Point cloud segmentation using hierarchical tree for architectural models

Recent developments in the 3D scanning technologies have made the generation of highly accurate 3D point clouds relatively easy but the segmentation of these point clouds remains a challenging area. A number of techniques have set precedent of either planar or primitive based segmentation in literature. In this work, we present a novel and an effective primitive based point cloud segmentation algorithm. The primary focus, i.e. the main technical contribution of our method is a hierarchical tree which iteratively divides the point cloud into segments. This tree uses an exclusive energy function and a 3D convolutional neural network, HollowNets to classify the segments. We test the efficacy of our proposed approach using both real and synthetic data obtaining an accuracy greater than 90% for domes and minarets.Comment: 9 pages. 10 figures. Submitted in EuroGraphics 201

Multisource Point Clouds, Point Simplification and Surface Reconstruction

As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for surface reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for surface reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for surface reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known surface reconstruction methods, i.e., Alpha shapes, Screened Poisson reconstruction (SPR), the Crust, and Algebraic point set surfaces (APSS Marching Cubes), were utilized for object reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for surface reconstruction. These reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that (i) the capacity of surface reconstruction in dealing with diverse objects needs to be improved; (ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the reconstruction methods might fail; (iii) for some reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; (iv) all reconstruction methods are beneficial from the reduction of running time; and (v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics

Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points, weighted according to its distance to the sensor, and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create and filter triangles for each triplet of self-connected edges and according to their local planarity. We compare our results to an unweighted simplicial complex reconstruction.Comment: 8 pages, 11 figures, CFPT 2018. arXiv admin note: substantial text overlap with arXiv:1802.0748

Point to Pipe: Automatic Reconstruction and Classification of Pipes Using Lasergrammetry and Thermogrammetry for Building Information Modeling (BIM)

Existing buildings account for 40% of global energy consumption, and two-thirds of them will be still be operational in 2050. As most of these buildings lack the needed documentation for energy upgrades, it is essential to understand and represent the current conditions of their envelopes and mechanical systems. This project proposed a skeleton-based application for reconstructing and classifying pipes in existing buildings using point clouds from laser scanners and thermal images for Building Information Modeling (BIM) applications. MATLAB and Dynamo were used to process and model this information in Revit. Initial results indicate that the application is robust to identifying pipes and connections, and that thermal images can be used to create sematic-rich models. These results can contribute to improving the capabilities of some of the commercially available software for pipe reconstruction in BIM and to expediting the digital reconstruction processes in existing buildings

Scan registration using planar features

Point cloud acquisition by using laser scanners provides an efficient way for 3D as-built modelling of indoor/outdoor urban environments. In the case of large structures, multiple scans may be required to cover the entire scene and registration is needed to merge them together. In general, the identification of corresponding geometric features among a series of scans can be used to compute the 3D rigid-body transformation useful for the registration of each scan into the reference system of the final point cloud. Different automatic or semi-automatic methods have been developed to this purpose. Several solutions based on artificial targets are available, which however may not be suitable in any situations. Methods based on surface matching (like ICP and LS3D) can be applied if the scans to align have a proper geometry and surface texture. In the case of urban and architectural scenes that present the prevalence of a few basic geometric shapes ("Legoland" scenes) the availability of many planar features is exploited here for registration. The presented technique does not require artificial targets to be added to the scanned scene. In addition, unlike other surface-based techniques (like ICP) the planar feature-based registration technique is not limited to work in a pairwise manner but it can handle the simultaneous alignment of multiple scans. Finally, some applications are presented and discussed to show how this technique can achieve accuracy comparable to a consolidated registration method