A unified framework for isotropic meshing based on narrow-band Euclidean distance transformation

In this paper, we propose a simple-yet-effective method for isotropic meshing relying on Euclidean distance transformation based centroidal Voronoi tessellation (CVT). Our approach improves the performance and robustness of computing CVT on curved domains while simultaneously providing high-quality output meshes. While conventional extrinsic methods compute CVTs in the entire volume bounded by the input model, we restrict the computation to a 3D shell of user-controlled thickness. Taking voxels which contain surface samples as sites, we compute the exact Euclidean distance transform on the GPU. Our algorithm is parallel and memory-efficient, and can construct the shell space for resolutions up to 20483 at interactive speed. The 3D centroidal Voronoi tessellation and restricted Voronoi diagrams are also computed efficiently on the GPU. Since the shell space can bridge holes and gaps smaller than a certain tolerance, and tolerate non-manifold edges and degenerate triangles, our algorithm can handle models with such defects, which typically cause conventional remeshing methods to fail. Our method can process implicit surfaces, polyhedral surfaces, and point clouds in a unified framework. Computational results show that our GPU-based isotropic meshing algorithm produces results comparable to state-of- the-art techniques, but is significantly faster than conventional CPU-based implementations.MOE (Min. of Education, S’pore)Published versio

Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data

This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust z-score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maximum Consistency with Minimum Distance (MCMD). The methods estimate the best-fit-plane based on most probable outlier free, and most consistent, points set in a local neighbourhood. Then the normal and curvature from the best-fit-plane will be highly robust to noise and outliers. Experiments are performed to show the performance of the algorithms compared to several existing well-known methods (from computer vision, data mining, machine learning and statistics) using synthetic and real laser scanning datasets of complex (planar and non-planar) objects. Results for plane fitting, denoising, sharp feature preserving and segmentation are significantly improved. The algorithms are demonstrated to be significantly faster, more accurate and robust. Quantitatively, for a sample size of 50 with 20% outliers the proposed MCMD_Z is approximately 5, 15 and 98 times faster than the existing methods: uLSIF, RANSAC and RPCA, respectively. The proposed MCMD_MD method can tolerate 75% clustered outliers, whereas, RPCA and RANSAC can only tolerate 47% and 64% outliers, respectively. In terms of outlier detection, for the same dataset, MCMD_Z has an accuracy of 99.72%, 0.4% false positive rate and 0% false negative rate; for RPCA, RANSAC and uLSIF, the accuracies are 97.05%, 47.06% and 94.54%, respectively, and they have misclassification rates higher than the proposed methods. The new methods have potential for local surface reconstruction, fitting, and other point cloud processing tasks

Robust statistical approaches for local planar surface fitting in 3D laser scanning data

This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks.Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods

Relevés lumineux tridimensionnels en architecture

Ce mémoire de maîtrise propose la numérisation laser tridimensionnelle LiDARcomme nouvelle méthode d'étude et de visualisation de l'éclairage naturel dans des environnements réels pour architectes et designers. Elle constitue un complément aux méthodes d'éclairage actuelles car elle répond aux limites de la méthode de mesure de l'éclairement, de la simulation numérique et de l'imagerie à haute dynamique(HDR)en ce qui concerne le relevé et la représentation des patterns lumineux. Il présente une étude de cas pour déterminer les avantages et les limites de la numérisation laser 3D dans une vaste cafétéria à éclairage naturel et artificiel, vaste, géométriquement complexe et fragmentée. Les patterns lumineuxet la géométrie del'espace sont capturés par un appareil à balayage laser 3D à travers une série de quatre numérisations. Les numérisations sont alignées et fusionnées pour former un seul modèle 3D de l'espace entier. Les patterns lumineuxsont présentés en relation avec la matérialité, la géométrie et la position des fenêtres, des murs, des appareils d'éclairage et des sources d'éclairage et présentés sous forme d'images semblables à des dessins de présentation architecturaux. Plus précisément, les patterns lumineuxsont illustrés dans un plan d'étage, un plan de plafond réfléchi, une axonométrie et une coupe transversale. La méthode fournit des résultats de visualisation percutants. Elle facilite leur compréhension des patterns lumineux, car un nombre illimité d'images peut être généré à partir d'un nuage de points. L'exactitude de la méthode de relevé des espaces éclairés naturellement estégalementvérifié pour desespacesrelevésen une et plusieurs numérisations en comparant les patterns lumineux des imageries HDR et des nuages de points. De plus, le mémoire explore le potentiel de la numérisation laser tridimensionnelle comme méthode pour simuler de nouvelles ambiances lumineuses dans des espaces existants.This master thesisproposesLiDARtridimensional laser scanning as a new daylighting enquiry and visualization method for real built environments for architects and designers. It constitutes a complement to actual lighting methods because it responds to the limitations of the illuminance measuring method, computer simulation and high dynamic rangeimagery concerning the survey and representation of lighting patterns. It presents a case study to determine the advantages and limitations of 3D laser scanning in a vast, geometrically complex and fragmented naturally and artificially lit cafeteria. Lighting patterns and the geometry of the space are captured with a 3D laser scanner through a series of four scans. The scans are aligned and fused to form a single 3D model of the entire space. The lighting distribution patterns are showcased in relation to the materiality, geometry and position of windows, walls, lighting fixtures and the lighting sources and presented through images similar to architectural presentation drawings. More precisely, the lighting distribution patterns are illustrated in a floor plan, a reflected ceiling plan, an axonometry and a cross-section. The method provides powerful visualization results and facilitates their understanding as an unlimited number of images can be generated from a point cloud.The precision of the method for surveying daylit environments surveyed through one and several scans is also verified by comparing lighting patterns between HDR and point cloud imageries. Moreover, it explores tridimensional laser scanning as a method for rendering new lighting ambiances in existing spaces