining our approach.

  17. Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations.

    PubMed

    Huang, Rongyong; Zheng, Shunyi; Hu, Kun

    2018-06-01

    Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for registering close-range optical images with terrestrial LiDAR data to a variety of large-scale aerial optical images and airborne LiDAR data. The fundamental principle is to minimize the distances from the photogrammetric matching points to the terrestrial LiDAR data surface. Except for the satisfactory efficiency of about 79 s per 6732 × 8984 image, the experimental results also show that the unit weighted root mean square (RMS) of the image points is able to reach a sub-pixel level (0.45 to 0.62 pixel), and the actual horizontal and vertical accuracy can be greatly improved to a high level of 1/4⁻1/2 (0.17⁻0.27 m) and 1/8⁻1/4 (0.10⁻0.15 m) of the average LiDAR point distance respectively. Finally, the method is proved to be more accurate, feasible, efficient, and practical in variety of large-scale aerial optical image and LiDAR data.

  18. 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.

  19. Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.

    2018-05-01

    Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

  20. a Point Cloud Classification Approach Based on Vertical Structures of Ground Objects

    NASA Astrophysics Data System (ADS)

    Zhao, Y.; Hu, Q.; Hu, W.

    2018-04-01

    This paper proposes a novel method for point cloud classification using vertical structural characteristics of ground objects. Since urbanization develops rapidly nowadays, urban ground objects also change frequently. Conventional photogrammetric methods cannot satisfy the requirements of updating the ground objects' information efficiently, so LiDAR (Light Detection and Ranging) technology is employed to accomplish this task. LiDAR data, namely point cloud data, can obtain detailed three-dimensional coordinates of ground objects, but this kind of data is discrete and unorganized. To accomplish ground objects classification with point cloud, we first construct horizontal grids and vertical layers to organize point cloud data, and then calculate vertical characteristics, including density and measures of dispersion, and form characteristic curves for each grids. With the help of PCA processing and K-means algorithm, we analyze the similarities and differences of characteristic curves. Curves that have similar features will be classified into the same class and point cloud correspond to these curves will be classified as well. The whole process is simple but effective, and this approach does not need assistance of other data sources. In this study, point cloud data are classified into three classes, which are vegetation, buildings, and roads. When horizontal grid spacing and vertical layer spacing are 3 m and 1 m respectively, vertical characteristic is set as density, and the number of dimensions after PCA processing is 11, the overall precision of classification result is about 86.31 %. The result can help us quickly understand the distribution of various ground objects.

  1. 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.

  2. Surface Fitting Filtering of LIDAR Point Cloud with Waveform Information

    NASA Astrophysics Data System (ADS)

    Xing, S.; Li, P.; Xu, Q.; Wang, D.; Li, P.

    2017-09-01

    Full-waveform LiDAR is an active technology of photogrammetry and remote sensing. It provides more detailed information about objects along the path of a laser pulse than discrete-return topographic LiDAR. The point cloud and waveform information with high quality can be obtained by waveform decomposition, which could make contributions to accurate filtering. The surface fitting filtering method with waveform information is proposed to present such advantage. Firstly, discrete point cloud and waveform parameters are resolved by global convergent Levenberg Marquardt decomposition. Secondly, the ground seed points are selected, of which the abnormal ones are detected by waveform parameters and robust estimation. Thirdly, the terrain surface is fitted and the height difference threshold is determined in consideration of window size and mean square error. Finally, the points are classified gradually with the rising of window size. The filtering process is finished until window size is larger than threshold. The waveform data in urban, farmland and mountain areas from "WATER (Watershed Allied Telemetry Experimental Research)" are selected for experiments. Results prove that compared with traditional method, the accuracy of point cloud filtering is further improved and the proposed method has highly practical value.

  3. On the performance of metrics to predict quality in point cloud representations

    NASA Astrophysics Data System (ADS)

    Alexiou, Evangelos; Ebrahimi, Touradj

    2017-09-01

    Point clouds are a promising alternative for immersive representation of visual contents. Recently, an increased interest has been observed in the acquisition, processing and rendering of this modality. Although subjective and objective evaluations are critical in order to assess the visual quality of media content, they still remain open problems for point cloud representation. In this paper we focus our efforts on subjective quality assessment of point cloud geometry, subject to typical types of impairments such as noise corruption and compression-like distortions. In particular, we propose a subjective methodology that is closer to real-life scenarios of point cloud visualization. The performance of the state-of-the-art objective metrics is assessed by considering the subjective scores as the ground truth. Moreover, we investigate the impact of adopting different test methodologies by comparing them. Advantages and drawbacks of every approach are reported, based on statistical analysis. The results and conclusions of this work provide useful insights that could be considered in future experimentation.

  4. A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

    NASA Astrophysics Data System (ADS)

    Yu, Le; Zhang, Dengrong; Holden, Eun-Jung

    2008-07-01

    Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

  5. Hierarchical Regularization of Polygons for Photogrammetric Point Clouds of Oblique Images

    NASA Astrophysics Data System (ADS)

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

    2017-05-01

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

  6. Robust Spacecraft Component Detection in Point Clouds.

    PubMed

    Wei, Quanmao; Jiang, Zhiguo; Zhang, Haopeng

    2018-03-21

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

  7. Robust Spacecraft Component Detection in Point Clouds

    PubMed Central

    Wei, Quanmao; Jiang, Zhiguo

    2018-01-01

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

  8. An approach of point cloud denoising based on improved bilateral filtering

    NASA Astrophysics Data System (ADS)

    Zheng, Zeling; Jia, Songmin; Zhang, Guoliang; Li, Xiuzhi; Zhang, Xiangyin

    2018-04-01

    An omnidirectional mobile platform is designed for building point cloud based on an improved filtering algorithm which is employed to handle the depth image. First, the mobile platform can move flexibly and the control interface is convenient to control. Then, because the traditional bilateral filtering algorithm is time-consuming and inefficient, a novel method is proposed which called local bilateral filtering (LBF). LBF is applied to process depth image obtained by the Kinect sensor. The results show that the effect of removing noise is improved comparing with the bilateral filtering. In the condition of off-line, the color images and processed images are used to build point clouds. Finally, experimental results demonstrate that our method improves the speed of processing time of depth image and the effect of point cloud which has been built.

  9. Neural network-based feature point descriptors for registration of optical and SAR images

    NASA Astrophysics Data System (ADS)

    Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

    2018-04-01

    Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

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

    PubMed

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

    2013-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2018-03-01

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

  12. A Robust False Matching Points Detection Method for Remote Sensing Image Registration

    NASA Astrophysics Data System (ADS)

    Shan, X. J.; Tang, P.

    2015-04-01

    Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

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

    NASA Astrophysics Data System (ADS)

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

    2018-03-01

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

  14. 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

  15. A new markerless patient-to-image registration method using a portable 3D scanner.

    PubMed

    Fan, Yifeng; Jiang, Dongsheng; Wang, Manning; Song, Zhijian

    2014-10-01

    Patient-to-image registration is critical to providing surgeons with reliable guidance information in the application of image-guided neurosurgery systems. The conventional point-matching registration method, which is based on skin markers, requires expensive and time-consuming logistic support. Surface-matching registration with facial surface scans is an alternative method, but the registration accuracy is unstable and the error in the more posterior parts of the head is usually large because the scan range is limited. This study proposes a new surface-matching method using a portable 3D scanner to acquire a point cloud of the entire head to perform the patient-to-image registration. A new method for transforming the scan points from the device space into the patient space without calibration and tracking was developed. Five positioning targets were attached on a reference star, and their coordinates in the patient space were measured prior. During registration, the authors moved the scanner around the head to scan its entire surface as well as the positioning targets, and the scanner generated a unique point cloud in the device space. The coordinates of the positioning targets in the device space were automatically detected by the scanner, and a spatial transformation from the device space to the patient space could be calculated by registering them to their coordinates in the patient space that had been measured prior. A three-step registration algorithm was then used to register the patient space to the image space. The authors evaluated their method on a rigid head phantom and an elastic head phantom to verify its practicality and to calculate the target registration error (TRE) in different regions of the head phantoms. The authors also conducted an experiment with a real patient's data to test the feasibility of their method in the clinical environment. In the phantom experiments, the mean fiducial registration error between the device space and the patient

  16. a Gross Error Elimination Method for Point Cloud Data Based on Kd-Tree

    NASA Astrophysics Data System (ADS)

    Kang, Q.; Huang, G.; Yang, S.

    2018-04-01

    Point cloud data has been one type of widely used data sources in the field of remote sensing. Key steps of point cloud data's pro-processing focus on gross error elimination and quality control. Owing to the volume feature of point could data, existed gross error elimination methods need spend massive memory both in space and time. This paper employed a new method which based on Kd-tree algorithm to construct, k-nearest neighbor algorithm to search, settled appropriate threshold to determine with result turns out a judgement that whether target point is or not an outlier. Experimental results show that, our proposed algorithm will help to delete gross error in point cloud data and facilitate to decrease memory consumption, improve efficiency.

  17. SEMANTIC3D.NET: a New Large-Scale Point Cloud Classification Benchmark

    NASA Astrophysics Data System (ADS)

    Hackel, T.; Savinov, N.; Ladicky, L.; Wegner, J. D.; Schindler, K.; Pollefeys, M.

    2017-05-01

    This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

  18. 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

  19. 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

  20. Three-dimensional measurement of small inner surface profiles using feature-based 3-D panoramic registration

    PubMed Central

    Gong, Yuanzheng; Seibel, Eric J.

    2017-01-01

    Rapid development in the performance of sophisticated optical components, digital image sensors, and computer abilities along with decreasing costs has enabled three-dimensional (3-D) optical measurement to replace more traditional methods in manufacturing and quality control. The advantages of 3-D optical measurement, such as noncontact, high accuracy, rapid operation, and the ability for automation, are extremely valuable for inline manufacturing. However, most of the current optical approaches are eligible for exterior instead of internal surfaces of machined parts. A 3-D optical measurement approach is proposed based on machine vision for the 3-D profile measurement of tiny complex internal surfaces, such as internally threaded holes. To capture the full topographic extent (peak to valley) of threads, a side-view commercial rigid scope is used to collect images at known camera positions and orientations. A 3-D point cloud is generated with multiview stereo vision using linear motion of the test piece, which is repeated by a rotation to form additional point clouds. Registration of these point clouds into a complete reconstruction uses a proposed automated feature-based 3-D registration algorithm. The resulting 3-D reconstruction is compared with x-ray computed tomography to validate the feasibility of our proposed method for future robotically driven industrial 3-D inspection. PMID:28286351

  1. Three-dimensional measurement of small inner surface profiles using feature-based 3-D panoramic registration

    NASA Astrophysics Data System (ADS)

    Gong, Yuanzheng; Seibel, Eric J.

    2017-01-01

    Rapid development in the performance of sophisticated optical components, digital image sensors, and computer abilities along with decreasing costs has enabled three-dimensional (3-D) optical measurement to replace more traditional methods in manufacturing and quality control. The advantages of 3-D optical measurement, such as noncontact, high accuracy, rapid operation, and the ability for automation, are extremely valuable for inline manufacturing. However, most of the current optical approaches are eligible for exterior instead of internal surfaces of machined parts. A 3-D optical measurement approach is proposed based on machine vision for the 3-D profile measurement of tiny complex internal surfaces, such as internally threaded holes. To capture the full topographic extent (peak to valley) of threads, a side-view commercial rigid scope is used to collect images at known camera positions and orientations. A 3-D point cloud is generated with multiview stereo vision using linear motion of the test piece, which is repeated by a rotation to form additional point clouds. Registration of these point clouds into a complete reconstruction uses a proposed automated feature-based 3-D registration algorithm. The resulting 3-D reconstruction is compared with x-ray computed tomography to validate the feasibility of our proposed method for future robotically driven industrial 3-D inspection.

  2. Point cloud modeling using the homogeneous transformation for non-cooperative pose estimation

    NASA Astrophysics Data System (ADS)

    Lim, Tae W.

    2015-06-01

    A modeling process to simulate point cloud range data that a lidar (light detection and ranging) sensor produces is presented in this paper in order to support the development of non-cooperative pose (relative attitude and position) estimation approaches which will help improve proximity operation capabilities between two adjacent vehicles. The algorithms in the modeling process were based on the homogeneous transformation, which has been employed extensively in robotics and computer graphics, as well as in recently developed pose estimation algorithms. Using a flash lidar in a laboratory testing environment, point cloud data of a test article was simulated and compared against the measured point cloud data. The simulated and measured data sets match closely, validating the modeling process. The modeling capability enables close examination of the characteristics of point cloud images of an object as it undergoes various translational and rotational motions. Relevant characteristics that will be crucial in non-cooperative pose estimation were identified such as shift, shadowing, perspective projection, jagged edges, and differential point cloud density. These characteristics will have to be considered in developing effective non-cooperative pose estimation algorithms. The modeling capability will allow extensive non-cooperative pose estimation performance simulations prior to field testing, saving development cost and providing performance metrics of the pose estimation concepts and algorithms under evaluation. The modeling process also provides "truth" pose of the test objects with respect to the sensor frame so that the pose estimation error can be quantified.

  3. 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

  4. Robust non-rigid registration algorithm based on local affine registration

    NASA Astrophysics Data System (ADS)

    Wu, Liyang; Xiong, Lei; Du, Shaoyi; Bi, Duyan; Fang, Ting; Liu, Kun; Wu, Dongpeng

    2018-04-01

    Aiming at the problem that the traditional point set non-rigid registration algorithm has low precision and slow convergence speed for complex local deformation data, this paper proposes a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the point set non-rigid registration from coarse to fine. In each iteration, the sub data point sets and sub model point sets are divided and the shape control points of each sub point set are updated. Then we use the control point guided affine ICP algorithm to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. When the algorithm reaches the maximum iteration layer K, the loop ends and outputs the updated sub data point sets. Experimental results demonstrate that the accuracy and convergence of our algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.

  5. 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

  6. Outdoor Illegal Construction Identification Algorithm Based on 3D Point Cloud Segmentation

    NASA Astrophysics Data System (ADS)

    An, Lu; Guo, Baolong

    2018-03-01

    Recently, various illegal constructions occur significantly in our surroundings, which seriously restrict the orderly development of urban modernization. The 3D point cloud data technology is used to identify the illegal buildings, which could address the problem above effectively. This paper proposes an outdoor illegal construction identification algorithm based on 3D point cloud segmentation. Initially, in order to save memory space and reduce processing time, a lossless point cloud compression method based on minimum spanning tree is proposed. Then, a ground point removing method based on the multi-scale filtering is introduced to increase accuracy. Finally, building clusters on the ground can be obtained using a region growing method, as a result, the illegal construction can be marked. The effectiveness of the proposed algorithm is verified using a publicly data set collected from the International Society for Photogrammetry and Remote Sensing (ISPRS).

  7. Real-time automatic registration in optical surgical navigation

    NASA Astrophysics Data System (ADS)

    Lin, Qinyong; Yang, Rongqian; Cai, Ken; Si, Xuan; Chen, Xiuwen; Wu, Xiaoming

    2016-05-01

    An image-guided surgical navigation system requires the improvement of the patient-to-image registration time to enhance the convenience of the registration procedure. A critical step in achieving this aim is performing a fully automatic patient-to-image registration. This study reports on a design of custom fiducial markers and the performance of a real-time automatic patient-to-image registration method using these markers on the basis of an optical tracking system for rigid anatomy. The custom fiducial markers are designed to be automatically localized in both patient and image spaces. An automatic localization method is performed by registering a point cloud sampled from the three dimensional (3D) pedestal model surface of a fiducial marker to each pedestal of fiducial markers searched in image space. A head phantom is constructed to estimate the performance of the real-time automatic registration method under four fiducial configurations. The head phantom experimental results demonstrate that the real-time automatic registration method is more convenient, rapid, and accurate than the manual method. The time required for each registration is approximately 0.1 s. The automatic localization method precisely localizes the fiducial markers in image space. The averaged target registration error for the four configurations is approximately 0.7 mm. The automatic registration performance is independent of the positions relative to the tracking system and the movement of the patient during the operation.

  8. A Parallel Point Matching Algorithm for Landmark Based Image Registration Using Multicore Platform

    PubMed Central

    Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L.; Foran, David J.

    2013-01-01

    Point matching is crucial for many computer vision applications. Establishing the correspondence between a large number of data points is a computationally intensive process. Some point matching related applications, such as medical image registration, require real time or near real time performance if applied to critical clinical applications like image assisted surgery. In this paper, we report a new multicore platform based parallel algorithm for fast point matching in the context of landmark based medical image registration. We introduced a non-regular data partition algorithm which utilizes the K-means clustering algorithm to group the landmarks based on the number of available processing cores, which optimize the memory usage and data transfer. We have tested our method using the IBM Cell Broadband Engine (Cell/B.E.) platform. The results demonstrated a significant speed up over its sequential implementation. The proposed data partition and parallelization algorithm, though tested only on one multicore platform, is generic by its design. Therefore the parallel algorithm can be extended to other computing platforms, as well as other point matching related applications. PMID:24308014

  9. Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics

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

    Van Aardt, Jan; Romanczyk, Paul; van Leeuwen, Martin

    Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud datamore » and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.« less

  10. Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics

    DOE PAGES

    Van Aardt, Jan; Romanczyk, Paul; van Leeuwen, Martin; ...

    2016-04-04

    Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud datamore » and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.« less

  11. Underwater 3d Modeling: Image Enhancement and Point Cloud Filtering

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

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

    PubMed Central

    Liu, Lujiang; Zhao, Gaopeng; Bo, Yuming

    2016-01-01

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

  13. GPU-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites.

    PubMed

    Leinweber, Matthias; Fober, Thomas; Freisleben, Bernd

    2018-01-01

    In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.

  14. Gene Expression Data to Mouse Atlas Registration Using a Nonlinear Elasticity Smoother and Landmark Points Constraints

    PubMed Central

    Lin, Tungyou; Guyader, Carole Le; Dinov, Ivo; Thompson, Paul; Toga, Arthur; Vese, Luminita

    2013-01-01

    This paper proposes a numerical algorithm for image registration using energy minimization and nonlinear elasticity regularization. Application to the registration of gene expression data to a neuroanatomical mouse atlas in two dimensions is shown. We apply a nonlinear elasticity regularization to allow larger and smoother deformations, and further enforce optimality constraints on the landmark points distance for better feature matching. To overcome the difficulty of minimizing the nonlinear elasticity functional due to the nonlinearity in the derivatives of the displacement vector field, we introduce a matrix variable to approximate the Jacobian matrix and solve for the simplified Euler-Lagrange equations. By comparison with image registration using linear regularization, experimental results show that the proposed nonlinear elasticity model also needs fewer numerical corrections such as regridding steps for binary image registration, it renders better ground truth, and produces larger mutual information; most importantly, the landmark points distance and L2 dissimilarity measure between the gene expression data and corresponding mouse atlas are smaller compared with the registration model with biharmonic regularization. PMID:24273381

  15. 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

  16. Parallel Processing of Big Point Clouds Using Z-Order Partitioning

    NASA Astrophysics Data System (ADS)

    Alis, C.; Boehm, J.; Liu, K.

    2016-06-01

    As laser scanning technology improves and costs are coming down, the amount of point cloud data being generated can be prohibitively difficult and expensive to process on a single machine. This data explosion is not only limited to point cloud data. Voluminous amounts of high-dimensionality and quickly accumulating data, collectively known as Big Data, such as those generated by social media, Internet of Things devices and commercial transactions, are becoming more prevalent as well. New computing paradigms and frameworks are being developed to efficiently handle the processing of Big Data, many of which utilize a compute cluster composed of several commodity grade machines to process chunks of data in parallel. A central concept in many of these frameworks is data locality. By its nature, Big Data is large enough that the entire dataset would not fit on the memory and hard drives of a single node hence replicating the entire dataset to each worker node is impractical. The data must then be partitioned across worker nodes in a manner that minimises data transfer across the network. This is a challenge for point cloud data because there exist different ways to partition data and they may require data transfer. We propose a partitioning based on Z-order which is a form of locality-sensitive hashing. The Z-order or Morton code is computed by dividing each dimension to form a grid then interleaving the binary representation of each dimension. For example, the Z-order code for the grid square with coordinates (x = 1 = 012, y = 3 = 112) is 10112 = 11. The number of points in each partition is controlled by the number of bits per dimension: the more bits, the fewer the points. The number of bits per dimension also controls the level of detail with more bits yielding finer partitioning. We present this partitioning method by implementing it on Apache Spark and investigating how different parameters affect the accuracy and running time of the k nearest neighbour algorithm

  17. Point Cloud Based Approach to Stem Width Extraction of Sorghum

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

    Jin, Jihui; Zakhor, Avideh

    A revolution in the field of genomics has produced vast amounts of data and furthered our understanding of the genotypephenotype map, but is currently constrained by manually intensive or limited phenotype data collection. We propose an algorithm to estimate stem width, a key characteristic used for biomass potential evaluation, from 3D point cloud data collected by a robot equipped with a depth sensor in a single pass in a standard field. The algorithm applies a two step alignment to register point clouds in different frames, a Frangi filter to identify stemlike objects in the point cloud and an orientation basedmore » filter to segment out and refine individual stems for width estimation. Individually, detected stems which are split due to occlusions are merged and then registered with previously found stems in previous camera frames in order to track temporally. We then refine the estimates to produce an accurate histogram of width estimates per plot. Since the plants in each plot are genetically identical, distributions of the stem width per plot can be useful in identifying genetically superior sorghum for biofuels.« less

  18. Point Cloud Based Approach to Stem Width Extraction of Sorghum

    DOE PAGES

    Jin, Jihui; Zakhor, Avideh

    2017-01-29

    A revolution in the field of genomics has produced vast amounts of data and furthered our understanding of the genotypephenotype map, but is currently constrained by manually intensive or limited phenotype data collection. We propose an algorithm to estimate stem width, a key characteristic used for biomass potential evaluation, from 3D point cloud data collected by a robot equipped with a depth sensor in a single pass in a standard field. The algorithm applies a two step alignment to register point clouds in different frames, a Frangi filter to identify stemlike objects in the point cloud and an orientation basedmore » filter to segment out and refine individual stems for width estimation. Individually, detected stems which are split due to occlusions are merged and then registered with previously found stems in previous camera frames in order to track temporally. We then refine the estimates to produce an accurate histogram of width estimates per plot. Since the plants in each plot are genetically identical, distributions of the stem width per plot can be useful in identifying genetically superior sorghum for biofuels.« less

  19. 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.

  20. 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.

  1. 3D matching techniques using OCT fingerprint point clouds

    NASA Astrophysics Data System (ADS)

    Gutierrez da Costa, Henrique S.; Silva, Luciano; Bellon, Olga R. P.; Bowden, Audrey K.; Czovny, Raphael K.

    2017-02-01

    Optical Coherence Tomography (OCT) makes viable acquisition of 3D fingerprints from both dermis and epidermis skin layers and their interfaces, exposing features that can be explored to improve biometric identification such as the curvatures and distinctive 3D regions. Scanned images from eleven volunteers allowed the construction of the first OCT 3D fingerprint database, to our knowledge, containing epidermal and dermal fingerprints. 3D dermal fingerprints can be used to overcome cases of Failure to Enroll (FTE) due to poor ridge image quality and skin alterations, cases that affect 2D matching performance. We evaluate three matching techniques, including the well-established Iterative Closest Points algorithm (ICP), Surface Interpenetration Measure (SIM) and the well-known KH Curvature Maps, all assessed using a 3D OCT fingerprint database, the first one for this purpose. Two of these techniques are based on registration techniques and one on curvatures. These were evaluated, compared and the fusion of matching scores assessed. We applied a sequence of steps to extract regions of interest named (ROI) minutiae clouds, representing small regions around distinctive minutia, usually located at ridges/valleys endings or bifurcations. The obtained ROI is acquired from the epidermis and dermis-epidermis interface by OCT imaging. A comparative analysis of identification accuracy was explored using different scenarios and the obtained results shows improvements for biometric identification. A comparison against 2D fingerprint matching algorithms is also presented to assess the improvements.

  2. Surface registration technique for close-range mapping applications

    NASA Astrophysics Data System (ADS)

    Habib, Ayman F.; Cheng, Rita W. T.

    2006-08-01

    Close-range mapping applications such as cultural heritage restoration, virtual reality modeling for the entertainment industry, and anatomical feature recognition for medical activities require 3D data that is usually acquired by high resolution close-range laser scanners. Since these datasets are typically captured from different viewpoints and/or at different times, accurate registration is a crucial procedure for 3D modeling of mapped objects. Several registration techniques are available that work directly with the raw laser points or with extracted features from the point cloud. Some examples include the commonly known Iterative Closest Point (ICP) algorithm and a recently proposed technique based on matching spin-images. This research focuses on developing a surface matching algorithm that is based on the Modified Iterated Hough Transform (MIHT) and ICP to register 3D data. The proposed algorithm works directly with the raw 3D laser points and does not assume point-to-point correspondence between two laser scans. The algorithm can simultaneously establish correspondence between two surfaces and estimates the transformation parameters relating them. Experiment with two partially overlapping laser scans of a small object is performed with the proposed algorithm and shows successful registration. A high quality of fit between the two scans is achieved and improvement is found when compared to the results obtained using the spin-image technique. The results demonstrate the feasibility of the proposed algorithm for registering 3D laser scanning data in close-range mapping applications to help with the generation of complete 3D models.

  3. 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.

  4. 3D reconstruction from non-uniform point clouds via local hierarchical clustering

    NASA Astrophysics Data System (ADS)

    Yang, Jiaqi; Li, Ruibo; Xiao, Yang; Cao, Zhiguo

    2017-07-01

    Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.

  5. Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points - A Review.

    PubMed

    Zou, Weibao; Li, Yan; Li, Zhilin; Ding, Xiaoli

    2009-01-01

    Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram.

  6. 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.

  7. 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).

  8. 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.

  9. Applications of 3D-EDGE Detection for ALS Point Cloud

    NASA Astrophysics Data System (ADS)

    Ni, H.; Lin, X. G.; Zhang, J. X.

    2017-09-01

    Edge detection has been one of the major issues in the field of remote sensing and photogrammetry. With the fast development of sensor technology of laser scanning system, dense point clouds have become increasingly common. Precious 3D-edges are able to be detected from these point clouds and a great deal of edge or feature line extraction methods have been proposed. Among these methods, an easy-to-use 3D-edge detection method, AGPN (Analyzing Geometric Properties of Neighborhoods), has been proposed. The AGPN method detects edges based on the analysis of geometric properties of a query point's neighbourhood. The AGPN method detects two kinds of 3D-edges, including boundary elements and fold edges, and it has many applications. This paper presents three applications of AGPN, i.e., 3D line segment extraction, ground points filtering, and ground breakline extraction. Experiments show that the utilization of AGPN method gives a straightforward solution to these applications.

  10. 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

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  12. A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

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

    Liu, Wenyang; Cheung, Yam; Sawant, Amit

    2016-05-15

    Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparsemore » regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the

  13. A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system.

    PubMed

    Liu, Wenyang; Cheung, Yam; Sawant, Amit; Ruan, Dan

    2016-05-01

    To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. The authors have

  14. A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

    PubMed Central

    Liu, Wenyang; Cheung, Yam; Sawant, Amit; Ruan, Dan

    2016-01-01

    Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced

  15. Classification of Dual-Wavelength Airborne Laser Scanning Point Cloud Based on the Radiometric Properties of the Objects

    NASA Astrophysics Data System (ADS)

    Pilarska, M.

    2018-05-01

    Airborne laser scanning (ALS) is a well-known and willingly used technology. One of the advantages of this technology is primarily its fast and accurate data registration. In recent years ALS is continuously developed. One of the latest achievements is multispectral ALS, which consists in obtaining simultaneously the data in more than one laser wavelength. In this article the results of the dual-wavelength ALS data classification are presented. The data were acquired with RIEGL VQ-1560i sensor, which is equipped with two laser scanners operating in different wavelengths: 532 nm and 1064 nm. Two classification approaches are presented in the article: classification, which is based on geometric relationships between points and classification, which mostly relies on the radiometric properties of registered objects. The overall accuracy of the geometric classification was 86 %, whereas for the radiometric classification it was 81 %. As a result, it can be assumed that the radiometric features which are provided by the multispectral ALS have potential to be successfully used in ALS point cloud classification.

  16. 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.

  17. 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

  18. plas.io: Open Source, Browser-based WebGL Point Cloud Visualization

    NASA Astrophysics Data System (ADS)

    Butler, H.; Finnegan, D. C.; Gadomski, P. J.; Verma, U. K.

    2014-12-01

    Point cloud data, in the form of Light Detection and Ranging (LiDAR), RADAR, or semi-global matching (SGM) image processing, are rapidly becoming a foundational data type to quantify and characterize geospatial processes. Visualization of these data, due to overall volume and irregular arrangement, is often difficult. Technological advancement in web browsers, in the form of WebGL and HTML5, have made interactivity and visualization capabilities ubiquitously available which once only existed in desktop software. plas.io is an open source JavaScript application that provides point cloud visualization, exploitation, and compression features in a web-browser platform, reducing the reliance for client-based desktop applications. The wide reach of WebGL and browser-based technologies mean plas.io's capabilities can be delivered to a diverse list of devices -- from phones and tablets to high-end workstations -- with very little custom software development. These properties make plas.io an ideal open platform for researchers and software developers to communicate visualizations of complex and rich point cloud data to devices to which everyone has easy access.

  19. 3D reconstruction of wooden member of ancient architecture from point clouds

    NASA Astrophysics Data System (ADS)

    Zhang, Ruiju; Wang, Yanmin; Li, Deren; Zhao, Jun; Song, Daixue

    2006-10-01

    This paper presents a 3D reconstruction method to model wooden member of ancient architecture from point clouds based on improved deformable model. Three steps are taken to recover the shape of wooden member. Firstly, Hessian matrix is adopted to compute the axe of wooden member. Secondly, an initial model of wooden member is made by contour orthogonal to its axis. Thirdly, an accurate model is got through the coupling effect between the initial model and the point clouds of the wooden member according to the theory of improved deformable model. Every step and algorithm is studied and described in the paper. Using the point clouds captured from Forbidden City of China, shaft member and beam member are taken as examples to test the method proposed in the paper. Results show the efficiency and robustness of the method addressed in the literature to model the wooden member of ancient architecture.

  20. Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network

    NASA Astrophysics Data System (ADS)

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

    2017-11-01

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

  1. 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.

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

    PubMed

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

    2018-03-28

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

  3. Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration

    PubMed Central

    Kim, Hyunjoo; Parashar, Manish; Foran, David J.; Yang, Lin

    2010-01-01

    This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues. A policy-driven scheduling agent uses the QoS constraints along with performance history and the state of the resources to determine the appropriate size and mix of the public and private cloud resource that should be allocated to a specific request. The virtual computational cloud and the medical image registration service have been developed using the CometCloud engine and have been deployed on a combination of private clouds at Rutgers University and the Cancer Institute of New Jersey and Amazon EC2. An experimental evaluation is presented and demonstrates the effectiveness of autonomic cloudbursts and policy-based autonomic scheduling for this application. PMID:20640235

  4. Three-dimensional reconstruction of indoor whole elements based on mobile LiDAR point cloud data

    NASA Astrophysics Data System (ADS)

    Gong, Yuejian; Mao, Wenbo; Bi, Jiantao; Ji, Wei; He, Zhanjun

    2014-11-01

    Ground-based LiDAR is one of the most effective city modeling tools at present, which has been widely used for three-dimensional reconstruction of outdoor objects. However, as for indoor objects, there are some technical bottlenecks due to lack of GPS signal. In this paper, based on the high-precision indoor point cloud data which was obtained by LiDAR, an international advanced indoor mobile measuring equipment, high -precision model was fulfilled for all indoor ancillary facilities. The point cloud data we employed also contain color feature, which is extracted by fusion with CCD images. Thus, it has both space geometric feature and spectral information which can be used for constructing objects' surface and restoring color and texture of the geometric model. Based on Autodesk CAD platform and with help of PointSence plug, three-dimensional reconstruction of indoor whole elements was realized. Specifically, Pointools Edit Pro was adopted to edit the point cloud, then different types of indoor point cloud data was processed, including data format conversion, outline extracting and texture mapping of the point cloud model. Finally, three-dimensional visualization of the real-world indoor was completed. Experiment results showed that high-precision 3D point cloud data obtained by indoor mobile measuring equipment can be used for indoor whole elements' 3-d reconstruction and that methods proposed in this paper can efficiently realize the 3 -d construction of indoor whole elements. Moreover, the modeling precision could be controlled within 5 cm, which was proved to be a satisfactory result.

  5. Automatic Monitoring of Tunnel Deformation Based on High Density Point Clouds Data

    NASA Astrophysics Data System (ADS)

    Du, L.; Zhong, R.; Sun, H.; Wu, Q.

    2017-09-01

    An automated method for tunnel deformation monitoring using high density point clouds data is presented. Firstly, the 3D point clouds data are converted to two-dimensional surface by projection on the XOY plane, the projection point set of central axis on XOY plane named Uxoy is calculated by combining the Alpha Shape algorithm with RANSAC (Random Sampling Consistency) algorithm, and then the projection point set of central axis on YOZ plane named Uyoz is obtained by highest and lowest points which are extracted by intersecting straight lines that through each point of Uxoy and perpendicular to the two -dimensional surface with the tunnel point clouds, Uxoy and Uyoz together form the 3D center axis finally. Secondly, the buffer of each cross section is calculated by K-Nearest neighbor algorithm, and the initial cross-sectional point set is quickly constructed by projection method. Finally, the cross sections are denoised and the section lines are fitted using the method of iterative ellipse fitting. In order to improve the accuracy of the cross section, a fine adjustment method is proposed to rotate the initial sectional plane around the intercept point in the horizontal and vertical direction within the buffer. The proposed method is used in Shanghai subway tunnel, and the deformation of each section in the direction of 0 to 360 degrees is calculated. The result shows that the cross sections becomes flat circles from regular circles due to the great pressure at the top of the tunnel

  6. Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration.

    PubMed

    Pycinski, Bartlomiej; Czajkowska, Joanna; Badura, Pawel; Juszczyk, Jan; Pietka, Ewa

    2016-01-01

    A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed. We present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm. The experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera. The obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers.

  7. Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

    PubMed Central

    Badura, Pawel; Juszczyk, Jan; Pietka, Ewa

    2016-01-01

    Purpose A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed. Methods We present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm. Results The experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera. Conclusion The obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers

  8. Cost-effective surgical registration using consumer depth cameras

    NASA Astrophysics Data System (ADS)

    Potter, Michael; Yaniv, Ziv

    2016-03-01

    The high costs associated with technological innovation have been previously identified as both a major contributor to the rise of health care expenses, and as a limitation for widespread adoption of new technologies. In this work we evaluate the use of two consumer grade depth cameras, the Microsoft Kinect v1 and 3DSystems Sense, as a means for acquiring point clouds for registration. These devices have the potential to replace professional grade laser range scanning devices in medical interventions that do not require sub-millimetric registration accuracy, and may do so at a significantly reduced cost. To facilitate the use of these devices we have developed a near real-time (1-4 sec/frame) rigid registration framework combining several alignment heuristics with the Iterative Closest Point (ICP) algorithm. Using nearest neighbor registration error as our evaluation criterion we found the optimal scanning distances for the Sense and Kinect to be 50-60cm and 70-80cm respectively. When imaging a skull phantom at these distances, RMS error values of 1.35mm and 1.14mm were obtained. The registration framework was then evaluated using cranial MR scans of two subjects. For the first subject, the RMS error using the Sense was 1.28 +/- 0.01 mm. Using the Kinect this error was 1.24 +/- 0.03 mm. For the second subject, whose MR scan was significantly corrupted by metal implants, the errors increased to 1.44 +/- 0.03 mm and 1.74 +/- 0.06 mm but the system nonetheless performed within acceptable bounds.

  9. 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

  10. Multiview 3D sensing and analysis for high quality point cloud reconstruction

    NASA Astrophysics Data System (ADS)

    Satnik, Andrej; Izquierdo, Ebroul; Orjesek, Richard

    2018-04-01

    Multiview 3D reconstruction techniques enable digital reconstruction of 3D objects from the real world by fusing different viewpoints of the same object into a single 3D representation. This process is by no means trivial and the acquisition of high quality point cloud representations of dynamic 3D objects is still an open problem. In this paper, an approach for high fidelity 3D point cloud generation using low cost 3D sensing hardware is presented. The proposed approach runs in an efficient low-cost hardware setting based on several Kinect v2 scanners connected to a single PC. It performs autocalibration and runs in real-time exploiting an efficient composition of several filtering methods including Radius Outlier Removal (ROR), Weighted Median filter (WM) and Weighted Inter-Frame Average filtering (WIFA). The performance of the proposed method has been demonstrated through efficient acquisition of dense 3D point clouds of moving objects.

  11. Improvement of registration accuracy in accelerated partial breast irradiation using the point-based rigid-body registration algorithm for patients with implanted fiducial markers

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

    Inoue, Minoru; Yoshimura, Michio, E-mail: myossy@kuhp.kyoto-u.ac.jp; Sato, Sayaka

    2015-04-15

    Purpose: To investigate image-registration errors when using fiducial markers with a manual method and the point-based rigid-body registration (PRBR) algorithm in accelerated partial breast irradiation (APBI) patients, with accompanying fiducial deviations. Methods: Twenty-two consecutive patients were enrolled in a prospective trial examining 10-fraction APBI. Titanium clips were implanted intraoperatively around the seroma in all patients. For image-registration, the positions of the clips in daily kV x-ray images were matched to those in the planning digitally reconstructed radiographs. Fiducial and gravity registration errors (FREs and GREs, respectively), representing resulting misalignments of the edge and center of the target, respectively, were comparedmore » between the manual and algorithm-based methods. Results: In total, 218 fractions were evaluated. Although the mean FRE/GRE values for the manual and algorithm-based methods were within 3 mm (2.3/1.7 and 1.3/0.4 mm, respectively), the percentages of fractions where FRE/GRE exceeded 3 mm using the manual and algorithm-based methods were 18.8%/7.3% and 0%/0%, respectively. Manual registration resulted in 18.6% of patients with fractions of FRE/GRE exceeding 5 mm. The patients with larger clip deviation had significantly more fractions showing large FRE/GRE using manual registration. Conclusions: For image-registration using fiducial markers in APBI, the manual registration results in more fractions with considerable registration error due to loss of fiducial objectivity resulting from their deviation. The authors recommend the PRBR algorithm as a safe and effective strategy for accurate, image-guided registration and PTV margin reduction.« less

  12. Robust group-wise rigid registration of point sets using t-mixture model

    NASA Astrophysics Data System (ADS)

    Ravikumar, Nishant; Gooya, Ali; Frangi, Alejandro F.; Taylor, Zeike A.

    2016-03-01

    A probabilistic framework for robust, group-wise rigid alignment of point-sets using a mixture of Students t-distribution especially when the point sets are of varying lengths, are corrupted by an unknown degree of outliers or in the presence of missing data. Medical images (in particular magnetic resonance (MR) images), their segmentations and consequently point-sets generated from these are highly susceptible to corruption by outliers. This poses a problem for robust correspondence estimation and accurate alignment of shapes, necessary for training statistical shape models (SSMs). To address these issues, this study proposes to use a t-mixture model (TMM), to approximate the underlying joint probability density of a group of similar shapes and align them to a common reference frame. The heavy-tailed nature of t-distributions provides a more robust registration framework in comparison to state of the art algorithms. Significant reduction in alignment errors is achieved in the presence of outliers, using the proposed TMM-based group-wise rigid registration method, in comparison to its Gaussian mixture model (GMM) counterparts. The proposed TMM-framework is compared with a group-wise variant of the well-known Coherent Point Drift (CPD) algorithm and two other group-wise methods using GMMs, using both synthetic and real data sets. Rigid alignment errors for groups of shapes are quantified using the Hausdorff distance (HD) and quadratic surface distance (QSD) metrics.

  13. Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

    PubMed Central

    Zou, Weibao; Li, Yan; Li, Zhilin; Ding, Xiaoli

    2009-01-01

    Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram. PMID:22399966

  14. Adaptive marker-free registration using a multiple point strategy for real-time and robust endoscope electromagnetic navigation.

    PubMed

    Luo, Xiongbiao; Wan, Ying; He, Xiangjian; Mori, Kensaku

    2015-02-01

    Registration of pre-clinical images to physical space is indispensable for computer-assisted endoscopic interventions in operating rooms. Electromagnetically navigated endoscopic interventions are increasingly performed at current diagnoses and treatments. Such interventions use an electromagnetic tracker with a miniature sensor that is usually attached at an endoscope distal tip to real time track endoscope movements in a pre-clinical image space. Spatial alignment between the electromagnetic tracker (or sensor) and pre-clinical images must be performed to navigate the endoscope to target regions. This paper proposes an adaptive marker-free registration method that uses a multiple point selection strategy. This method seeks to address an assumption that the endoscope is operated along the centerline of an intraluminal organ which is easily violated during interventions. We introduce an adaptive strategy that generates multiple points in terms of sensor measurements and endoscope tip center calibration. From these generated points, we adaptively choose the optimal point, which is the closest to its assigned the centerline of the hollow organ, to perform registration. The experimental results demonstrate that our proposed adaptive strategy significantly reduced the target registration error from 5.32 to 2.59 mm in static phantoms validation, as well as from at least 7.58 mm to 4.71 mm in dynamic phantom validation compared to current available methods. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

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

    PubMed Central

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

    2018-01-01

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

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

    PubMed Central

    Arastounia, Mostafa; Oude Elberink, Sander

    2016-01-01

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

  18. 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.

  19. Image registration with uncertainty analysis

    DOEpatents

    Simonson, Katherine M [Cedar Crest, NM

    2011-03-22

    In an image registration method, edges are detected in a first image and a second image. A percentage of edge pixels in a subset of the second image that are also edges in the first image shifted by a translation is calculated. A best registration point is calculated based on a maximum percentage of edges matched. In a predefined search region, all registration points other than the best registration point are identified that are not significantly worse than the best registration point according to a predetermined statistical criterion.

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

    PubMed

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

    2017-06-01

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

  1. Comparing and characterizing three-dimensional point clouds derived by structure from motion photogrammetry

    NASA Astrophysics Data System (ADS)

    Schwind, Michael

    Structure from Motion (SfM) is a photogrammetric technique whereby three-dimensional structures (3D) are estimated from overlapping two-dimensional (2D) image sequences. It is studied in the field of computer vision and utilized in fields such as archeology, engineering, and the geosciences. Currently, many SfM software packages exist that allow for the generation of 3D point clouds. Little work has been done to show how topographic data generated from these software differ over varying terrain types and why they might produce different results. This work aims to compare and characterize the differences between point clouds generated by three different SfM software packages: two well-known proprietary solutions (Pix4D, Agisoft PhotoScan) and one open source solution (OpenDroneMap). Five terrain types were imaged utilizing a DJI Phantom 3 Professional small unmanned aircraft system (sUAS). These terrain types include a marsh environment, a gently sloped sandy beach and jetties, a forested peninsula, a house, and a flat parking lot. Each set of imagery was processed with each software and then directly compared to each other. Before processing the sets of imagery, the software settings were analyzed and chosen in a manner that allowed for the most similar settings to be set across the three software types. This was done in an attempt to minimize point cloud differences caused by dissimilar settings. The characteristics of the resultant point clouds were then compared with each other. Furthermore, a terrestrial light detection and ranging (LiDAR) survey was conducted over the flat parking lot using a Riegl VZ- 400 scanner. This data served as ground truth in order to conduct an accuracy assessment of the sUAS-SfM point clouds. Differences were found between the different results, apparent not only in the characteristics of the clouds, but also the accuracy. This study allows for users of SfM photogrammetry to have a better understanding of how different processing

  2. Multiseasonal Tree Crown Structure Mapping with Point Clouds from OTS Quadrocopter Systems

    NASA Astrophysics Data System (ADS)

    Hese, S.; Behrendt, F.

    2017-08-01

    OTF (Off The Shelf) quadro copter systems provide a cost effective (below 2000 Euro), flexible and mobile platform for high resolution point cloud mapping. Various studies showed the full potential of these small and flexible platforms. Especially in very tight and complex 3D environments the automatic obstacle avoidance, low copter weight, long flight times and precise maneuvering are important advantages of these small OTS systems in comparison with larger octocopter systems. This study examines the potential of the DJI Phantom 4 pro series and the Phantom 3A series for within-stand and forest tree crown 3D point cloud mapping using both within stand oblique imaging in different altitude levels and data captured from a nadir perspective. On a test site in Brandenburg/Germany a beach crown was selected and measured with 3 different altitude levels in Point Of Interest (POI) mode with oblique data capturing and deriving one nadir mosaic created with 85/85 % overlap using Drone Deploy automatic mapping software. Three different flight campaigns were performed, one in September 2016 (leaf-on), one in March 2017 (leaf-off) and one in May 2017 (leaf-on) to derive point clouds from different crown structure and phenological situations - covering the leaf-on and leafoff status of the tree crown. After height correction, the point clouds where used with GPS geo referencing to calculate voxel based densities on 50 × 10 × 10 cm voxel definitions using a topological network of chessboard image objects in 0,5 m height steps in an object based image processing environment. Comparison between leaf-off and leaf-on status was done on volume pixel definitions comparing the attributed point densities per volume and plotting the resulting values as a function of distance to the crown center. In the leaf-off status SFM (structure from motion) algorithms clearly identified the central stem and also secondary branch systems. While the

  3. 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.

  4. 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

  5. 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.

  6. A hierarchical methodology for urban facade parsing from TLS point clouds

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  7. 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

  8. Photogrammetric point cloud compression for tactical networks

    NASA Astrophysics Data System (ADS)

    Madison, Andrew C.; Massaro, Richard D.; Wayant, Clayton D.; Anderson, John E.; Smith, Clint B.

    2017-05-01

    We report progress toward the development of a compression schema suitable for use in the Army's Common Operating Environment (COE) tactical network. The COE facilitates the dissemination of information across all Warfighter echelons through the establishment of data standards and networking methods that coordinate the readout and control of a multitude of sensors in a common operating environment. When integrated with a robust geospatial mapping functionality, the COE enables force tracking, remote surveillance, and heightened situational awareness to Soldiers at the tactical level. Our work establishes a point cloud compression algorithm through image-based deconstruction and photogrammetric reconstruction of three-dimensional (3D) data that is suitable for dissimination within the COE. An open source visualization toolkit was used to deconstruct 3D point cloud models based on ground mobile light detection and ranging (LiDAR) into a series of images and associated metadata that can be easily transmitted on a tactical network. Stereo photogrammetric reconstruction is then conducted on the received image stream to reveal the transmitted 3D model. The reported method boasts nominal compression ratios typically on the order of 250 while retaining tactical information and accurate georegistration. Our work advances the scope of persistent intelligence, surveillance, and reconnaissance through the development of 3D visualization and data compression techniques relevant to the tactical operations environment.

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

    PubMed

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

    2018-02-03

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

  10. Feasibility of Smartphone Based Photogrammetric Point Clouds for the Generation of Accessibility Maps

    NASA Astrophysics Data System (ADS)

    Angelats, E.; Parés, M. E.; Kumar, P.

    2018-05-01

    Accessible cities with accessible services are an old claim of people with reduced mobility. But this demand is still far away of becoming a reality as lot of work is required to be done yet. First step towards accessible cities is to know about real situation of the cities and its pavement infrastructure. Detailed maps or databases on street slopes, access to sidewalks, mobility in public parks and gardens, etc. are required. In this paper, we propose to use smartphone based photogrammetric point clouds, as a starting point to create accessible maps or databases. This paper analyses the performance of these point clouds and the complexity of the image acquisition procedure required to obtain them. The paper proves, through two test cases, that smartphone technology is an economical and feasible solution to get the required information, which is quite often seek by city planners to generate accessible maps. The proposed approach paves the way to generate, in a near term, accessibility maps through the use of point clouds derived from crowdsourced smartphone imagery.

  11. Study into Point Cloud Geometric Rigidity and Accuracy of TLS-Based Identification of Geometric Bodies

    NASA Astrophysics Data System (ADS)

    Klapa, Przemyslaw; Mitka, Bartosz; Zygmunt, Mariusz

    2017-12-01

    Capability of obtaining a multimillion point cloud in a very short time has made the Terrestrial Laser Scanning (TLS) a widely used tool in many fields of science and technology. The TLS accuracy matches traditional devices used in land surveying (tacheometry, GNSS - RTK), but like any measurement it is burdened with error which affects the precise identification of objects based on their image in the form of a point cloud. The point’s coordinates are determined indirectly by means of measuring the angles and calculating the time of travel of the electromagnetic wave. Each such component has a measurement error which is translated into the final result. The XYZ coordinates of a measuring point are determined with some uncertainty and the very accuracy of determining these coordinates is reduced as the distance to the instrument increases. The paper presents the results of examination of geometrical stability of a point cloud obtained by means terrestrial laser scanner and accuracy evaluation of solids determined using the cloud. Leica P40 scanner and two different settings of measuring points were used in the tests. The first concept involved placing a few balls in the field and then scanning them from various sides at similar distances. The second part of measurement involved placing balls and scanning them a few times from one side but at varying distances from the instrument to the object. Each measurement encompassed a scan of the object with automatic determination of its position and geometry. The desk studies involved a semiautomatic fitting of solids and measurement of their geometrical elements, and comparison of parameters that determine their geometry and location in space. The differences of measures of geometrical elements of balls and translations vectors of the solids centres indicate the geometrical changes of the point cloud depending on the scanning distance and parameters. The results indicate the changes in the geometry of scanned objects

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

    NASA Astrophysics Data System (ADS)

    Bolkas, Dimitrios; Martinez, Aaron

    2018-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-08-01

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

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

    NASA Astrophysics Data System (ADS)

    Gézero, L.; Antunes, C.

    2017-05-01

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

  15. 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

  16. Registration and fusion quantification of augmented reality based nasal endoscopic surgery.

    PubMed

    Chu, Yakui; Yang, Jian; Ma, Shaodong; Ai, Danni; Li, Wenjie; Song, Hong; Li, Liang; Chen, Duanduan; Chen, Lei; Wang, Yongtian

    2017-12-01

    This paper quantifies the registration and fusion display errors of augmented reality-based nasal endoscopic surgery (ARNES). We comparatively investigated the spatial calibration process for front-end endoscopy and redefined the accuracy level of a calibrated endoscope by using a calibration tool with improved structural reliability. We also studied how registration accuracy was combined with the number and distribution of the deployed fiducial points (FPs) for positioning and the measured registration time. A physically integrated ARNES prototype was customarily configured for performance evaluation in skull base tumor resection surgery with an innovative approach of dynamic endoscopic vision expansion. As advised by surgical experts in otolaryngology, we proposed a hierarchical rendering scheme to properly adapt the fused images with the required visual sensation. By constraining the rendered sight in a known depth and radius, the visual focus of the surgeon can be induced only on the anticipated critical anatomies and vessel structures to avoid misguidance. Furthermore, error analysis was conducted to examine the feasibility of hybrid optical tracking based on point cloud, which was proposed in our previous work as an in-surgery registration solution. Measured results indicated that the error of target registration for ARNES can be reduced to 0.77 ± 0.07 mm. For initial registration, our results suggest that a trade-off for a new minimal time of registration can be reached when the distribution of five FPs is considered. For in-surgery registration, our findings reveal that the intrinsic registration error is a major cause of performance loss. Rigid model and cadaver experiments confirmed that the scenic integration and display fluency of ARNES are smooth, as demonstrated by three clinical trials that surpassed practicality. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

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

    2018-01-01

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

  18. Registration system of cloud campus by using android smart tablet.

    PubMed

    Kamada, Shin; Ichimura, Takumi; Shigeyasu, Tetsuya; Takemoto, Yasuhiko

    2014-01-01

    Near Field Communication (NFC) standard covers communication protocols and data exchange formats. NFC technology is one of radio-frequency identification (RFID) standards. In Japan, Felica card is a popular way to identify the unique ID. We developed the attendance management system (AMS) as the Android application which works in the smart tablet with NFC. Generally, the AMS in the university is fixed to the wall and each student touches or slides his/her own card to the dedicated equipment. Because a teacher can use his/her own smart tablet and/or smartphone, the attendance records are viewed anytime and anywhere. Moreover, we developed the collecting system between PC and some tablets by using Android beam. Any personal data are encrypted and the file can be transferred over the NFC Bluetooth Handover between PC Linux and smart tablet. By the mining of the collected records, early discovery for chronic non-attenders are extracted in educational affairs section. In this paper, a registration system on the cloud campus system by using the personal smartphone with NFC is developed. The system enables to introduce the university courses that are open to the general public.

  19. Contextual Classification of Point Cloud Data by Exploiting Individual 3d Neigbourhoods

    NASA Astrophysics Data System (ADS)

    Weinmann, M.; Schmidt, A.; Mallet, C.; Hinz, S.; Rottensteiner, F.; Jutzi, B.

    2015-03-01

    The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.

  20. Balloon borne Antarctic frost point measurements and their impact on polar stratospheric cloud theories

    NASA Technical Reports Server (NTRS)

    Rosen, James M.; Hofmann, D. J.; Carpenter, J. R.; Harder, J. W.; Oltmans, S. J.

    1988-01-01

    The first balloon-borne frost point measurements over Antarctica were made during September and October, 1987 as part of the NOZE 2 effort at McMurdo. The results indicate water vapor mixing ratios on the order of 2 ppmv in the 15 to 20 km region which is somewhat smaller than the typical values currently being used significantly smaller than the typical values currently being used in polar stratospheric cloud (PSC) theories. The observed water vapor mixing ratio would correspond to saturated conditions for what is thought to be the lowest stratospheric temperatures encountered over the Antarctic. Through the use of available lidar observations there appears to be significant evidence that some PSCs form at temperatures higher than the local frost point (with respect to water) in the 10 to 20 km region thus supporting the nitric acid theory of PSC composition. Clouds near 15 km and below appear to form in regions saturated with respect to water and thus are probably mostly ice water clouds although they could contain relatively small amounts of other constituents. Photographic evidence suggests that the clouds forming above the frost point probably have an appearance quite different from the lower altitude iridescent, colored nacreous clouds.

  1. 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.

  2. 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.

  3. Sideloading - Ingestion of Large Point Clouds Into the Apache Spark Big Data Engine

    NASA Astrophysics Data System (ADS)

    Boehm, J.; Liu, K.; Alis, C.

    2016-06-01

    In the geospatial domain we have now reached the point where data volumes we handle have clearly grown beyond the capacity of most desktop computers. This is particularly true in the area of point cloud processing. It is therefore naturally lucrative to explore established big data frameworks for big geospatial data. The very first hurdle is the import of geospatial data into big data frameworks, commonly referred to as data ingestion. Geospatial data is typically encoded in specialised binary file formats, which are not naturally supported by the existing big data frameworks. Instead such file formats are supported by software libraries that are restricted to single CPU execution. We present an approach that allows the use of existing point cloud file format libraries on the Apache Spark big data framework. We demonstrate the ingestion of large volumes of point cloud data into a compute cluster. The approach uses a map function to distribute the data ingestion across the nodes of a cluster. We test the capabilities of the proposed method to load billions of points into a commodity hardware compute cluster and we discuss the implications on scalability and performance. The performance is benchmarked against an existing native Apache Spark data import implementation.

  4. Foliage penetration by using 4-D point cloud data

    NASA Astrophysics Data System (ADS)

    Méndez Rodríguez, Javier; Sánchez-Reyes, Pedro J.; Cruz-Rivera, Sol M.

    2012-06-01

    Real-time awareness and rapid target detection are critical for the success of military missions. New technologies capable of detecting targets concealed in forest areas are needed in order to track and identify possible threats. Currently, LAser Detection And Ranging (LADAR) systems are capable of detecting obscured targets; however, tracking capabilities are severely limited. Now, a new LADAR-derived technology is under development to generate 4-D datasets (3-D video in a point cloud format). As such, there is a new need for algorithms that are able to process data in real time. We propose an algorithm capable of removing vegetation and other objects that may obfuscate concealed targets in a real 3-D environment. The algorithm is based on wavelets and can be used as a pre-processing step in a target recognition algorithm. Applications of the algorithm in a real-time 3-D system could help make pilots aware of high risk hidden targets such as tanks and weapons, among others. We will be using a 4-D simulated point cloud data to demonstrate the capabilities of our algorithm.

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

    NASA Astrophysics Data System (ADS)

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

    2017-07-01

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

  6. Alternative Methods for Estimating Plane Parameters Based on a Point Cloud

    NASA Astrophysics Data System (ADS)

    Stryczek, Roman

    2017-12-01

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

  7. Integration of multitemporal and multisource data using point cloud based methods to quantify landscape change at the Mill Gulch earthflow, California

    NASA Astrophysics Data System (ADS)

    DeLong, S. B.; Avdievitch, N. N.

    2014-12-01

    As high-resolution topographic data become increasingly available, comparison of multitemporal and disparate datasets (e.g. airborne and terrestrial lidar) enable high-accuracy quantification of landscape change and detailed mapping of surface processes. However, if these data are not properly managed and aligned with maximum precision, results may be spurious. Often this is due to slight differences in coordinate systems that require complex geographic transformations and systematic error that is difficult to diagnose and correct. Here we present an analysis of four airborne and three terrestrial lidar datasets collected between 2003 and 2014 that we use to quantify change at an active earthflow in Mill Gulch, Sonoma County, California. We first identify and address systematic error internal to each dataset, such as registration offset between flight lines or scan positions. We then use a variant of an iterative closest point (ICP) algorithm to align point cloud data by maximizing use of stable portions of the landscape with minimal internal error. Using products derived from the aligned point clouds, we make our geomorphic analyses. These methods may be especially useful for change detection analyses in which accurate georeferencing is unavailable, as is often the case with some terrestrial lidar or "structure from motion" data. Our results show that the Mill Gulch earthflow has been active throughout the study period. We see continuous downslope flow, ongoing incorporation of new hillslope material into the flow, sediment loss from hillslopes, episodic fluvial erosion of the earthflow toe, and an indication of increased activity during periods of high precipitation.

  8. Image Registration: A Necessary Evil

    NASA Technical Reports Server (NTRS)

    Bell, James; McLachlan, Blair; Hermstad, Dexter; Trosin, Jeff; George, Michael W. (Technical Monitor)

    1995-01-01

    Registration of test and reference images is a key component of nearly all PSP data reduction techniques. This is done to ensure that a test image pixel viewing a particular point on the model is ratioed by the reference image pixel which views the same point. Typically registration is needed to account for model motion due to differing airloads when the wind-off and wind-on images are taken. Registration is also necessary when two cameras are used for simultaneous acquisition of data from a dual-frequency paint. This presentation will discuss the advantages and disadvantages of several different image registration techniques. In order to do so, it is necessary to propose both an accuracy requirement for image registration and a means for measuring the accuracy of a particular technique. High contrast regions in the unregistered images are most sensitive to registration errors, and it is proposed that these regions be used to establish the error limits for registration. Once this is done, the actual registration error can be determined by locating corresponding points on the test and reference images, and determining how well a particular registration technique matches them. An example of this procedure is shown for three transforms used to register images of a semispan model. Thirty control points were located on the model. A subset of the points were used to determine the coefficients of each registration transform, and the error with which each transform aligned the remaining points was determined. The results indicate the general superiority of a third-order polynomial over other candidate transforms, as well as showing how registration accuracy varies with number of control points. Finally, it is proposed that image registration may eventually be done away with completely. As more accurate image resection techniques and more detailed model surface grids become available, it will be possible to map raw image data onto the model surface accurately. Intensity

  9. Quantifying Biomass from Point Clouds by Connecting Representations of Ecosystem Structure

    NASA Astrophysics Data System (ADS)

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

    2017-12-01

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

  10. 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

  11. Automatic Extraction of Road Markings from Mobile Laser-Point Cloud Using Intensity Data

    NASA Astrophysics Data System (ADS)

    Yao, L.; Chen, Q.; Qin, C.; Wu, H.; Zhang, S.

    2018-04-01

    With the development of intelligent transportation, road's high precision information data has been widely applied in many fields. This paper proposes a concise and practical way to extract road marking information from point cloud data collected by mobile mapping system (MMS). The method contains three steps. Firstly, road surface is segmented through edge detection from scan lines. Then the intensity image is generated by inverse distance weighted (IDW) interpolation and the road marking is extracted by using adaptive threshold segmentation based on integral image without intensity calibration. Moreover, the noise is reduced by removing a small number of plaque pixels from binary image. Finally, point cloud mapped from binary image is clustered into marking objects according to Euclidean distance, and using a series of algorithms including template matching and feature attribute filtering for the classification of linear markings, arrow markings and guidelines. Through processing the point cloud data collected by RIEGL VUX-1 in case area, the results show that the F-score of marking extraction is 0.83, and the average classification rate is 0.9.

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

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

  13. Point Clouds to Indoor/outdoor Accessibility Diagnosis

    NASA Astrophysics Data System (ADS)

    Balado, J.; Díaz-Vilariño, L.; Arias, P.; Garrido, I.

    2017-09-01

    This work presents an approach to automatically detect structural floor elements such as steps or ramps in the immediate environment of buildings, elements that may affect the accessibility to buildings. The methodology is based on Mobile Laser Scanner (MLS) point cloud and trajectory information. First, the street is segmented in stretches along the trajectory of the MLS to work in regular spaces. Next, the lower region of each stretch (the ground zone) is selected as the ROI and normal, curvature and tilt are calculated for each point. With this information, points in the ROI are classified in horizontal, inclined or vertical. Points are refined and grouped in structural elements using raster process and connected components in different phases for each type of previously classified points. At last, the trajectory data is used to distinguish between road and sidewalks. Adjacency information is used to classify structural elements in steps, ramps, curbs and curb-ramps. The methodology is tested in a real case study, consisting of 100 m of an urban street. Ground elements are correctly classified in an acceptable computation time. Steps and ramps also are exported to GIS software to enrich building models from Open Street Map with information about accessible/inaccessible entrances and their locations.

  14. Point Cloud Classification of Tesserae from Terrestrial Laser Data Combined with Dense Image Matching for Archaeological Information Extraction

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

    Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

  15. Compliance with prospective trial registration guidance remained low in high-impact journals and has implications for primary end point reporting.

    PubMed

    Dal-Ré, Rafael; Ross, Joseph S; Marušić, Ana

    2016-07-01

    To examine compliance with International Committee of Medical Journal Editors' (ICMJE) policy on prospective trial registration along with predictors of compliance. Cross-sectional analysis of all articles reporting trial results published in the six highest-impact general medicine journals in January-June 2014 that were registered in a public trial registry. The main outcome measure was compliance with ICMJE policy. The time frame for trial primary end point ascertainment was used to assess whether retrospective registration could have allowed changing of primary end points following an interim analysis. Forty of 144 (28%) articles did not comply with the ICMJE policy. Trials of non-FDA-regulated interventions were less compliant than trials of FDA-regulated interventions (i.e., medicines, medical devices) (42% vs. 21%; P = 0.016). Twenty-nine of these 40 (72%; 20% overall) were registered before any interim analysis of primary end points could have been conducted; 11 (28%; 8% overall) were registered after primary end point ascertainment, such that investigators could have had the opportunity to conduct an interim analysis before trial registration. Twenty-eight percent of trials published in high-impact journals were retrospectively registered including nearly 10% that were registered after primary end point ascertainment could have had taken place. Prospective registration should be prompted and enforced to ensure transparency and accountability in clinical research. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.

    PubMed

    Wahabzada, Mirwaes; Paulus, Stefan; Kersting, Kristian; Mahlein, Anne-Katrin

    2015-08-08

    Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation. The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture. An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated - even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant

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

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-11-01

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

  19. 3D local feature BKD to extract road information from mobile laser scanning point clouds

    NASA Astrophysics Data System (ADS)

    Yang, Bisheng; Liu, Yuan; Dong, Zhen; Liang, Fuxun; Li, Bijun; Peng, Xiangyang

    2017-08-01

    Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise.

  20. Biological fiducial point based registration for multiple brain tissues reconstructed from different imaging modalities

    NASA Astrophysics Data System (ADS)

    Wu, Huiqun; Zhou, Gangping; Geng, Xingyun; Zhang, Xiaofeng; Jiang, Kui; Tang, Lemin; Zhou, Guomin; Dong, Jiancheng

    2013-10-01

    With the development of computer aided navigation system, more and more tissues shall be reconstructed to provide more useful information for surgical pathway planning. In this study, we aimed to propose a registration framework for different reconstructed tissues from multi-modalities based on some fiducial points on lateral ventricles. A male patient with brain lesion was admitted and his brain scans were performed by different modalities. Then, the different brain tissues were segmented in different modality with relevant suitable algorithms. Marching cubes were calculated for three dimensional reconstructions, and then the rendered tissues were imported to a common coordinate system for registration. Four pairs of fiducial markers were selected to calculate the rotation and translation matrix using least-square measure method. The registration results were satisfied in a glioblastoma surgery planning as it provides the spatial relationship between tumors and surrounding fibers as well as vessels. Hence, our framework is of potential value for clinicians to plan surgery.

  1. 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.

  2. 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

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

    NASA Astrophysics Data System (ADS)

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

    2017-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-12-01

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

  5. Control over structure-specific flexibility improves anatomical accuracy for point-based deformable registration in bladder cancer radiotherapy

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

    Wognum, S.; Chai, X.; Hulshof, M. C. C. M.

    2013-02-15

    Purpose: Future developments in image guided adaptive radiotherapy (IGART) for bladder cancer require accurate deformable image registration techniques for the precise assessment of tumor and bladder motion and deformation that occur as a result of large bladder volume changes during the course of radiotherapy treatment. The aim was to employ an extended version of a point-based deformable registration algorithm that allows control over tissue-specific flexibility in combination with the authors' unique patient dataset, in order to overcome two major challenges of bladder cancer registration, i.e., the difficulty in accounting for the difference in flexibility between the bladder wall and tumormore » and the lack of visible anatomical landmarks for validation. Methods: The registration algorithm used in the current study is an extension of the symmetric-thin plate splines-robust point matching (S-TPS-RPM) algorithm, a symmetric feature-based registration method. The S-TPS-RPM algorithm has been previously extended to allow control over the degree of flexibility of different structures via a weight parameter. The extended weighted S-TPS-RPM algorithm was tested and validated on CT data (planning- and four to five repeat-CTs) of five urinary bladder cancer patients who received lipiodol injections before radiotherapy. The performance of the weighted S-TPS-RPM method, applied to bladder and tumor structures simultaneously, was compared with a previous version of the S-TPS-RPM algorithm applied to bladder wall structure alone and with a simultaneous nonweighted S-TPS-RPM registration of the bladder and tumor structures. Performance was assessed in terms of anatomical and geometric accuracy. The anatomical accuracy was calculated as the residual distance error (RDE) of the lipiodol markers and the geometric accuracy was determined by the surface distance, surface coverage, and inverse consistency errors. Optimal parameter values for the flexibility and bladder weight

  6. Control over structure-specific flexibility improves anatomical accuracy for point-based deformable registration in bladder cancer radiotherapy.

    PubMed

    Wognum, S; Bondar, L; Zolnay, A G; Chai, X; Hulshof, M C C M; Hoogeman, M S; Bel, A

    2013-02-01

    Future developments in image guided adaptive radiotherapy (IGART) for bladder cancer require accurate deformable image registration techniques for the precise assessment of tumor and bladder motion and deformation that occur as a result of large bladder volume changes during the course of radiotherapy treatment. The aim was to employ an extended version of a point-based deformable registration algorithm that allows control over tissue-specific flexibility in combination with the authors' unique patient dataset, in order to overcome two major challenges of bladder cancer registration, i.e., the difficulty in accounting for the difference in flexibility between the bladder wall and tumor and the lack of visible anatomical landmarks for validation. The registration algorithm used in the current study is an extension of the symmetric-thin plate splines-robust point matching (S-TPS-RPM) algorithm, a symmetric feature-based registration method. The S-TPS-RPM algorithm has been previously extended to allow control over the degree of flexibility of different structures via a weight parameter. The extended weighted S-TPS-RPM algorithm was tested and validated on CT data (planning- and four to five repeat-CTs) of five urinary bladder cancer patients who received lipiodol injections before radiotherapy. The performance of the weighted S-TPS-RPM method, applied to bladder and tumor structures simultaneously, was compared with a previous version of the S-TPS-RPM algorithm applied to bladder wall structure alone and with a simultaneous nonweighted S-TPS-RPM registration of the bladder and tumor structures. Performance was assessed in terms of anatomical and geometric accuracy. The anatomical accuracy was calculated as the residual distance error (RDE) of the lipiodol markers and the geometric accuracy was determined by the surface distance, surface coverage, and inverse consistency errors. Optimal parameter values for the flexibility and bladder weight parameters were determined

  7. 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

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

    NASA Astrophysics Data System (ADS)

    Lei, Qujiang; Wisse, Martijn

    2017-03-01

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

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

    PubMed Central

    Rosnell, Tomi; Honkavaara, Eija

    2012-01-01

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

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

    PubMed

    Rosnell, Tomi; Honkavaara, Eija

    2012-01-01

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

  11. Lost in Virtual Reality: Pathfinding Algorithms Detect Rock Fractures and Contacts in Point Clouds

    NASA Astrophysics Data System (ADS)

    Thiele, S.; Grose, L.; Micklethwaite, S.

    2016-12-01

    UAV-based photogrammetric and LiDAR techniques provide high resolution 3D point clouds and ortho-rectified photomontages that can capture surface geology in outstanding detail over wide areas. Automated and semi-automated methods are vital to extract full value from these data in practical time periods, though the nuances of geological structures and materials (natural variability in colour and geometry, soft and hard linkage, shadows and multiscale properties) make this a challenging task. We present a novel method for computer assisted trace detection in dense point clouds, using a lowest cost path solver to "follow" fracture traces and lithological contacts between user defined end points. This is achieved by defining a local neighbourhood network where each point in the cloud is linked to its neighbours, and then using a least-cost path algorithm to search this network and estimate the trace of the fracture or contact. A variety of different algorithms can then be applied to calculate the best fit plane, produce a fracture network, or map properties such as roughness, curvature and fracture intensity. Our prototype of this method (Fig. 1) suggests the technique is feasible and remarkably good at following traces under non-optimal conditions such as variable-shadow, partial occlusion and complex fracturing. Furthermore, if a fracture is initially mapped incorrectly, the user can easily provide further guidance by defining intermediate waypoints. Future development will include optimization of the algorithm to perform well on large point clouds and modifications that permit the detection of features such as step-overs. We also plan on implementing this approach in an interactive graphical user environment.

  12. Object recognition and localization from 3D point clouds by maximum-likelihood estimation

    NASA Astrophysics Data System (ADS)

    Dantanarayana, Harshana G.; Huntley, Jonathan M.

    2017-08-01

    We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike `interest point'-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

  13. Extractive biodegradation and bioavailability assessment of phenanthrene in the cloud point system by Sphingomonas polyaromaticivorans.

    PubMed

    Pan, Tao; Deng, Tao; Zeng, Xinying; Dong, Wei; Yu, Shuijing

    2016-01-01

    The biological treatment of polycyclic aromatic hydrocarbons is an important issue. Most microbes have limited practical applications because of the poor bioavailability of polycyclic aromatic hydrocarbons. In this study, the extractive biodegradation of phenanthrene by Sphingomonas polyaromaticivorans was conducted by introducing the cloud point system. The cloud point system is composed of a mixture of (40 g/L) Brij 30 and Tergitol TMN-3, which are nonionic surfactants, in equal proportions. After phenanthrene degradation, a higher wet cell weight and lower phenanthrene residue were obtained in the cloud point system than that in the control system. According to the results of high-performance liquid chromatography, the residual phenanthrene preferred to partition from the dilute phase into the coacervate phase. The concentration of residual phenanthrene in the dilute phase (below 0.001 mg/L) is lower than its solubility in water (1.18 mg/L) after extractive biodegradation. Therefore, dilute phase detoxification was achieved, thus indicating that the dilute phase could be discharged without causing phenanthrene pollution. Bioavailability was assessed by introducing the apparent logP in the cloud point system. Apparent logP decreased significantly, thus indicating that the bioavailability of phenanthrene increased remarkably in the system. This study provides a potential application of biological treatment in water and soil contaminated by phenanthrene.

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

    NASA Astrophysics Data System (ADS)

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

    2017-06-01

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

  15. 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.

  16. Bayesian Multiscale Modeling of Closed Curves in Point Clouds

    PubMed Central

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

    2014-01-01

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

  17. Comparison of Uas-Based Photogrammetry Software for 3d Point Cloud Generation: a Survey Over a Historical Site

    NASA Astrophysics Data System (ADS)

    Alidoost, F.; Arefi, H.

    2017-11-01

    Nowadays, Unmanned Aerial System (UAS)-based photogrammetry offers an affordable, fast and effective approach to real-time acquisition of high resolution geospatial information and automatic 3D modelling of objects for numerous applications such as topography mapping, 3D city modelling, orthophoto generation, and cultural heritages preservation. In this paper, the capability of four different state-of-the-art software packages as 3DSurvey, Agisoft Photoscan, Pix4Dmapper Pro and SURE is examined to generate high density point cloud as well as a Digital Surface Model (DSM) over a historical site. The main steps of this study are including: image acquisition, point cloud generation, and accuracy assessment. The overlapping images are first captured using a quadcopter and next are processed by different software to generate point clouds and DSMs. In order to evaluate the accuracy and quality of point clouds and DSMs, both visual and geometric assessments are carry out and the comparison results are reported.

  18. 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.

  19. Visualization of the Construction of Ancient Roman Buildings in Ostia Using Point Cloud Data

    NASA Astrophysics Data System (ADS)

    Hori, Y.; Ogawa, T.

    2017-02-01

    The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we "skipped" many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

  20. - and Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

    NASA Astrophysics Data System (ADS)

    Xu, Y.; Hoegner, L.; Tuttas, S.; Stilla, U.

    2017-05-01

    Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

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

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

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

  2. Datum Feature Extraction and Deformation Analysis Method Based on Normal Vector of Point Cloud

    NASA Astrophysics Data System (ADS)

    Sun, W.; Wang, J.; Jin, F.; Liang, Z.; Yang, Y.

    2018-04-01

    In order to solve the problem lacking applicable analysis method in the application of three-dimensional laser scanning technology to the field of deformation monitoring, an efficient method extracting datum feature and analysing deformation based on normal vector of point cloud was proposed. Firstly, the kd-tree is used to establish the topological relation. Datum points are detected by tracking the normal vector of point cloud determined by the normal vector of local planar. Then, the cubic B-spline curve fitting is performed on the datum points. Finally, datum elevation and the inclination angle of the radial point are calculated according to the fitted curve and then the deformation information was analyzed. The proposed approach was verified on real large-scale tank data set captured with terrestrial laser scanner in a chemical plant. The results show that the method could obtain the entire information of the monitor object quickly and comprehensively, and reflect accurately the datum feature deformation.

  3. Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics

    DOE PAGES

    Kelbe, David; Oak Ridge National Lab.; van Aardt, Jan; ...

    2016-10-18

    Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in ordermore » to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. Lastly, this paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.« less

  4. 3D granulometry: grain-scale shape and size distribution from point cloud dataset of river environments

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  5. 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.

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

    PubMed

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

    2017-07-28

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

  7. Reconstruction of 3d Models from Point Clouds with Hybrid Representation

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

  8. An automatic markerless registration method for neurosurgical robotics based on an optical camera.

    PubMed

    Meng, Fanle; Zhai, Fangwen; Zeng, Bowei; Ding, Hui; Wang, Guangzhi

    2018-02-01

    Current markerless registration methods for neurosurgical robotics use the facial surface to match the robot space with the image space, and acquisition of the facial surface usually requires manual interaction and constrains the patient to a supine position. To overcome these drawbacks, we propose a registration method that is automatic and does not constrain patient position. An optical camera attached to the robot end effector captures images around the patient's head from multiple views. Then, high coverage of the head surface is reconstructed from the images through multi-view stereo vision. Since the acquired head surface point cloud contains color information, a specific mark that is manually drawn on the patient's head prior to the capture procedure can be extracted to automatically accomplish coarse registration rather than using facial anatomic landmarks. Then, fine registration is achieved by registering the high coverage of the head surface without relying solely on the facial region, thus eliminating patient position constraints. The head surface was acquired by the camera with a good repeatability accuracy. The average target registration error of 8 different patient positions measured with targets inside a head phantom was [Formula: see text], while the mean surface registration error was [Formula: see text]. The method proposed in this paper achieves automatic markerless registration in multiple patient positions and guarantees registration accuracy inside the head. This method provides a new approach for establishing the spatial relationship between the image space and the robot space.

  9. Analysis, Thematic Maps and Data Mining from Point Cloud to Ontology for Software Development

    NASA Astrophysics Data System (ADS)

    Nespeca, R.; De Luca, L.

    2016-06-01

    The primary purpose of the survey for the restoration of Cultural Heritage is the interpretation of the state of building preservation. For this, the advantages of the remote sensing systems that generate dense point cloud (range-based or image-based) are not limited only to the acquired data. The paper shows that it is possible to extrapolate very useful information in diagnostics using spatial annotation, with the use of algorithms already implemented in open-source software. Generally, the drawing of degradation maps is the result of manual work, so dependent on the subjectivity of the operator. This paper describes a method of extraction and visualization of information, obtained by mathematical procedures, quantitative, repeatable and verifiable. The case study is a part of the east facade of the Eglise collégiale Saint-Maurice also called Notre Dame des Grâces, in Caromb, in southern France. The work was conducted on the matrix of information contained in the point cloud asci format. The first result is the extrapolation of new geometric descriptors. First, we create the digital maps with the calculated quantities. Subsequently, we have moved to semi-quantitative analyses that transform new data into useful information. We have written the algorithms for accurate selection, for the segmentation of point cloud, for automatic calculation of the real surface and the volume. Furthermore, we have created the graph of spatial distribution of the descriptors. This work shows that if we work during the data processing we can transform the point cloud into an enriched database: the use, the management and the data mining is easy, fast and effective for everyone involved in the restoration process.

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

    NASA Astrophysics Data System (ADS)

    He, Liu; Yang, Haoxiang; Huang, Yuchun

    2016-10-01

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

  11. TH-AB-202-08: A Robust Real-Time Surface Reconstruction Method On Point Clouds Captured From a 3D Surface Photogrammetry System

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

    Liu, W; Sawant, A; Ruan, D

    2016-06-15

    Purpose: Surface photogrammetry (e.g. VisionRT, C-Rad) provides a noninvasive way to obtain high-frequency measurement for patient motion monitoring in radiotherapy. This work aims to develop a real-time surface reconstruction method on the acquired point clouds, whose acquisitions are subject to noise and missing measurements. In contrast to existing surface reconstruction methods that are usually computationally expensive, the proposed method reconstructs continuous surfaces with comparable accuracy in real-time. Methods: The key idea in our method is to solve and propagate a sparse linear relationship from the point cloud (measurement) manifold to the surface (reconstruction) manifold, taking advantage of the similarity inmore » local geometric topology in both manifolds. With consistent point cloud acquisition, we propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, building the point correspondences by the iterative closest point (ICP) method. To accommodate changing noise levels and/or presence of inconsistent occlusions, we further propose a modified sparse regression (MSR) model to account for the large and sparse error built by ICP, with a Laplacian prior. We evaluated our method on both clinical acquired point clouds under consistent conditions and simulated point clouds with inconsistent occlusions. The reconstruction accuracy was evaluated w.r.t. root-mean-squared-error, by comparing the reconstructed surfaces against those from the variational reconstruction method. Results: On clinical point clouds, both the SR and MSR models achieved sub-millimeter accuracy, with mean reconstruction time reduced from 82.23 seconds to 0.52 seconds and 0.94 seconds, respectively. On simulated point cloud with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent performance despite the introduced occlusions. Conclusion: We have developed a

  12. Nanosatellite Maneuver Planning for Point Cloud Generation With a Rangefinder

    DTIC Science & Technology

    2015-06-05

    aided active vision systems [11], dense stereo [12], and TriDAR [13]. However, these systems are unsuitable for a nanosatellite system from power, size...command profiles as well as improving the fidelity of gap detection with better filtering methods for background objects . For example, attitude...application of a single beam laser rangefinder (LRF) to point cloud generation, shape detection , and shape reconstruction for a space-based space

  13. New Perspectives of Point Clouds Color Management - the Development of Tool in Matlab for Applications in Cultural Heritage

    NASA Astrophysics Data System (ADS)

    Pepe, M.; Ackermann, S.; Fregonese, L.; Achille, C.

    2017-02-01

    The paper describes a method for Point Clouds Color management and Integration obtained from Terrestrial Laser Scanner (TLS) and Image Based (IB) survey techniques. Especially in the Cultural Heritage (CH) environment, methods and techniques to improve the color quality of Point Clouds have a key role because a homogenous texture brings to a more accurate reconstruction of the investigated object and to a more pleasant perception of the color object as well. A color management method for point clouds can be useful in case of single data set acquired by TLS or IB technique as well as in case of chromatic heterogeneity resulting by merging different datasets. The latter condition can occur when the scans are acquired in different moments of the same day or when scans of the same object are performed in a period of weeks or months, and consequently with a different environment/lighting condition. In this paper, a procedure to balance the point cloud color in order to uniform the different data sets, to improve the chromatic quality and to highlight further details will be presented and discussed.

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

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

  15. Sci—Thur AM: YIS - 11: Estimation of Bladder-Wall Cumulative Dose in Multi-Fraction Image-Based Gynaecological Brachytherapy Using Deformable Point Set Registration

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

    Zakariaee, R; Brown, C J; Hamarneh, G

    2014-08-15

    Dosimetric parameters based on dose-volume histograms (DVH) of contoured structures are routinely used to evaluate dose delivered to target structures and organs at risk. However, the DVH provides no information on the spatial distribution of the dose in situations of repeated fractions with changes in organ shape or size. The aim of this research was to develop methods to more accurately determine geometrically localized, cumulative dose to the bladder wall in intracavitary brachytherapy for cervical cancer. The CT scans and treatment plans of 20 cervical cancer patients were used. Each patient was treated with five high-dose-rate (HDR) brachytherapy fractions ofmore » 600cGy prescribed dose. The bladder inner and outer surfaces were delineated using MIM Maestro software (MIM Software Inc.) and were imported into MATLAB (MathWorks) as 3-dimensional point clouds constituting the “bladder wall”. A point-set registration toolbox for MATLAB, Coherent Point Drift (CPD), was used to non-rigidly transform the bladder-wall points from four of the fractions to the coordinate system of the remaining (reference) fraction, which was chosen to be the emptiest bladder for each patient. The doses were accumulated on the reference fraction and new cumulative dosimetric parameters were calculated. The LENT-SOMA toxicity scores of these patients were studied against the cumulative dose parameters. Based on this study, there was no significant correlation between the toxicity scores and the determined cumulative dose parameters.« less

  16. Impact of Surface Active Ionic Liquids on the Cloud Points of Nonionic Surfactants and the Formation of Aqueous Micellar Two-Phase Systems.

    PubMed

    Vicente, Filipa A; Cardoso, Inês S; Sintra, Tânia E; Lemus, Jesus; Marques, Eduardo F; Ventura, Sónia P M; Coutinho, João A P

    2017-09-21

    Aqueous micellar two-phase systems (AMTPS) hold a large potential for cloud point extraction of biomolecules but are yet poorly studied and characterized, with few phase diagrams reported for these systems, hence limiting their use in extraction processes. This work reports a systematic investigation of the effect of different surface-active ionic liquids (SAILs)-covering a wide range of molecular properties-upon the clouding behavior of three nonionic Tergitol surfactants. Two different effects of the SAILs on the cloud points and mixed micelle size have been observed: ILs with a more hydrophilic character and lower critical packing parameter (CPP < 1 / 2 ) lead to the formation of smaller micelles and concomitantly increase the cloud points; in contrast, ILs with a more hydrophobic character and higher CPP (CPP ≥ 1) induce significant micellar growth and a decrease in the cloud points. The latter effect is particularly interesting and unusual for it was accepted that cloud point reduction is only induced by inorganic salts. The effects of nonionic surfactant concentration, SAIL concentration, pH, and micelle ζ potential are also studied and rationalized.

  17. a Weighted Closed-Form Solution for Rgb-D Data Registration

    NASA Astrophysics Data System (ADS)

    Vestena, K. M.; Dos Santos, D. R.; Oilveira, E. M., Jr.; Pavan, N. L.; Khoshelham, K.

    2016-06-01

    Existing 3D indoor mapping of RGB-D data are prominently point-based and feature-based methods. In most cases iterative closest point (ICP) and its variants are generally used for pairwise registration process. Considering that the ICP algorithm requires an relatively accurate initial transformation and high overlap a weighted closed-form solution for RGB-D data registration is proposed. In this solution, we weighted and normalized the 3D points based on the theoretical random errors and the dual-number quaternions are used to represent the 3D rigid body motion. Basically, dual-number quaternions provide a closed-form solution by minimizing a cost function. The most important advantage of the closed-form solution is that it provides the optimal transformation in one-step, it does not need to calculate good initial estimates and expressively decreases the demand for computer resources in contrast to the iterative method. Basically, first our method exploits RGB information. We employed a scale invariant feature transformation (SIFT) for extracting, detecting, and matching features. It is able to detect and describe local features that are invariant to scaling and rotation. To detect and filter outliers, we used random sample consensus (RANSAC) algorithm, jointly with an statistical dispersion called interquartile range (IQR). After, a new RGB-D loop-closure solution is implemented based on the volumetric information between pair of point clouds and the dispersion of the random errors. The loop-closure consists to recognize when the sensor revisits some region. Finally, a globally consistent map is created to minimize the registration errors via a graph-based optimization. The effectiveness of the proposed method is demonstrated with a Kinect dataset. The experimental results show that the proposed method can properly map the indoor environment with an absolute accuracy around 1.5% of the travel of a trajectory.

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  19. Comparison of roadway roughness derived from LIDAR and SFM 3D point clouds.

    DOT National Transportation Integrated Search

    2015-10-01

    This report describes a short-term study undertaken to investigate the potential for using dense three-dimensional (3D) point : clouds generated from light detection and ranging (LIDAR) and photogrammetry to assess roadway roughness. Spatially : cont...

  20. Deformable Image Registration for Cone-Beam CT Guided Transoral Robotic Base of Tongue Surgery

    PubMed Central

    Reaungamornrat, S.; Liu, W. P.; Wang, A. S.; Otake, Y.; Nithiananthan, S.; Uneri, A.; Schafer, S.; Tryggestad, E.; Richmon, J.; Sorger, J. M.; Siewerdsen, J. H.; Taylor, R. H.

    2013-01-01

    Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base of tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam CT (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e., volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC), and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid, and Demons steps was 4.6, 2.1, and 1.7 mm, respectively. The respective ECC was 0.57, 0.70, and 0.73 and NPMI was 0.46, 0.57, and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support

  1. Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

    NASA Astrophysics Data System (ADS)

    Reaungamornrat, S.; Liu, W. P.; Wang, A. S.; Otake, Y.; Nithiananthan, S.; Uneri, A.; Schafer, S.; Tryggestad, E.; Richmon, J.; Sorger, J. M.; Siewerdsen, J. H.; Taylor, R. H.

    2013-07-01

    Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base-of-tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam computed tomography (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e. volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC) and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid and Demons steps was 4.6, 2.1 and 1.7 mm, respectively. The respective ECC was 0.57, 0.70 and 0.73, and NPMI was 0.46, 0.57 and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to

  2. Cloud Point and Liquid-Liquid Equilibrium Behavior of Thermosensitive Polymer L61 and Salt Aqueous Two-Phase System.

    PubMed

    Rao, Wenwei; Wang, Yun; Han, Juan; Wang, Lei; Chen, Tong; Liu, Yan; Ni, Liang

    2015-06-25

    The cloud point of thermosensitive triblock polymer L61, poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO), was determined in the presence of various electrolytes (K2HPO4, (NH4)3C6H5O7, and K3C6H5O7). The cloud point of L61 was lowered by the addition of electrolytes, and the cloud point of L61 decreased linearly with increasing electrolyte concentration. The efficacy of electrolytes on reducing cloud point followed the order: K3C6H5O7 > (NH4)3C6H5O7 > K2HPO4. With the increase in salt concentration, aqueous two-phase systems exhibited a phase inversion. In addition, increasing the temperature reduced the concentration of salt needed that could promote phase inversion. The phase diagrams and liquid-liquid equilibrium data of the L61-K2HPO4/(NH4)3C6H5O7/K3C6H5O7 aqueous two-phase systems (before the phase inversion but also after phase inversion) were determined at T = (25, 30, and 35) °C. Phase diagrams of aqueous two-phase systems were fitted to a four-parameter empirical nonlinear expression. Moreover, the slopes of the tie-lines and the area of two-phase region in the diagram have a tendency to rise with increasing temperature. The capacity of different salts to induce aqueous two-phase system formation was the same order as the ability of salts to reduce the cloud point.

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

    NASA Astrophysics Data System (ADS)

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

    2017-11-01

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

  4. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2017-12-01

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

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

    PubMed

    Inoue, Tohru; Misono, Takeshi

    2008-10-15

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

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

  8. Public Participation Process for Registration Actions

    EPA Pesticide Factsheets

    Describes the process for registration actions which provides the opportunity for the public to comment on major registration decisions at a point in the registration process when comprehensive information and analysis are available.

  9. Automated estimation of leaf distribution for individual trees based on TLS point clouds

    NASA Astrophysics Data System (ADS)

    Koma, Zsófia; Rutzinger, Martin; Bremer, Magnus

    2017-04-01

    Light Detection and Ranging (LiDAR) especially the ground based LiDAR (Terrestrial Laser Scanning - TLS) is an operational used and widely available measurement tool supporting forest inventory updating and research in forest ecology. High resolution point clouds from TLS already represent single leaves which can be used for a more precise estimation of Leaf Area Index (LAI) and for higher accurate biomass estimation. However, currently the methodology for extracting single leafs from the unclassified point clouds for individual trees is still missing. The aim of this study is to present a novel segmentation approach in order to extract single leaves and derive features related to leaf morphology (such as area, slope, length and width) of each single leaf from TLS point cloud data. For the study two exemplary single trees were scanned in leaf-on condition on the university campus of Innsbruck during calm wind conditions. A northern red oak (Quercus rubra) was scanned by a discrete return recording Optech ILRIS-3D TLS scanner and a tulip tree (Liliodendron tulpifera) with Riegl VZ-6000 scanner. During the scanning campaign a reference dataset was measured parallel to scanning. In this case 230 leaves were randomly collected around the lower branches of the tree and photos were taken. The developed workflow steps were the following: in the first step normal vectors and eigenvalues were calculated based on the user specified neighborhood. Then using the direction of the largest eigenvalue outliers i.e. ghost points were removed. After that region growing segmentation based on the curvature and angles between normal vectors was applied on the filtered point cloud. On each segment a RANSAC plane fitting algorithm was applied in order to extract the segment based normal vectors. Using the related features of the calculated segments the stem and branches were labeled as non-leaf and other segments were classified as leaf. The validation of the different segmentation

  10. Automatic extraction of pavement markings on streets from point cloud data of mobile LiDAR

    NASA Astrophysics Data System (ADS)

    Gao, Yang; Zhong, Ruofei; Tang, Tao; Wang, Liuzhao; Liu, Xianlin

    2017-08-01

    Pavement markings provide an important foundation as they help to keep roads users safe. Accurate and comprehensive information about pavement markings assists the road regulators and is useful in developing driverless technology. Mobile light detection and ranging (LiDAR) systems offer new opportunities to collect and process accurate pavement markings’ information. Mobile LiDAR systems can directly obtain the three-dimensional (3D) coordinates of an object, thus defining spatial data and the intensity of (3D) objects in a fast and efficient way. The RGB attribute information of data points can be obtained based on the panoramic camera in the system. In this paper, we present a novel method process to automatically extract pavement markings using multiple attribute information of the laser scanning point cloud from the mobile LiDAR data. This method process utilizes a differential grayscale of RGB color, laser pulse reflection intensity, and the differential intensity to identify and extract pavement markings. We utilized point cloud density to remove the noise and used morphological operations to eliminate the errors. In the application, we tested our method process on different sections of roads in Beijing, China, and Buffalo, NY, USA. The results indicated that both correctness (p) and completeness (r) were higher than 90%. The method process of this research can be applied to extract pavement markings from huge point cloud data produced by mobile LiDAR.

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

    NASA Astrophysics Data System (ADS)

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

    2017-05-01

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

  12. SigVox - A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds

    NASA Astrophysics Data System (ADS)

    Wang, Jinhu; Lindenbergh, Roderik; Menenti, Massimo

    2017-06-01

    Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate

  13. Data Products on Cloud

    NASA Technical Reports Server (NTRS)

    Ly, Vuong T.; Mandl, Daniel J.

    2014-01-01

    This presentation lays out the data processing products that exist and are planned for the Matsu cloud for Earth Observing 1. The presentation focuses on a new feature called co-registration of Earth Observing 1 with Landsat Global Land Survey chips.

  14. Displacement fields from point cloud data: Application of particle imaging velocimetry to landslide geodesy

    USGS Publications Warehouse

    Aryal, Arjun; Brooks, Benjamin A.; Reid, Mark E.; Bawden, Gerald W.; Pawlak, Geno

    2012-01-01

    Acquiring spatially continuous ground-surface displacement fields from Terrestrial Laser Scanners (TLS) will allow better understanding of the physical processes governing landslide motion at detailed spatial and temporal scales. Problems arise, however, when estimating continuous displacement fields from TLS point-clouds because reflecting points from sequential scans of moving ground are not defined uniquely, thus repeat TLS surveys typically do not track individual reflectors. Here, we implemented the cross-correlation-based Particle Image Velocimetry (PIV) method to derive a surface deformation field using TLS point-cloud data. We estimated associated errors using the shape of the cross-correlation function and tested the method's performance with synthetic displacements applied to a TLS point cloud. We applied the method to the toe of the episodically active Cleveland Corral Landslide in northern California using TLS data acquired in June 2005–January 2007 and January–May 2010. Estimated displacements ranged from decimeters to several meters and they agreed well with independent measurements at better than 9% root mean squared (RMS) error. For each of the time periods, the method provided a smooth, nearly continuous displacement field that coincides with independently mapped boundaries of the slide and permits further kinematic and mechanical inference. For the 2010 data set, for instance, the PIV-derived displacement field identified a diffuse zone of displacement that preceded by over a month the development of a new lateral shear zone. Additionally, the upslope and downslope displacement gradients delineated by the dense PIV field elucidated the non-rigid behavior of the slide.

  15. 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.

  16. Point Cloud Analysis for Conservation and Enhancement of Modernist Architecture

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

  17. A Comparative Study of Point Cloud Data Collection and Processing

    NASA Astrophysics Data System (ADS)

    Pippin, J. E.; Matheney, M.; Gentle, J. N., Jr.; Pierce, S. A.; Fuentes-Pineda, G.

    2016-12-01

    Over the past decade, there has been dramatic growth in the acquisition of publicly funded high-resolution topographic data for scientific, environmental, engineering and planning purposes. These data sets are valuable for applications of interest across a large and varied user community. However, because of the large volumes of data produced by high-resolution mapping technologies and expense of aerial data collection, it is often difficult to collect and distribute these datasets. Furthermore, the data can be technically challenging to process, requiring software and computing resources not readily available to many users. This study presents a comparison of advanced computing hardware and software that is used to collect and process point cloud datasets, such as LIDAR scans. Activities included implementation and testing of open source libraries and applications for point cloud data processing such as, Meshlab, Blender, PDAL, and PCL. Additionally, a suite of commercial scale applications, Skanect and Cloudcompare, were applied to raw datasets. Handheld hardware solutions, a Structure Scanner and Xbox 360 Kinect V1, were tested for their ability to scan at three field locations. The resultant data projects successfully scanned and processed subsurface karst features ranging from small stalactites to large rooms, as well as a surface waterfall feature. Outcomes support the feasibility of rapid sensing in 3D at field scales.

  18. Automatic initialization for 3D bone registration

    NASA Astrophysics Data System (ADS)

    Foroughi, Pezhman; Taylor, Russell H.; Fichtinger, Gabor

    2008-03-01

    In image-guided bone surgery, sample points collected from the surface of the bone are registered to the preoperative CT model using well-known registration methods such as Iterative Closest Point (ICP). These techniques are generally very sensitive to the initial alignment of the datasets. Poor initialization significantly increases the chances of getting trapped local minima. In order to reduce the risk of local minima, the registration is manually initialized by locating the sample points close to the corresponding points on the CT model. In this paper, we present an automatic initialization method that aligns the sample points collected from the surface of pelvis with CT model of the pelvis. The main idea is to exploit a mean shape of pelvis created from a large number of CT scans as the prior knowledge to guide the initial alignment. The mean shape is constant for all registrations and facilitates the inclusion of application-specific information into the registration process. The CT model is first aligned with the mean shape using the bilateral symmetry of the pelvis and the similarity of multiple projections. The surface points collected using ultrasound are then aligned with the pelvis mean shape. This will, in turn, lead to initial alignment of the sample points with the CT model. The experiments using a dry pelvis and two cadavers show that the method can align the randomly dislocated datasets close enough for successful registration. The standard ICP has been used for final registration of datasets.

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

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  20. RPBS: Rotational Projected Binary Structure for point cloud representation

    NASA Astrophysics Data System (ADS)

    Fang, Bin; Zhou, Zhiwei; Ma, Tao; Hu, Fangyu; Quan, Siwen; Ma, Jie

    2018-03-01

    In this paper, we proposed a novel three-dimension local surface descriptor named RPBS for point cloud representation. First, points cropped form the query point within a predefined radius is regard as a local surface patch. Then pose normalization is done to the local surface to equip our descriptor with the invariance to rotation transformation. To obtain more information about the cropped surface, multi-view representation is formed by successively rotating it along the coordinate axis. Further, orthogonal projections to the three coordinate plane are adopted to construct two-dimension distribution matrixes, and binarization is applied to each matrix by following the rule that whether the grid is occupied, if yes, set the grid one, otherwise zero. We calculate the binary maps from all the viewpoints and concatenate them together as the final descriptor. Comparative experiments for evaluating our proposed descriptor is conducted on the standard dataset named Bologna with several state-of-the-art 3D descriptors, and results show that our descriptor achieves the best performance on feature matching experiments.

  1. Nonrigid iterative closest points for registration of 3D biomedical surfaces

    NASA Astrophysics Data System (ADS)

    Liang, Luming; Wei, Mingqiang; Szymczak, Andrzej; Petrella, Anthony; Xie, Haoran; Qin, Jing; Wang, Jun; Wang, Fu Lee

    2018-01-01

    Advanced 3D optical and laser scanners bring new challenges to computer graphics. We present a novel nonrigid surface registration algorithm based on Iterative Closest Point (ICP) method with multiple correspondences. Our method, called the Nonrigid Iterative Closest Points (NICPs), can be applied to surfaces of arbitrary topology. It does not impose any restrictions on the deformation, e.g. rigidity or articulation. Finally, it does not require parametrization of input meshes. Our method is based on an objective function that combines distance and regularization terms. Unlike the standard ICP, the distance term is determined based on multiple two-way correspondences rather than single one-way correspondences between surfaces. A Laplacian-based regularization term is proposed to take full advantage of multiple two-way correspondences. This term regularizes the surface movement by enforcing vertices to move coherently with their 1-ring neighbors. The proposed method achieves good performances when no global pose differences or significant amount of bending exists in the models, for example, families of similar shapes, like human femur and vertebrae models.

  2. Automatic registration of optical imagery with 3d lidar data using local combined mutual information

    NASA Astrophysics Data System (ADS)

    Parmehr, E. G.; Fraser, C. S.; Zhang, C.; Leach, J.

    2013-10-01

    Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.

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

    NASA Astrophysics Data System (ADS)

    Gong, K.; Fritsch, D.

    2018-05-01

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

  4. Extractive biodecolorization of triphenylmethane dyes in cloud point system by Aeromonas hydrophila DN322p.

    PubMed

    Pan, Tao; Ren, Suizhou; Xu, Meiying; Sun, Guoping; Guo, Jun

    2013-07-01

    The biological treatment of triphenylmethane dyes is an important issue. Most microbes have limited practical application because they cannot completely detoxicate these dyes. In this study, the extractive biodecolorization of triphenylmethane dyes by Aeromonas hydrophila DN322p was carried out by introducing the cloud point system. The cloud point system is composed of a mixture of nonionic surfactants (20 g/L) Brij 30 and Tergitol TMN-3 in equal proportions. After the decolorization of crystal violet, a higher wet cell weight was obtained in the cloud point system than that of the control system. Based on the results of thin-layer chromatography, the residual crystal violet and its decolorized product, leuco crystal violet, preferred to partition into the coacervate phase. Therefore, the detoxification of the dilute phase was achieved, which indicated that the dilute phase could be discharged without causing dye pollution. The extractive biodecolorization of three other triphenylmethane dyes was also examined in this system. The decolorization of malachite green and brilliant green was similar to that of crystal violet. Only ethyl violet achieved a poor decolorization rate because DN322p decolorized it via adsorption but did not convert it into its leuco form. This study provides potential application of biological treatment in triphenylmethane dye wastewater.

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

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

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

  7. 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.

  8. Skull registration for prone patient position using tracked ultrasound

    NASA Astrophysics Data System (ADS)

    Underwood, Grace; Ungi, Tamas; Baum, Zachary; Lasso, Andras; Kronreif, Gernot; Fichtinger, Gabor

    2017-03-01

    PURPOSE: Tracked navigation has become prevalent in neurosurgery. Problems with registration of a patient and a preoperative image arise when the patient is in a prone position. Surfaces accessible to optical tracking on the back of the head are unreliable for registration. We investigated the accuracy of surface-based registration using points accessible through tracked ultrasound. Using ultrasound allows access to bone surfaces that are not available through optical tracking. Tracked ultrasound could eliminate the need to work (i) under the table for registration and (ii) adjust the tracker between surgery and registration. In addition, tracked ultrasound could provide a non-invasive method in comparison to an alternative method of registration involving screw implantation. METHODS: A phantom study was performed to test the feasibility of tracked ultrasound for registration. An initial registration was performed to partially align the pre-operative computer tomography data and skull phantom. The initial registration was performed by an anatomical landmark registration. Surface points accessible by tracked ultrasound were collected and used to perform an Iterative Closest Point Algorithm. RESULTS: When the surface registration was compared to a ground truth landmark registration, the average TRE was found to be 1.6+/-0.1mm and the average distance of points off the skull surface was 0.6+/-0.1mm. CONCLUSION: The use of tracked ultrasound is feasible for registration of patients in prone position and eliminates the need to perform registration under the table. The translational component of error found was minimal. Therefore, the amount of TRE in registration is due to a rotational component of error.

  9. Space Subdivision in Indoor Mobile Laser Scanning Point Clouds Based on Scanline Analysis.

    PubMed

    Zheng, Yi; Peter, Michael; Zhong, Ruofei; Oude Elberink, Sander; Zhou, Quan

    2018-06-05

    Indoor space subdivision is an important aspect of scene analysis that provides essential information for many applications, such as indoor navigation and evacuation route planning. Until now, most proposed scene understanding algorithms have been based on whole point clouds, which has led to complicated operations, high computational loads and low processing speed. This paper presents novel methods to efficiently extract the location of openings (e.g., doors and windows) and to subdivide space by analyzing scanlines. An opening detection method is demonstrated that analyses the local geometric regularity in scanlines to refine the extracted opening. Moreover, a space subdivision method based on the extracted openings and the scanning system trajectory is described. Finally, the opening detection and space subdivision results are saved as point cloud labels which will be used for further investigations. The method has been tested on a real dataset collected by ZEB-REVO. The experimental results validate the completeness and correctness of the proposed method for different indoor environment and scanning paths.

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

    NASA Astrophysics Data System (ADS)

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

    2011-09-01

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

  11. Cloud point extraction: an alternative to traditional liquid-liquid extraction for lanthanides(III) separation.

    PubMed

    Favre-Réguillon, Alain; Draye, Micheline; Lebuzit, Gérard; Thomas, Sylvie; Foos, Jacques; Cote, Gérard; Guy, Alain

    2004-06-17

    Cloud point extraction (CPE) was used to extract and separate lanthanum(III) and gadolinium(III) nitrate from an aqueous solution. The methodology used is based on the formation of lanthanide(III)-8-hydroxyquinoline (8-HQ) complexes soluble in a micellar phase of non-ionic surfactant. The lanthanide(III) complexes are then extracted into the surfactant-rich phase at a temperature above the cloud point temperature (CPT). The structure of the non-ionic surfactant, and the chelating agent-metal molar ratio are identified as factors determining the extraction efficiency and selectivity. In an aqueous solution containing equimolar concentrations of La(III) and Gd(III), extraction efficiency for Gd(III) can reach 96% with a Gd(III)/La(III) selectivity higher than 30 using Triton X-114. Under those conditions, a Gd(III) decontamination factor of 50 is obtained.

  12. The potential of cloud point system as a novel two-phase partitioning system for biotransformation.

    PubMed

    Wang, Zhilong

    2007-05-01

    Although the extractive biotransformation in two-phase partitioning systems have been studied extensively, such as the water-organic solvent two-phase system, the aqueous two-phase system, the reverse micelle system, and the room temperature ionic liquid, etc., this has not yet resulted in a widespread industrial application. Based on the discussion of the main obstacles, an exploitation of a cloud point system, which has already been applied in a separation field known as a cloud point extraction, as a novel two-phase partitioning system for biotransformation, is reviewed by analysis of some topical examples. At the end of the review, the process control and downstream processing in the application of the novel two-phase partitioning system for biotransformation are also briefly discussed.

  13. Layer stacking: A novel algorithm for individual forest tree segmentation from LiDAR point clouds

    Treesearch

    Elias Ayrey; Shawn Fraver; John A. Kershaw; Laura S. Kenefic; Daniel Hayes; Aaron R. Weiskittel; Brian E. Roth

    2017-01-01

    As light detection and ranging (LiDAR) technology advances, it has become common for datasets to be acquired at a point density high enough to capture structural information from individual trees. To process these data, an automatic method of isolating individual trees from a LiDAR point cloud is required. Traditional methods for segmenting trees attempt to isolate...

  14. A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images

    NASA Astrophysics Data System (ADS)

    Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K.; Yashar, Catheryn M.; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

    2015-04-01

    Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based ‘thin-plate-spline robust point matching’ algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

  15. A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images.

    PubMed

    Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K; Yashar, Catheryn M; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

    2015-04-07

    Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based 'thin-plate-spline robust point matching' algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

  16. Extracting Topological Relations Between Indoor Spaces from Point Clouds

    NASA Astrophysics Data System (ADS)

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

    2017-09-01

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

  17. 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.

  18. 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

  19. Evaluation of various deformable image registrations for point and volume variations

    NASA Astrophysics Data System (ADS)

    Han, Su Chul; Lee, Soon Sung; Kim, Mi-Sook; Ji, Young Hoon; Kim, Kum Bae; Choi, Sang Hyun; Park, Seungwoo; Jung, Haijo; Yoo, Hyung Jun; Yi, Chul Young

    2015-07-01

    The accuracy of deformable image registration (DIR) has a significant dosimetric impact in radiationtreatment planning. Many groups have studied the accuracy of DIR. In this study, we evaluatedthe accuracy of various DIR algorithms by using variations of the deformation point and volume.The reference image (I ref ) and volume (V ref ) were first generated by using virtual deformation QAsoftware (ImSimQA, Oncology System Limited, UK). We deformed I ref with axial movement of thedeformation point and V ref , depending on the type of deformation (relaxation and contraction) inImSimQA software. The deformed image (I def ) and volume (V def ) acquired by using the ImSimQAsoftware were inversely deformed relative to I ref and V ref by using DIR algorithms. As a result,we acquired a deformed image (I id ) from I def and volume (V id ) from V ref . Four intensity-basedalgorithms were tested by following the horn-schunk optical flow (HS), iterative optical flow (IOF),modified demons (MD) and fast demons (FD) with the Deformable Image Registration and AdaptiveRadiotherapy Toolkit (DIRART) of MATLAB. The image similarity between I ref and I id wascalculated to evaluate the accuracy of DIR algorithms using by Normalized Mutual Information(NMI) and Normalized Cross Correlation (NCC) metrics, when the distance of point deformationwas moved 4 mm, the value of NMI was above 1.81 and that of NCC was above 0.99 in all DIRalgorithms. As the degree of deformation was increased, the degree of image similarity decreased.When the V ref was increased or decreased by about 12%, the difference between V ref and V id waswithin ±5% regardless of the type of deformation, the deformation was classified into two types:deformation 1 increased the V ref (relaxation) and deformation 2 decreased the V ref (contraction).The value of the Dice Similarity Coefficient (DSC) was above 0.95 in deformation 1 except for theMD algorithm. In the case of deformation 2, the value of the DSC was above 0.95 in

  20. MO-C-17A-11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images

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

    Zhen, X; Chen, H; Zhou, L

    2014-06-15

    Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the randommore » walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866

  1. Multiple window spatial registration error of a gamma camera: 133Ba point source as a replacement of the NEMA procedure.

    PubMed

    Bergmann, Helmar; Minear, Gregory; Raith, Maria; Schaffarich, Peter M

    2008-12-09

    The accuracy of multiple window spatial resolution characterises the performance of a gamma camera for dual isotope imaging. In the present study we investigate an alternative method to the standard NEMA procedure for measuring this performance parameter. A long-lived 133Ba point source with gamma energies close to 67Ga and a single bore lead collimator were used to measure the multiple window spatial registration error. Calculation of the positions of the point source in the images used the NEMA algorithm. The results were validated against the values obtained by the standard NEMA procedure which uses a liquid 67Ga source with collimation. Of the source-collimator configurations under investigation an optimum collimator geometry, consisting of a 5 mm thick lead disk with a diameter of 46 mm and a 5 mm central bore, was selected. The multiple window spatial registration errors obtained by the 133Ba method showed excellent reproducibility (standard deviation < 0.07 mm). The values were compared with the results from the NEMA procedure obtained at the same locations and showed small differences with a correlation coefficient of 0.51 (p < 0.05). In addition, the 133Ba point source method proved to be much easier to use. A Bland-Altman analysis showed that the 133Ba and the 67Ga Method can be used interchangeably. The 133Ba point source method measures the multiple window spatial registration error with essentially the same accuracy as the NEMA-recommended procedure, but is easier and safer to use and has the potential to replace the current standard procedure.

  2. What are the essential competencies required of a midwife at the point of registration?

    PubMed

    Butler, Michelle M; Fraser, Diane M; Murphy, Roger J L

    2008-09-01

    to identify the essential competencies required of a midwife at the point of registration. qualitative, descriptive, extended case study and depth interviews. pre-registration midwifery education in England. 39 qualifying midwives, their assessors, midwives and midwife teachers across six higher education institutions, and 20 experienced midwives at two sites. essential competencies were identified relating to (1) being a safe practitioner; (2) having the right attitude; and (3) being an effective communicator. In order to be a safe practitioner, it was proposed that a midwife must have a reasonable degree of self-sufficiency, use up-to-date knowledge in practice, and have self and professional awareness. It was suggested that having the right attitude involves being motivated, being committed to midwifery and being caring and kind. Participants highlighted the importance of effective communication so that midwives can relate to and work in partnership with women and provide truly informed choice. Essential communication skills include active listening, providing appropriate information and flexibility. the most important requirement at registration is that a midwife is safe and will practise safely. However, this capability to be safe is further mediated by attitudes and communication skills. models of midwifery competence should always include personal attributes and effective communication in addition to the competencies required to be able to practise safely, and there should be an explicit focus in curriculum content, skills training and assessment on attitudes and communication.

  3. Extracting cross sections and water levels of vegetated ditches from LiDAR point clouds

    NASA Astrophysics Data System (ADS)

    Roelens, Jennifer; Dondeyne, Stefaan; Van Orshoven, Jos; Diels, Jan

    2016-12-01

    The hydrologic response of a catchment is sensitive to the morphology of the drainage network. Dimensions of bigger channels are usually well known, however, geometrical data for man-made ditches is often missing as there are many and small. Aerial LiDAR data offers the possibility to extract these small geometrical features. Analysing the three-dimensional point clouds directly will maintain the highest degree of information. A longitudinal and cross-sectional buffer were used to extract the cross-sectional profile points from the LiDAR point cloud. The profile was represented by spline functions fitted through the minimum envelop of the extracted points. The cross-sectional ditch profiles were classified for the presence of water and vegetation based on the normalized difference water index and the spatial characteristics of the points along the profile. The normalized difference water index was created using the RGB and intensity data coupled to the LiDAR points. The mean vertical deviation of 0.14 m found between the extracted and reference cross sections could mainly be attributed to the occurrence of water and partly to vegetation on the banks. In contrast to the cross-sectional area, the extracted width was not influenced by the environment (coefficient of determination R2 = 0.87). Water and vegetation influenced the extracted ditch characteristics, but the proposed method is still robust and therefore facilitates input data acquisition and improves accuracy of spatially explicit hydrological models.

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

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

  5. Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

    NASA Technical Reports Server (NTRS)

    Solarna, David; Moser, Gabriele; Le Moigne-Stewart, Jacqueline; Serpico, Sebastiano B.

    2017-01-01

    Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

  6. Investigation of TM Band-to-band Registration Using the JSC Registration Processor

    NASA Technical Reports Server (NTRS)

    Yao, S. S.; Amis, M. L.

    1984-01-01

    The JSC registration processor performs scene-to-scene (or band-to-band) correlation based on edge images. The edge images are derived from a percentage of the edge pixels calculated from the raw scene data, excluding clouds and other extraneous data in the scene. Correlations are performed on patches (blocks) of the edge images, and the correlation peak location in each patch is estimated iteratively to fractional pixel location accuracy. Peak offset locations from all patches over the scene are then considered together, and a variety of tests are made to weed out outliers and other inconsistencies before a distortion model is assumed. Thus, the correlation peak offset locations in each patch indicate quantitatively how well the two TM bands register to each other over that patch of scene data. The average of these offsets indicate the overall accuracies of the band-to-band registration. The registration processor was also used to register one acquisition to another acquisition of multitemporal TM data acquired over the same ground track. Band 4 images from both acquisitions were correlated and an rms error of a fraction of a pixel was routinely obtained.

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

  8. Addressing scale dependence in roughness and morphometric statistics derived from point cloud data.

    NASA Astrophysics Data System (ADS)

    Buscombe, D.; Wheaton, J. M.; Hensleigh, J.; Grams, P. E.; Welcker, C. W.; Anderson, K.; Kaplinski, M. A.

    2015-12-01

    The heights of natural surfaces can be measured with such spatial density that almost the entire spectrum of physical roughness scales can be characterized, down to the morphological form and grain scales. With an ability to measure 'microtopography' comes a demand for analytical/computational tools for spatially explicit statistical characterization of surface roughness. Detrended standard deviation of surface heights is a popular means to create continuous maps of roughness from point cloud data, using moving windows and reporting window-centered statistics of variations from a trend surface. If 'roughness' is the statistical variation in the distribution of relief of a surface, then 'texture' is the frequency of change and spatial arrangement of roughness. The variance in surface height as a function of frequency obeys a power law. In consequence, roughness is dependent on the window size through which it is examined, which has a number of potential disadvantages: 1) the choice of window size becomes crucial, and obstructs comparisons between data; 2) if windows are large relative to multiple roughness scales, it is harder to discriminate between those scales; 3) if roughness is not scaled by the texture length scale, information on the spacing and clustering of roughness `elements' can be lost; and 4) such practice is not amenable to models describing the scattering of light and sound from rough natural surfaces. We discuss the relationship between roughness and texture. Some useful parameters which scale vertical roughness to characteristic horizontal length scales are suggested, with examples of bathymetric point clouds obtained using multibeam from two contrasting riverbeds, namely those of the Colorado River in Grand Canyon, and the Snake River in Hells Canyon. Such work, aside from automated texture characterization and texture segmentation, roughness and grain size calculation, might also be useful for feature detection and classification from point

  9. The Complex Point Cloud for the Knowledge of the Architectural Heritage. Some Experiences

    NASA Astrophysics Data System (ADS)

    Aveta, C.; Salvatori, M.; Vitelli, G. P.

    2017-05-01

    The present paper aims to present a series of experiences and experimentations that a group of PhD from the University of Naples Federico II conducted over the past decade. This work has concerned the survey and the graphic restitution of monuments and works of art, finalized to their conservation. The targeted query of complex point cloud acquired by 3D scanners, integrated with photo sensors and thermal imaging, has allowed to explore new possibilities of investigation. In particular, we will present the scientific results of the experiments carried out on some important historical artifacts with distinct morphological and typological characteristics. According to aims and needs that emerged during the connotative process, with the support of archival and iconographic historical research, the laser scanner technology has been used in many different ways. New forms of representation, obtained directly from the point cloud, have been tested for the elaboration of thematic studies for documenting the pathologies and the decay of materials, for correlating visible aspects with invisible aspects of the artifact.

  10. Cloud Top Scanning radiometer (CTS)

    NASA Technical Reports Server (NTRS)

    1978-01-01

    A scanning radiometer to be used for measuring cloud radiances in each of three spectral regions is described. Significant features incorporated in the Cloud Top Scanner design are: (1) flexibility and growth potential through use of easily replaceable modular detectors and filters; (2) full aperture, multilevel inflight calibration; (3) inherent channel registration through employment of a single shared field stop; and (4) radiometric sensitivity margin in a compact optical design through use of Honeywell developed (Hg,Cd)Te detectors and preamplifiers.

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

    NASA Astrophysics Data System (ADS)

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

    2008-04-01

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

  12. Accuracy analysis of point cloud modeling for evaluating concrete specimens

    NASA Astrophysics Data System (ADS)

    D'Amico, Nicolas; Yu, Tzuyang

    2017-04-01

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

  13. a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    He, H.; Khoshelham, K.; Fraser, C.

    2017-09-01

    Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

  14. a Super Voxel-Based Riemannian Graph for Multi Scale Segmentation of LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Li, Minglei

    2018-04-01

    Automatically segmenting LiDAR points into respective independent partitions has become a topic of great importance in photogrammetry, remote sensing and computer vision. In this paper, we cast the problem of point cloud segmentation as a graph optimization problem by constructing a Riemannian graph. The scale space of the observed scene is explored by an octree-based over-segmentation with different depths. The over-segmentation produces many super voxels which restrict the structure of the scene and will be used as nodes of the graph. The Kruskal coordinates are used to compute edge weights that are proportional to the geodesic distance between nodes. Then we compute the edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths between super voxel nodes on the scene surface. The final segmentation results are generated by clustering similar super voxels and cutting off the weak edges in the graph. The performance of this method was evaluated on LiDAR point clouds for both indoor and outdoor scenes. Additionally, extensive comparisons to state of the art techniques show that our algorithm outperforms on many metrics.

  15. Separation and recycling of nanoparticles using cloud point extraction with non-ionic surfactant mixtures.

    PubMed

    Nazar, Muhammad Faizan; Shah, Syed Sakhawat; Eastoe, Julian; Khan, Asad Muhammad; Shah, Afzal

    2011-11-15

    A viable cost-effective approach employing mixtures of non-ionic surfactants Triton X-114/Triton X-100 (TX-114/TX-100), and subsequent cloud point extraction (CPE), has been utilized to concentrate and recycle inorganic nanoparticles (NPs) in aqueous media. Gold Au- and palladium Pd-NPs have been pre-synthesized in aqueous phases and stabilized by sodium 2-mercaptoethanesulfonate (MES) ligands, then dispersed in aqueous non-ionic surfactant mixtures. Heating the NP-micellar systems induced cloud point phase separations, resulting in concentration of the NPs in lower phases after the transition. For the Au-NPs UV/vis absorption has been used to quantify the recovery and recycle efficiency after five repeated CPE cycles. Transmission electron microscopy (TEM) was used to investigate NP size, shape, and stability. The results showed that NPs are preserved after the recovery processes, but highlight a potential limitation, in that further particle growth can occur in the condensed phases. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Efficient LIDAR Point Cloud Data Managing and Processing in a Hadoop-Based Distributed Framework

    NASA Astrophysics Data System (ADS)

    Wang, C.; Hu, F.; Sha, D.; Han, X.

    2017-10-01

    Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mapping city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop's storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data.

  17. Image registration with auto-mapped control volumes

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

    Schreibmann, Eduard; Xing Lei

    2006-04-15

    Many image registration algorithms rely on the use of homologous control points on the two input image sets to be registered. In reality, the interactive identification of the control points on both images is tedious, difficult, and often a source of error. We propose a two-step algorithm to automatically identify homologous regions that are used as a priori information during the image registration procedure. First, a number of small control volumes having distinct anatomical features are identified on the model image in a somewhat arbitrary fashion. Instead of attempting to find their correspondences in the reference image through user interaction,more » in the proposed method, each of the control regions is mapped to the corresponding part of the reference image by using an automated image registration algorithm. A normalized cross-correlation (NCC) function or mutual information was used as the auto-mapping metric and a limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) was employed to optimize the function to find the optimal mapping. For rigid registration, the transformation parameters of the system are obtained by averaging that derived from the individual control volumes. In our deformable calculation, the mapped control volumes are treated as the nodes or control points with known positions on the two images. If the number of control volumes is not enough to cover the whole image to be registered, additional nodes are placed on the model image and then located on the reference image in a manner similar to the conventional BSpline deformable calculation. For deformable registration, the established correspondence by the auto-mapped control volumes provides valuable guidance for the registration calculation and greatly reduces the dimensionality of the problem. The performance of the two-step registrations was applied to three rigid registration cases (two PET-CT registrations and a brain MRI-CT registration) and one deformable

  18. Fitting a Point Cloud to a 3d Polyhedral Surface

    NASA Astrophysics Data System (ADS)

    Popov, E. V.; Rotkov, S. I.

    2017-05-01

    The ability to measure parameters of large-scale objects in a contactless fashion has a tremendous potential in a number of industrial applications. However, this problem is usually associated with an ambiguous task to compare two data sets specified in two different co-ordinate systems. This paper deals with the study of fitting a set of unorganized points to a polyhedral surface. The developed approach uses Principal Component Analysis (PCA) and Stretched grid method (SGM) to substitute a non-linear problem solution with several linear steps. The squared distance (SD) is a general criterion to control the process of convergence of a set of points to a target surface. The described numerical experiment concerns the remote measurement of a large-scale aerial in the form of a frame with a parabolic shape. The experiment shows that the fitting process of a point cloud to a target surface converges in several linear steps. The method is applicable to the geometry remote measurement of large-scale objects in a contactless fashion.

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Bunds, M. P.

    2017-12-01

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

  1. Conoscopic holography for image registration: a feasibility study

    NASA Astrophysics Data System (ADS)

    Lathrop, Ray A.; Cheng, Tiffany T.; Webster, Robert J., III

    2009-02-01

    Preoperative image data can facilitate intrasurgical guidance by revealing interior features of opaque tissues, provided image data can be accurately registered to the physical patient. Registration is challenging in organs that are deformable and lack features suitable for use as alignment fiducials (e.g. liver, kidneys, etc.). However, provided intraoperative sensing of surface contours can be accomplished, a variety of rigid and deformable 3D surface registration techniques become applicable. In this paper, we evaluate the feasibility of conoscopic holography as a new method to sense organ surface shape. We also describe potential advantages of conoscopic holography, including the promise of replacing open surgery with a laparoscopic approach. Our feasibility study investigated use of a tracked off-the-shelf conoscopic holography unit to perform a surface scans on several types of biological and synthetic phantom tissues. After first exploring baseline accuracy and repeatability of distance measurements, we performed a number of surface scan experiments on the phantom and ex vivo tissues with a variety of surface properties and shapes. These indicate that conoscopic holography is capable of generating surface point clouds of at least comparable (and perhaps eventually improved) accuracy in comparison to published experimental laser triangulation-based surface scanning results.

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

    PubMed Central

    Sawicki, Piotr

    2018-01-01

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

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

    PubMed

    Gabara, Grzegorz; Sawicki, Piotr

    2018-03-06

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

  4. Curvature computation in volume-of-fluid method based on point-cloud sampling

    NASA Astrophysics Data System (ADS)

    Kassar, Bruno B. M.; Carneiro, João N. E.; Nieckele, Angela O.

    2018-01-01

    This work proposes a novel approach to compute interface curvature in multiphase flow simulation based on Volume of Fluid (VOF) method. It is well documented in the literature that curvature and normal vector computation in VOF may lack accuracy mainly due to abrupt changes in the volume fraction field across the interfaces. This may cause deterioration on the interface tension forces estimates, often resulting in inaccurate results for interface tension dominated flows. Many techniques have been presented over the last years in order to enhance accuracy in normal vectors and curvature estimates including height functions, parabolic fitting of the volume fraction, reconstructing distance functions, coupling Level Set method with VOF, convolving the volume fraction field with smoothing kernels among others. We propose a novel technique based on a representation of the interface by a cloud of points. The curvatures and the interface normal vectors are computed geometrically at each point of the cloud and projected onto the Eulerian grid in a Front-Tracking manner. Results are compared to benchmark data and significant reduction on spurious currents as well as improvement in the pressure jump are observed. The method was developed in the open source suite OpenFOAM® extending its standard VOF implementation, the interFoam solver.

  5. 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.

  6. 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

  7. 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

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

    NASA Astrophysics Data System (ADS)

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

    2017-07-01

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

  9. Section-Based Tree Species Identification Using Airborne LIDAR Point Cloud

    NASA Astrophysics Data System (ADS)

    Yao, C.; Zhang, X.; Liu, H.

    2017-09-01

    The application of LiDAR data in forestry initially focused on mapping forest community, particularly and primarily intended for largescale forest management and planning. Then with the smaller footprint and higher sampling density LiDAR data available, detecting individual tree overstory, estimating crowns parameters and identifying tree species are demonstrated practicable. This paper proposes a section-based protocol of tree species identification taking palm tree as an example. Section-based method is to detect objects through certain profile among different direction, basically along X-axis or Y-axis. And this method improve the utilization of spatial information to generate accurate results. Firstly, separate the tree points from manmade-object points by decision-tree-based rules, and create Crown Height Mode (CHM) by subtracting the Digital Terrain Model (DTM) from the digital surface model (DSM). Then calculate and extract key points to locate individual trees, thus estimate specific tree parameters related to species information, such as crown height, crown radius, and cross point etc. Finally, with parameters we are able to identify certain tree species. Comparing to species information measured on ground, the portion correctly identified trees on all plots could reach up to 90.65 %. The identification result in this research demonstrate the ability to distinguish palm tree using LiDAR point cloud. Furthermore, with more prior knowledge, section-based method enable the process to classify trees into different classes.

  10. Investigating Freezing Point Depression and Cirrus Cloud Nucleation Mechanisms Using a Differential Scanning Calorimeter

    ERIC Educational Resources Information Center

    Bodzewski, Kentaro Y.; Caylor, Ryan L.; Comstock, Ashley M.; Hadley, Austin T.; Imholt, Felisha M.; Kirwan, Kory D.; Oyama, Kira S.; Wise, Matthew E.

    2016-01-01

    A differential scanning calorimeter was used to study homogeneous nucleation of ice from micron-sized aqueous ammonium sulfate aerosol particles. It is important to understand the conditions at which these particles nucleate ice because of their connection to cirrus cloud formation. Additionally, the concept of freezing point depression, a topic…

  11. Cross Correlation versus Normalized Mutual Information on Image Registration

    NASA Technical Reports Server (NTRS)

    Tan, Bin; Tilton, James C.; Lin, Guoqing

    2016-01-01

    This is the first study to quantitatively assess and compare cross correlation and normalized mutual information methods used to register images in subpixel scale. The study shows that the normalized mutual information method is less sensitive to unaligned edges due to the spectral response differences than is cross correlation. This characteristic makes the normalized image resolution a better candidate for band to band registration. Improved band-to-band registration in the data from satellite-borne instruments will result in improved retrievals of key science measurements such as cloud properties, vegetation, snow and fire.

  12. Assessing Temporal Behavior in LIDAR Point Clouds of Urban Environments

    NASA Astrophysics Data System (ADS)

    Schachtschneider, J.; Schlichting, A.; Brenner, C.

    2017-05-01

    Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

  13. 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

  14. Handheld laser scanner automatic registration based on random coding

    NASA Astrophysics Data System (ADS)

    He, Lei; Yu, Chun-ping; Wang, Li

    2011-06-01

    Current research on Laser Scanner often focuses mainly on the static measurement. Little use has been made of dynamic measurement, that are appropriate for more problems and situations. In particular, traditional Laser Scanner must Keep stable to scan and measure coordinate transformation parameters between different station. In order to make the scanning measurement intelligently and rapidly, in this paper ,we developed a new registration algorithm for handleheld laser scanner based on the positon of target, which realize the dynamic measurement of handheld laser scanner without any more complex work. the double camera on laser scanner can take photograph of the artificial target points to get the three-dimensional coordinates, this points is designed by random coding. And then, a set of matched points is found from control points to realize the orientation of scanner by the least-square common points transformation. After that the double camera can directly measure the laser point cloud in the surface of object and get the point cloud data in an unified coordinate system. There are three major contributions in the paper. Firstly, a laser scanner based on binocular vision is designed with double camera and one laser head. By those, the real-time orientation of laser scanner is realized and the efficiency is improved. Secondly, the coding marker is introduced to solve the data matching, a random coding method is proposed. Compared with other coding methods,the marker with this method is simple to match and can avoid the shading for the object. Finally, a recognition method of coding maker is proposed, with the use of the distance recognition, it is more efficient. The method present here can be used widely in any measurement from small to huge obiect, such as vehicle, airplane which strengthen its intelligence and efficiency. The results of experiments and theory analzing demonstrate that proposed method could realize the dynamic measurement of handheld laser

  15. Comparison of a UAV-derived point-cloud to Lidar data at Haig Glacier, Alberta, Canada

    NASA Astrophysics Data System (ADS)

    Bash, E. A.; Moorman, B.; Montaghi, A.; Menounos, B.; Marshall, S. J.

    2016-12-01

    The use of unmanned aerial vehicles (UAVs) is expanding rapidly in glaciological research as a result of technological improvements that make UAVs a cost-effective solution for collecting high resolution datasets with relative ease. The cost and difficult access traditionally associated with performing fieldwork in glacial environments makes UAVs a particularly attractive tool. In the small, but growing, body of literature using UAVs in glaciology the accuracy of UAV data is tested through the comparison of a UAV-derived DEM to measured control points. A field campaign combining simultaneous lidar and UAV flights over Haig Glacier in April 2015, provided the unique opportunity to directly compare UAV data to lidar. The UAV was a six-propeller Mikrokopter carrying a Panasonic Lumix DMC-GF1 camera with a 12 Megapixel Live MOS sensor and Lumix G 20 mm lens flown at a height of 90 m, resulting in sub-centimetre ground resolution per image pixel. Lidar data collection took place April 20, while UAV flights were conducted April 20-21. A set of 65 control points were laid out and surveyed on the glacier surface on April 19 and 21 using a RTK GPS with a vertical uncertainty of 5 cm. A direct comparison of lidar points to these control points revealed a 9 cm offset between the control points and the lidar points on average, but the difference changed distinctly from points collected on April 19 versus those collected April 21 (7 cm and 12 cm). Agisoft Photoscan was used to create a point-cloud from imagery collected with the UAV and CloudCompare was used to calculate the difference between this and the lidar point cloud, revealing an average difference of less than 17 cm. This field campaign also highlighted some of the benefits and drawbacks of using a rotary UAV for glaciological research. The vertical takeoff and landing capabilities, combined with quick responsiveness and higher carrying capacity, make the rotary vehicle favourable for high-resolution photos when

  16. A Multistage Approach for Image Registration.

    PubMed

    Bowen, Francis; Hu, Jianghai; Du, Eliza Yingzi

    2016-09-01

    Successful image registration is an important step for object recognition, target detection, remote sensing, multimodal content fusion, scene blending, and disaster assessment and management. The geometric and photometric variations between images adversely affect the ability for an algorithm to estimate the transformation parameters that relate the two images. Local deformations, lighting conditions, object obstructions, and perspective differences all contribute to the challenges faced by traditional registration techniques. In this paper, a novel multistage registration approach is proposed that is resilient to view point differences, image content variations, and lighting conditions. Robust registration is realized through the utilization of a novel region descriptor which couples with the spatial and texture characteristics of invariant feature points. The proposed region descriptor is exploited in a multistage approach. A multistage process allows the utilization of the graph-based descriptor in many scenarios thus allowing the algorithm to be applied to a broader set of images. Each successive stage of the registration technique is evaluated through an effective similarity metric which determines subsequent action. The registration of aerial and street view images from pre- and post-disaster provide strong evidence that the proposed method estimates more accurate global transformation parameters than traditional feature-based methods. Experimental results show the robustness and accuracy of the proposed multistage image registration methodology.

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

    NASA Astrophysics Data System (ADS)

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

    2018-05-01

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

  18. 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

  19. 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.

  20. 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

  1. [Determination of biphenyl ether herbicides in water using HPLC with cloud-point extraction].

    PubMed

    He, Cheng-Yan; Li, Yuan-Qian; Wang, Shen-Jiao; Ouyang, Hua-Xue; Zheng, Bo

    2010-01-01

    To determine residues of multiple biphenyl ether herbicides simultaneously in water using high performance liquid chromatography (HPLC) with cloud-point extraction. The residues of eight biphenyl ether herbicides (including bentazone, fomesafen, acifluorfen, aclonifen, bifenox, fluoroglycofenethy, nitrofen, oxyfluorfen) in water samples were extracted with cloud-point extraction of Triton X-114. The analytes were separated and determined using reverse phase HPLC with ultraviolet detector at 300 nm. Optimized conditions for the pretreatment of water samples and the parameters of chromatographic separation applied. There was a good linear correlation between the concentration and the peak area of the analytes in the range of 0.05-2.00 mg/L (r = 0.9991-0.9998). Except bentazone, the spiked recoveries of the biphenyl ether herbicides in the water samples ranged from 80.1% to 100.9%, with relative standard deviations ranging from 2.70% to 6.40%. The detection limit of the method ranged from 0.10 microg/L to 0.50 microg/L. The proposed method is simple, rapid and sensitive, and can meet the requirements of determination of multiple biphenyl ether herbicides simultaneously in natural waters.

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

    NASA Astrophysics Data System (ADS)

    Michele, Mangiameli; Giuseppe, Mussumeci; Salvatore, Zito

    2017-07-01

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

  3. Determination of Cd in urine by cloud point extraction-tungsten coil atomic absorption spectrometry.

    PubMed

    Donati, George L; Pharr, Kathryn E; Calloway, Clifton P; Nóbrega, Joaquim A; Jones, Bradley T

    2008-09-15

    Cadmium concentrations in human urine are typically at or below the 1 microgL(-1) level, so only a handful of techniques may be appropriate for this application. These include sophisticated methods such as graphite furnace atomic absorption spectrometry and inductively coupled plasma mass spectrometry. While tungsten coil atomic absorption spectrometry is a simpler and less expensive technique, its practical detection limits often prohibit the detection of Cd in normal urine samples. In addition, the nature of the urine matrix often necessitates accurate background correction techniques, which would add expense and complexity to the tungsten coil instrument. This manuscript describes a cloud point extraction method that reduces matrix interference while preconcentrating Cd by a factor of 15. Ammonium pyrrolidinedithiocarbamate and Triton X-114 are used as complexing agent and surfactant, respectively, in the extraction procedure. Triton X-114 forms an extractant coacervate surfactant-rich phase that is denser than water, so the aqueous supernatant is easily removed leaving the metal-containing surfactant layer intact. A 25 microL aliquot of this preconcentrated sample is placed directly onto the tungsten coil for analysis. The cloud point extraction procedure allows for simple background correction based either on the measurement of absorption at a nearby wavelength, or measurement of absorption at a time in the atomization step immediately prior to the onset of the Cd signal. Seven human urine samples are analyzed by this technique and the results are compared to those found by the inductively coupled plasma mass spectrometry analysis of the same samples performed at a different institution. The limit of detection for Cd in urine is 5 ngL(-1) for cloud point extraction tungsten coil atomic absorption spectrometry. The accuracy of the method is determined with a standard reference material (toxic metals in freeze-dried urine) and the determined values agree with

  4. Topobathymetric LiDAR point cloud processing and landform classification in a tidal environment

    NASA Astrophysics Data System (ADS)

    Skovgaard Andersen, Mikkel; Al-Hamdani, Zyad; Steinbacher, Frank; Rolighed Larsen, Laurids; Brandbyge Ernstsen, Verner

    2017-04-01

    Historically it has been difficult to create high resolution Digital Elevation Models (DEMs) in land-water transition zones due to shallow water depth and often challenging environmental conditions. This gap of information has been reflected as a "white ribbon" with no data in the land-water transition zone. In recent years, the technology of airborne topobathymetric Light Detection and Ranging (LiDAR) has proven capable of filling out the gap by simultaneously capturing topographic and bathymetric elevation information, using only a single green laser. We collected green LiDAR point cloud data in the Knudedyb tidal inlet system in the Danish Wadden Sea in spring 2014. Creating a DEM from a point cloud requires the general processing steps of data filtering, water surface detection and refraction correction. However, there is no transparent and reproducible method for processing green LiDAR data into a DEM, specifically regarding the procedure of water surface detection and modelling. We developed a step-by-step procedure for creating a DEM from raw green LiDAR point cloud data, including a procedure for making a Digital Water Surface Model (DWSM) (see Andersen et al., 2017). Two different classification analyses were applied to the high resolution DEM: A geomorphometric and a morphological classification, respectively. The classification methods were originally developed for a small test area; but in this work, we have used the classification methods to classify the complete Knudedyb tidal inlet system. References Andersen MS, Gergely Á, Al-Hamdani Z, Steinbacher F, Larsen LR, Ernstsen VB (2017). Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment. Hydrol. Earth Syst. Sci., 21: 43-63, doi:10.5194/hess-21-43-2017. Acknowledgements This work was funded by the Danish Council for Independent Research | Natural Sciences through the project "Process-based understanding and

  5. 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.

  6. Mobile Laser Scanning along Dieppe coastal cliffs: reliability of the acquired point clouds applied to rockfall assessments

    NASA Astrophysics Data System (ADS)

    Michoud, Clément; Carrea, Dario; Augereau, Emmanuel; Cancouët, Romain; Costa, Stéphane; Davidson, Robert; Delacourt, Chirstophe; Derron, Marc-Henri; Jaboyedoff, Michel; Letortu, Pauline; Maquaire, Olivier

    2013-04-01

    Dieppe coastal cliffs, in Normandy, France, are mainly formed by sub-horizontal deposits of chalk and flintstone. Largely destabilized by an intense weathering and the Channel sea erosion, small and large rockfalls are regularly observed and contribute to retrogressive cliff processes. During autumn 2012, cliff and intertidal topographies have been acquired with a Terrestrial Laser Scanner (TLS) and a Mobile Laser Scanner (MLS), coupled with seafloor bathymetries realized with a multibeam echosounder (MBES). MLS is a recent development of laser scanning based on the same theoretical principles of aerial LiDAR, but using smaller, cheaper and portable devices. The MLS system, which is composed by an accurate dynamic positioning and orientation (INS) devices and a long range LiDAR, is mounted on a marine vessel; it is then possible to quickly acquire in motion georeferenced LiDAR point clouds with a resolution of about 15 cm. For example, it takes about 1 h to scan of shoreline of 2 km long. MLS is becoming a promising technique supporting erosion and rockfall assessments along the shores of lakes, fjords or seas. In this study, the MLS system used to acquire cliffs and intertidal areas of the Cap d'Ailly was composed by the INS Applanix POS-MV 320 V4 and the LiDAR Optech Ilirs LR. On the same day, three MLS scans with large overlaps (J1, J21 and J3) have been performed at ranges from 600 m at 4 knots (low tide) up to 200 m at 2.2 knots (up tide) with a calm sea at 2.5 Beaufort (small wavelets). Mean scan resolutions go from 26 cm for far scan (J1) to about 8.1 cm for close scan (J3). Moreover, one TLS point cloud on this test site has been acquired with a mean resolution of about 2.3 cm, using a Riegl LMS Z390i. In order to quantify the reliability of the methodology, comparisons between scans have been realized with the software Polyworks™, calculating shortest distances between points of one cloud and the interpolated surface of the reference point cloud. A Mat

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

    PubMed Central

    Wang, Yunsheng; Weinacker, Holger; Koch, Barbara

    2008-01-01

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

  8. Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds.

    PubMed

    Uher, Vojtěch; Gajdoš, Petr; Radecký, Michal; Snášel, Václav

    2016-01-01

    The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.

  9. Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

    PubMed Central

    Radecký, Michal; Snášel, Václav

    2016-01-01

    The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds. PMID:27974884

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

    PubMed

    Li, Jilong; Cheng, Jianlin

    2016-05-10

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

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

    PubMed Central

    Li, Jilong; Cheng, Jianlin

    2016-01-01

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

  12. Implicit Shape Models for Object Detection in 3d Point Clouds

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

  13. Cloud Modeling

    NASA Technical Reports Server (NTRS)

    Tao, Wei-Kuo; Moncrieff, Mitchell; Einaud, Franco (Technical Monitor)

    2001-01-01

    Numerical cloud models have been developed and applied extensively to study cloud-scale and mesoscale processes during the past four decades. The distinctive aspect of these cloud models is their ability to treat explicitly (or resolve) cloud-scale dynamics. This requires the cloud models to be formulated from the non-hydrostatic equations of motion that explicitly include the vertical acceleration terms since the vertical and horizontal scales of convection are similar. Such models are also necessary in order to allow gravity waves, such as those triggered by clouds, to be resolved explicitly. In contrast, the hydrostatic approximation, usually applied in global or regional models, does allow the presence of gravity waves. In addition, the availability of exponentially increasing computer capabilities has resulted in time integrations increasing from hours to days, domain grids boxes (points) increasing from less than 2000 to more than 2,500,000 grid points with 500 to 1000 m resolution, and 3-D models becoming increasingly prevalent. The cloud resolving model is now at a stage where it can provide reasonably accurate statistical information of the sub-grid, cloud-resolving processes poorly parameterized in climate models and numerical prediction models.

  14. Development of a piecewise linear omnidirectional 3D image registration method

    NASA Astrophysics Data System (ADS)

    Bae, Hyunsoo; Kang, Wonjin; Lee, SukGyu; Kim, Youngwoo

    2016-12-01

    This paper proposes a new piecewise linear omnidirectional image registration method. The proposed method segments an image captured by multiple cameras into 2D segments defined by feature points of the image and then stitches each segment geometrically by considering the inclination of the segment in the 3D space. Depending on the intended use of image registration, the proposed method can be used to improve image registration accuracy or reduce the computation time in image registration because the trade-off between the computation time and image registration accuracy can be controlled for. In general, nonlinear image registration methods have been used in 3D omnidirectional image registration processes to reduce image distortion by camera lenses. The proposed method depends on a linear transformation process for omnidirectional image registration, and therefore it can enhance the effectiveness of the geometry recognition process, increase image registration accuracy by increasing the number of cameras or feature points of each image, increase the image registration speed by reducing the number of cameras or feature points of each image, and provide simultaneous information on shapes and colors of captured objects.

  15. Registration of 4D time-series of cardiac images with multichannel Diffeomorphic Demons.

    PubMed

    Peyrat, Jean-Marc; Delingette, Hervé; Sermesant, Maxime; Pennec, Xavier; Xu, Chenyang; Ayache, Nicholas

    2008-01-01

    In this paper, we propose a generic framework for intersubject non-linear registration of 4D time-series images. In this framework, spatio-temporal registration is defined by mapping trajectories of physical points as opposed to spatial registration that solely aims at mapping homologous points. First, we determine the trajectories we want to register in each sequence using a motion tracking algorithm based on the Diffeomorphic Demons algorithm. Then, we perform simultaneously pairwise registrations of corresponding time-points with the constraint to map the same physical points over time. We show this trajectory registration can be formulated as a multichannel registration of 3D images. We solve it using the Diffeomorphic Demons algorithm extended to vector-valued 3D images. This framework is applied to the inter-subject non-linear registration of 4D cardiac CT sequences.

  16. 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

  17. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2014-01-01

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

  20. 3-D Deformation Field Of The 2010 El Mayor-Cucapah (Mexico) Earthquake From Matching Before To After Aerial Lidar Point Clouds

    NASA Astrophysics Data System (ADS)

    Hinojosa-Corona, A.; Nissen, E.; Arrowsmith, R.; Krishnan, A. K.; Saripalli, S.; Oskin, M. E.; Arregui, S. M.; Limon, J. F.

    2012-12-01

    The Mw 7.2 El Mayor-Cucapah earthquake (EMCE) of 4 April 2010 generated a ~110 km long, NW-SE trending rupture, with normal and right-lateral slip in the order of 2-3m in the Sierra Cucapah, the northern half, where the surface rupture has the most outstanding expression. Vertical and horizontal surface displacements produced by the EMCE have been addressed separately by other authors with a variety of aerial and satellite remote sensing techniques. Slip variation along fault and post-seismic scarp erosion and diffusion have been estimated in other studies using terrestrial LiDAR (TLS) on segments of the rupture. To complement these other studies, we computed the 3D deformation field by comparing pre- to post-event point clouds from aerial LiDAR surveys. 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 3-dimensional 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 a translation and a rotation) that best aligns the pre- to post-event points. Testing on synthetic datasets perturbed with displacements of known magnitude showed that windows with dimensions of 100-200m gave the best results for datasets with these densities. Here we present the deformation field with detailed displacements in segments of the surface rupture where its expression was recognized by ICP from the point cloud matching, mainly the scarcely vegetated Sierra Cucapah with the Borrego and Paso Superior fault segments the most outstanding, where we are able to compare our results with values measured in the field and results from TLS reported in other works. EMC simulated displacement field for a

  1. Methods and considerations to determine sphere center from terrestrial laser scanner point cloud data

    NASA Astrophysics Data System (ADS)

    Rachakonda, Prem; Muralikrishnan, Bala; Cournoyer, Luc; Cheok, Geraldine; Lee, Vincent; Shilling, Meghan; Sawyer, Daniel

    2017-10-01

    The Dimensional Metrology Group at the National Institute of Standards and Technology is performing research to support the development of documentary standards within the ASTM E57 committee. This committee is addressing the point-to-point performance evaluation of a subclass of 3D imaging systems called terrestrial laser scanners (TLSs), which are laser-based and use a spherical coordinate system. This paper discusses the usage of sphere targets for this effort, and methods to minimize the errors due to the determination of their centers. The key contributions of this paper include methods to segment sphere data from a TLS point cloud, and the study of some of the factors that influence the determination of sphere centers.

  2. Formation of massive, dense cores by cloud-cloud collisions

    NASA Astrophysics Data System (ADS)

    Takahira, Ken; Shima, Kazuhiro; Habe, Asao; Tasker, Elizabeth J.

    2018-03-01

    We performed sub-parsec (˜ 0.014 pc) scale simulations of cloud-cloud collisions of two idealized turbulent molecular clouds (MCs) with different masses in the range of (0.76-2.67) × 104 M_{⊙} and with collision speeds of 5-30 km s-1. Those parameters are larger than in Takahira, Tasker, and Habe (2014, ApJ, 792, 63), in which study the colliding system showed a partial gaseous arc morphology that supports the NANTEN observations of objects indicated to be colliding MCs using numerical simulations. Gas clumps with density greater than 10-20 g cm-3 were identified as pre-stellar cores and tracked through the simulation to investigate the effects of the mass of colliding clouds and the collision speeds on the resulting core population. Our results demonstrate that the smaller cloud property is more important for the results of cloud-cloud collisions. The mass function of formed cores can be approximated by a power-law relation with an index γ = -1.6 in slower cloud-cloud collisions (v ˜ 5 km s-1), and is in good agreement with observation of MCs. A faster relative speed increases the number of cores formed in the early stage of collisions and shortens the gas accretion phase of cores in the shocked region, leading to the suppression of core growth. The bending point appears in the high-mass part of the core mass function and the bending point mass decreases with increase in collision speed for the same combination of colliding clouds. The higher-mass part of the core mass function than the bending point mass can be approximated by a power law with γ = -2-3 that is similar to the power index of the massive part of the observed stellar initial mass function. We discuss implications of our results for the massive-star formation in our Galaxy.

  3. Formation of massive, dense cores by cloud-cloud collisions

    NASA Astrophysics Data System (ADS)

    Takahira, Ken; Shima, Kazuhiro; Habe, Asao; Tasker, Elizabeth J.

    2018-05-01

    We performed sub-parsec (˜ 0.014 pc) scale simulations of cloud-cloud collisions of two idealized turbulent molecular clouds (MCs) with different masses in the range of (0.76-2.67) × 104 M_{⊙} and with collision speeds of 5-30 km s-1. Those parameters are larger than in Takahira, Tasker, and Habe (2014, ApJ, 792, 63), in which study the colliding system showed a partial gaseous arc morphology that supports the NANTEN observations of objects indicated to be colliding MCs using numerical simulations. Gas clumps with density greater than 10-20 g cm-3 were identified as pre-stellar cores and tracked through the simulation to investigate the effects of the mass of colliding clouds and the collision speeds on the resulting core population. Our results demonstrate that the smaller cloud property is more important for the results of cloud-cloud collisions. The mass function of formed cores can be approximated by a power-law relation with an index γ = -1.6 in slower cloud-cloud collisions (v ˜ 5 km s-1), and is in good agreement with observation of MCs. A faster relative speed increases the number of cores formed in the early stage of collisions and shortens the gas accretion phase of cores in the shocked region, leading to the suppression of core growth. The bending point appears in the high-mass part of the core mass function and the bending point mass decreases with increase in collision speed for the same combination of colliding clouds. The higher-mass part of the core mass function than the bending point mass can be approximated by a power law with γ = -2-3 that is similar to the power index of the massive part of the observed stellar initial mass function. We discuss implications of our results for the massive-star formation in our Galaxy.

  4. Stereovision-based integrated system for point cloud reconstruction and simulated brain shift validation.

    PubMed

    Yang, Xiaochen; Clements, Logan W; Luo, Ma; Narasimhan, Saramati; Thompson, Reid C; Dawant, Benoit M; Miga, Michael I

    2017-07-01

    Intraoperative soft tissue deformation, referred to as brain shift, compromises the application of current image-guided surgery navigation systems in neurosurgery. A computational model driven by sparse data has been proposed as a cost-effective method to compensate for cortical surface and volumetric displacements. We present a mock environment developed to acquire stereoimages from a tracked operating microscope and to reconstruct three-dimensional point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent. The microscope is tracked via an attached tracking rigid body that facilitates the recording of the position of the microscope via a commercial optical tracking system as it moves during the procedure. Point clouds, reconstructed under different microscope positions, are registered into the same space to compute the feature displacements. Using our mock craniotomy device, realistic cortical deformations are generated. When comparing our tracked microscope stereo-pair measure of mock vessel displacements to that of the measurement determined by the independent optically tracked stylus marking, the displacement error was [Formula: see text] on average. These results demonstrate the practicality of using tracked stereoscopic microscope as an alternative to laser range scanners to collect sufficient intraoperative information for brain shift correction.

  5. Nonrigid mammogram registration using mutual information

    NASA Astrophysics Data System (ADS)

    Wirth, Michael A.; Narhan, Jay; Gray, Derek W. S.

    2002-05-01

    Of the papers dealing with the task of mammogram registration, the majority deal with the task by matching corresponding control-points derived from anatomical landmark points. One of the caveats encountered when using pure point-matching techniques is their reliance on accurately extracted anatomical features-points. This paper proposes an innovative approach to matching mammograms which combines the use of a similarity-measure and a point-based spatial transformation. Mutual information is a cost-function used to determine the degree of similarity between the two mammograms. An initial rigid registration is performed to remove global differences and bring the mammograms into approximate alignment. The mammograms are then subdivided into smaller regions and each of the corresponding subimages is matched independently using mutual information. The centroids of each of the matched subimages are then used as corresponding control-point pairs in association with the Thin-Plate Spline radial basis function. The resulting spatial transformation generates a nonrigid match of the mammograms. The technique is illustrated by matching mammograms from the MIAS mammogram database. An experimental comparison is made between mutual information incorporating purely rigid behavior, and that incorporating a more nonrigid behavior. The effectiveness of the registration process is evaluated using image differences.

  6. Feature-Based Retinal Image Registration Using D-Saddle Feature

    PubMed Central

    Hasikin, Khairunnisa; A. Karim, Noor Khairiah; Ahmedy, Fatimah

    2017-01-01

    Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle. PMID:29204257

  7. 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

    LiDAR, also referred to as laser scanning, has proved to be an important tool for topographic data acquisition. Terrestrial laser scanning allows for accurate (several millimeter) and high resolution (several centimeter) data acquisition at distances of up to some hundred meters. By contrast, airborne laser scanning allows for acquiring homogeneous data for large areas, albeit with lower accuracy (decimeter) and resolution (some ten points per square meter) compared to terrestrial laser scanning. Hence, terrestrial laser scanning is preferably used for precise data acquisition of limited areas such as landslides or steep structures, while airborne laser scanning is well suited for the acquisition of topographic data of huge areas or even country wide. Laser scanners acquire more or less homogeneously distributed point clouds. These points represent natural objects like terrain and vegetation and artificial objects like buildings, streets or power lines. Typical products derived from such data are geometric models such as digital surface models representing all natural and artificial objects and digital terrain models representing the geomorphic topography only. As the LiDAR technology evolves, the amount of data produced increases almost exponentially even in smaller projects. This means a considerable challenge for the end user of the data: the experimenter has to have enough knowledge, experience and computer capacity in order to manage the acquired dataset and to derive geomorphologically relevant information from the raw or intermediate data products. Additionally, all this information might need to be integrated with other data like orthophotos. In all theses cases, in general, interactive interpretation is necessary to determine geomorphic structures from such models to achieve effective data reduction. There is little support for the automatic determination of characteristic features and their statistical evaluation. From the lessons learnt from automated

  8. 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.

  9. Digital Investigations of AN Archaeological Smart Point Cloud: a Real Time Web-Based Platform to Manage the Visualisation of Semantical Queries

    NASA Astrophysics Data System (ADS)

    Poux, F.; Neuville, R.; Hallot, P.; Van Wersch, L.; Luczfalvy Jancsó, A.; Billen, R.

    2017-05-01

    While virtual copies of the real world tend to be created faster than ever through point clouds and derivatives, their working proficiency by all professionals' demands adapted tools to facilitate knowledge dissemination. Digital investigations are changing the way cultural heritage researchers, archaeologists, and curators work and collaborate to progressively aggregate expertise through one common platform. In this paper, we present a web application in a WebGL framework accessible on any HTML5-compatible browser. It allows real time point cloud exploration of the mosaics in the Oratory of Germigny-des-Prés, and emphasises the ease of use as well as performances. Our reasoning engine is constructed over a semantically rich point cloud data structure, where metadata has been injected a priori. We developed a tool that directly allows semantic extraction and visualisation of pertinent information for the end users. It leads to efficient communication between actors by proposing optimal 3D viewpoints as a basis on which interactions can grow.

  10. Carbon Sequestration Estimation of Street Trees Based on Point Cloud from Vehicle-Borne Laser Scanning System

    NASA Astrophysics Data System (ADS)

    Zhao, Y.; Hu, Q.

    2017-09-01

    Continuous development of urban road traffic system requests higher standards of road ecological environment. Ecological benefits of street trees are getting more attention. Carbon sequestration of street trees refers to the carbon stocks of street trees, which can be a measurement for ecological benefits of street trees. Estimating carbon sequestration in a traditional way is costly and inefficient. In order to solve above problems, a carbon sequestration estimation approach for street trees based on 3D point cloud from vehicle-borne laser scanning system is proposed in this paper. The method can measure the geometric parameters of a street tree, including tree height, crown width, diameter at breast height (DBH), by processing and analyzing point cloud data of an individual tree. Four Chinese scholartree trees and four camphor trees are selected for experiment. The root mean square error (RMSE) of tree height is 0.11m for Chinese scholartree and 0.02m for camphor. Crown widths in X direction and Y direction, as well as the average crown width are calculated. And the RMSE of average crown width is 0.22m for Chinese scholartree and 0.10m for camphor. The last calculated parameter is DBH, the RMSE of DBH is 0.5cm for both Chinese scholartree and camphor. Combining the measured geometric parameters and an appropriate carbon sequestration calculation model, the individual tree's carbon sequestration will be estimated. The proposed method can help enlarge application range of vehicle-borne laser point cloud data, improve the efficiency of estimating carbon sequestration, construct urban ecological environment and manage landscape.

  11. Speciation and Determination of Low Concentration of Iron in Beer Samples by Cloud Point Extraction

    ERIC Educational Resources Information Center

    Khalafi, Lida; Doolittle, Pamela; Wright, John

    2018-01-01

    A laboratory experiment is described in which students determine the concentration and speciation of iron in beer samples using cloud point extraction and absorbance spectroscopy. The basis of determination is the complexation between iron and 2-(5-bromo-2- pyridylazo)-5-diethylaminophenol (5-Br-PADAP) as a colorimetric reagent in an aqueous…

  12. On the Quality of Point-Clouds Derived from Sfm-Photogrammetry Applied to UAS Imagery

    NASA Astrophysics Data System (ADS)

    Carbonneau, P.; James, T.

    2014-12-01

    Structure from Motion photogrammetry (SfM-photogrammetry) recently appeared in environmental sciences as an impressive tool allowing for the creation of topographic data from unstructured imagery. Several authors have tested the performance of SfM-photogrammetry vs that of TLS or dGPS. Whilst the initial results were very promising, there is currently a growing awareness that systematic deformations occur in DEMs and point-clouds derived from SfM-photogrammetry. Notably, some authors have identified a systematic doming manifest as an increasing error vs distance to the model centre. Simulation studies have confirmed that this error is due to errors in the calibration of camera distortions. This work aims to further investigate these effects in the presence of real data. We start with a dataset of 220 images acquired from a sUAS. After obtaining an initial self-calibration of the camera lens with Agisoft Photoscan, our method consists in applying systematic perturbations to 2 key lens parameters: Focal length and the k1 distortion parameter. For each perturbation, a point-cloud was produced and compared to LiDAR data. After deriving the mean and standard deviation of the error residuals (ɛ), a 2nd order polynomial surface was fitted to the errors point-cloud and the peak ɛ defined as the mathematical extrema of this surface. The results are presented in figure 1. This figure shows that lens perturbations can induce a range of errors with systematic behaviours. Peak ɛ is primarily controlled by K1 with a secondary control exerted by the focal length. These results allow us to state that: To limit the peak ɛ to 10cm, the K1 parameter must be calibrated to within 0.00025 and the focal length to within 2.5 pixels (≈10 µm). This level of calibration accuracy can only be achieved with proper design of image acquisition and control network geometry. Our main point is therefore that SfM is not a bypass to a rigorous and well-informed photogrammetric approach. Users

  13. Advanced Visualization and Interactive Display Rapid Innovation and Discovery Evaluation Research (VISRIDER) Program Task 6: Point Cloud Visualization Techniques for Desktop and Web Platforms

    DTIC Science & Technology

    2017-04-01

    ADVANCED VISUALIZATION AND INTERACTIVE DISPLAY RAPID INNOVATION AND DISCOVERY EVALUATION RESEARCH (VISRIDER) PROGRAM TASK 6: POINT CLOUD...To) OCT 2013 – SEP 2014 4. TITLE AND SUBTITLE ADVANCED VISUALIZATION AND INTERACTIVE DISPLAY RAPID INNOVATION AND DISCOVERY EVALUATION RESEARCH...various point cloud visualization techniques for viewing large scale LiDAR datasets. Evaluate their potential use for thick client desktop platforms

  14. Individual Rocks Segmentation in Terrestrial Laser Scanning Point Cloud Using Iterative Dbscan Algorithm

    NASA Astrophysics Data System (ADS)

    Walicka, A.; Jóźków, G.; Borkowski, A.

    2018-05-01

    The fluvial transport is an important aspect of hydrological and geomorphologic studies. The knowledge about the movement parameters of different-size fractions is essential in many applications, such as the exploration of the watercourse changes, the calculation of the river bed parameters or the investigation of the frequency and the nature of the weather events. Traditional techniques used for the fluvial transport investigations do not provide any information about the long-term horizontal movement of the rocks. This information can be gained by means of terrestrial laser scanning (TLS). However, this is a complex issue consisting of several stages of data processing. In this study the methodology for individual rocks segmentation from TLS point cloud has been proposed, which is the first step for the semi-automatic algorithm for movement detection of individual rocks. The proposed algorithm is executed in two steps. Firstly, the point cloud is classified as rocks or background using only geometrical information. Secondly, the DBSCAN algorithm is executed iteratively on points classified as rocks until only one stone is detected in each segment. The number of rocks in each segment is determined using principal component analysis (PCA) and simple derivative method for peak detection. As a result, several segments that correspond to individual rocks are formed. Numerical tests were executed on two test samples. The results of the semi-automatic segmentation were compared to results acquired by manual segmentation. The proposed methodology enabled to successfully segment 76 % and 72 % of rocks in the test sample 1 and test sample 2, respectively.

  15. Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

    NASA Astrophysics Data System (ADS)

    Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

    2018-01-01

    To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

  16. Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

    NASA Astrophysics Data System (ADS)

    Khuon, Timothy; Kershner, Charles; Mattei, Enrico; Alverio, Arnel; Rand, Robert

    2014-06-01

    Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new

  17. Direct local building inundation depth determination in 3-D point clouds generated from user-generated flood images

    NASA Astrophysics Data System (ADS)

    Griesbaum, Luisa; Marx, Sabrina; Höfle, Bernhard

    2017-07-01

    In recent years, the number of people affected by flooding caused by extreme weather events has increased considerably. In order to provide support in disaster recovery or to develop mitigation plans, accurate flood information is necessary. Particularly pluvial urban floods, characterized by high temporal and spatial variations, are not well documented. This study proposes a new, low-cost approach to determining local flood elevation and inundation depth of buildings based on user-generated flood images. It first applies close-range digital photogrammetry to generate a geo-referenced 3-D point cloud. Second, based on estimated camera orientation parameters, the flood level captured in a single flood image is mapped to the previously derived point cloud. The local flood elevation and the building inundation depth can then be derived automatically from the point cloud. The proposed method is carried out once for each of 66 different flood images showing the same building façade. An overall accuracy of 0.05 m with an uncertainty of ±0.13 m for the derived flood elevation within the area of interest as well as an accuracy of 0.13 m ± 0.10 m for the determined building inundation depth is achieved. Our results demonstrate that the proposed method can provide reliable flood information on a local scale using user-generated flood images as input. The approach can thus allow inundation depth maps to be derived even in complex urban environments with relatively high accuracies.

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

    PubMed

    Symeonidou, Athanasia; Blinder, David; Schelkens, Peter

    2018-04-16

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

  19. Deformable structure registration of bladder through surface mapping.

    PubMed

    Xiong, Li; Viswanathan, Akila; Stewart, Alexandra J; Haker, Steven; Tempany, Clare M; Chin, Lee M; Cormack, Robert A

    2006-06-01

    Cumulative dose distributions in fractionated radiation therapy depict the dose to normal tissues and therefore may permit an estimation of the risk of normal tissue complications. However, calculation of these distributions is highly challenging because of interfractional changes in the geometry of patient anatomy. This work presents an algorithm for deformable structure registration of the bladder and the verification of the accuracy of the algorithm using phantom and patient data. In this algorithm, the registration process involves conformal mapping of genus zero surfaces using finite element analysis, and guided by three control landmarks. The registration produces a correspondence between fractions of the triangular meshes used to describe the bladder surface. For validation of the algorithm, two types of balloons were inflated gradually to three times their original size, and several computerized tomography (CT) scans were taken during the process. The registration algorithm yielded a local accuracy of 4 mm along the balloon surface. The algorithm was then applied to CT data of patients receiving fractionated high-dose-rate brachytherapy to the vaginal cuff, with the vaginal cylinder in situ. The patients' bladder filling status was intentionally different for each fraction. The three required control landmark points were identified for the bladder based on anatomy. Out of an Institutional Review Board (IRB) approved study of 20 patients, 3 had radiographically identifiable points near the bladder surface that were used for verification of the accuracy of the registration. The verification point as seen in each fraction was compared with its predicted location based on affine as well as deformable registration. Despite the variation in bladder shape and volume, the deformable registration was accurate to 5 mm, consistently outperforming the affine registration. We conclude that the structure registration algorithm presented works with reasonable accuracy and

  20. a Voxel-Based Metadata Structure for Change Detection in Point Clouds of Large-Scale Urban Areas

    NASA Astrophysics Data System (ADS)

    Gehrung, J.; Hebel, M.; Arens, M.; Stilla, U.

    2018-05-01

    Mobile laser scanning has not only the potential to create detailed representations of urban environments, but also to determine changes up to a very detailed level. An environment representation for change detection in large scale urban environments based on point clouds has drawbacks in terms of memory scalability. Volumes, however, are a promising building block for memory efficient change detection methods. The challenge of working with 3D occupancy grids is that the usual raycasting-based methods applied for their generation lead to artifacts caused by the traversal of unfavorable discretized space. These artifacts have the potential to distort the state of voxels in close proximity to planar structures. In this work we propose a raycasting approach that utilizes knowledge about planar surfaces to completely prevent this kind of artifacts. To demonstrate the capabilities of our approach, a method for the iterative volumetric approximation of point clouds that allows to speed up the raycasting by 36 percent is proposed.

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

    DOE PAGES

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

    2016-06-10

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  3. Beating-heart registration for organ-mounted robots.<