Reconstructing triangulated surfaces from unorganized points through local skeletal stars

Surface reconstruction from unorganized points arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from twodimensional slices, and interactive surface sketching. [...]Reconstrução da superfície de pontos desorganizados surge em uma variedade de situações práticas, tais como rastreamento de um objeto a partir de vários pontos de vista, a recuperação de formas biológicas de fatias bi-dimensionais, e esboçar superfícies interativas. [...

Polylidar3D -- Fast Polygon Extraction from 3D Data

Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data), organized point clouds (e.g., range images), or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of input data abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D's versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy.Comment: 40 page

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.This work was partially funded by the Spanish Government DPI2013-40534-R grant

Surface Reconstruction from Unorganized Point Cloud Data via Progressive Local Mesh Matching

This thesis presents an integrated triangle mesh processing framework for surface reconstruction based on Delaunay triangulation. It features an innovative multi-level inheritance priority queuing mechanism for seeking and updating the optimum local manifold mesh at each data point. The proposed algorithms aim at generating a watertight triangle mesh interpolating all the input points data when all the fully matched local manifold meshes (umbrellas) are found. Compared to existing reconstruction algorithms, the proposed algorithms can automatically reconstruct watertight interpolation triangle mesh without additional hole-filling or manifold post-processing. The resulting surface can effectively recover the sharp features in the scanned physical object and capture their correct topology and geometric shapes reliably. The main Umbrella Facet Matching (UFM) algorithm and its two extended algorithms are documented in detail in the thesis. The UFM algorithm accomplishes and implements the core surface reconstruction framework based on a multi-level inheritance priority queuing mechanism according to the progressive matching results of local meshes. The first extended algorithm presents a new normal vector combinatorial estimation method for point cloud data depending on local mesh matching results, which is benefit to sharp features reconstruction. The second extended algorithm addresses the sharp-feature preservation issue in surface reconstruction by the proposed normal vector cone (NVC) filtering. The effectiveness of these algorithms has been demonstrated using both simulated and real-world point cloud data sets. For each algorithm, multiple case studies are performed and analyzed to validate its performance

Segmentation And Spatial Depth Ridge Detection Of Unorganized Point Cloud Data

Visual 3D data are of interest to a number of fields: medical professionals, game designers, graphic designers, and (in the interest of this paper) ichthyologists interested in the taxonomy of fish. Since the release of the Kinect for the Microsoft XBox, game designers have been interested in using the 3D data returned by the device to understand human movement and translate that movement into an interface with which to interact with game systems. In the medical field, researchers must use computer vision tools to navigate through the data found in CT scans and MRI scans. These tools must segment images into the parts that are relevant to researchers and account for noise related to the scanning process all while ignoring other types of noise such as foreign elements in the body that might indicate signs of illness. 3D point cloud data represents some unique challenges. Consider an object scanned with a laser scanner. The scanner returns the surface points of the object, but nothing more. Using the tool Qhull, a researcher can quickly compute the convex hull of an object (which is an interesting challenge in itself), but the convex hull (obviously) leaves out any description of an object\u27s concave features. Several algorithms have been proposed to illustrate an object\u27s complete features based on unorganized 3D point cloud data as accurately as possible, most notably Boissonnat\u27s tetrahedral culling algorithm and The Power Crust algorithm. We introduce a new approach to the area partitioning problem that takes into consideration these algorithms\u27 strengths and weaknesses. In this paper we propose a methodology for approximating a shape\u27s solid geometry using the unorganized 3D point cloud data of that shape primarily by utilizing localized principal component analysis information. Our model accounts for three comissues that arise in the scanning of 3D objects: noise in surface points, poorly sampled surface area, and narrow corners. We explore each of these areas of concern and outline our approach to each. Our technique uses a growing algorithm that labels points as it progresses and uses those labels with a simple priority queue. We found that our approach works especially well for approximating surfaces under the condition where a local surface is poorly sampled (i.e a significant hole is present in the point cloud). We then turn to study the medial axis of a shape for the purposes of `unfolding\u27 that structure. Our approach uses a ridge formulation based on the spatial depth statistic to create the medial axis. We conclude the paper with visual results of our technique

A software tool for the semi-automatic segmentation of architectural 3D models with semantic annotation and Web fruition

The thorough documentation of Cultural Heritage artifacts is a fundamental concern for management and preservation. In this context, the semantic segmentation and annotation of 3D models of historic buildings is an important modern topic. This work describes a software tool currently under development, for interactive and semi-automatic segmentation, characterization, and annotation of 3D models produced by photogrammetric surveys. The system includes some generic and well-known segmentation approaches, such as region growing and Locally Convex Connected Patches segmentation, but it also contains original code for specific semantic segmentation of parts of buildings, in particular straight stairs and circular-section columns. Furthermore, a method for automatic wall-surface characterization is devoted to rusticated-ashlar detection, in view of masonry-unit segmentation. The software is modular, so allowing easy expandability. It also has tools for data encoding into formats ready for model fruition by Web technologies. These results were partly obtained in collaboration with Corvallis SPA (Padua-Italy, http://www.corvallis.it)

A quasi-Monte Carlo method for computing areas of point-sampled surfaces

A novel and efficient quasi-Monte Carlo method for computing the area of a point-sampled surface with associated surface normal for each point is presented. Our method operates directly on the point cloud without any surface reconstruction procedure. Using the Cauchy–Crofton formula, the area of the point-sampled surface is calculated by counting the number of intersection points between the point cloud and a set of uniformly distributed lines generated with low-discrepancy sequences. Based on a clustering technique, we also propose an effective algorithm for computing the intersection points of a line with the point-sampled surface. By testing on a number of point-based models, experiments suggest that our method is more robust and more efficient than those conventional approaches based on surface reconstruction.postprin

Robust segmentation in laser scanning 3D point cloud data

Segmentation is a most important intermediate step in point cloud data processing and understanding. Covariance statistics based local saliency features from Principal Component Analysis (PCA) are frequently used for point cloud segmentation. However it is well known that PCA is sensitive to outliers. Hence segmentation results can be erroneous and unreliable. The problems of surface segmentation in laser scanning point cloud data are investigated in this paper. We propose a region growing based statistically robust segmentation algorithm that uses a recently introduced fast Minimum Covariance Determinant (MCD) based robust PCA approach. Experiments for several real laser scanning datasets show that PCA gives unreliable and non-robust results whereas the proposed robust PCA based method has intrinsic ability to deal with noisy data and gives more accurate and robust results for planar and non planar smooth surface segmentation