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Progressive Modeling of 3D Building Rooftops from Airborne LiDAR and Imagery
Published in Jie Shan, Charles K. Toth, Topographic Laser Ranging and Scanning, 2018
Once all modeling cues are collected, topological relations among the modeling cues are constructed by the BSP technique. In computer science, the BSP is a hierarchical partitioning method for recursively subdividing a space into convex sets with hyperlines. The BSP technique is used to recover a topologically and geometrically correct 3D building rooftop model from incomplete modeling cues. The topology recovery process consists of a partitioning step and plane merging step. In the partitioning step, a hierarchical binary tree is generated by dividing a parent region into two child regions with hyperlines (linear modeling cue). The partitioning optimum is achieved by maximizing partitioning score that consists of planar homogeneity, geometric regularity, and edge correspondence (Sohn et al., 2008). In plane merging step, the adjacent roof planes having similar normal vectors are merged. The merging process continues until no plane can be accepted by the coplanar similarity test. Once all polygons are merged together, 3D building rooftop model can be reconstructed by collecting final leave nodes in the BSP tree. Figure 17.4 shows results of partitioning step, merging step, and corresponding 3D rooftop model.
A Data-Driven Method for Modeling 3D Building Objects Using a Binary Space Partitioning Tree
Published in Jie Shan, Charles K. Toth, Topographic Laser Ranging and Scanning, 2017
Gunho Sohn, Xianfeng Huang, Vincent Tao
The BSP-Tree is a binary data structure that is used to recursively partition a given region into a set of convex polygons with homogeneous attributes. In this section, we exploit the BSP-Tree as a special data structure consisting of convex hierarchical decompositions of 3D LiDAR points. With this tree structure, a prismatic building model is reconstructed through the union of convex polygons, which corresponds to segmented building regions that are delineated by a rectilinear boundary with maximum response to real edges. In other words, the convex decompositions of the LiDAR space induced by the BSP-Tree method serve as a fusion framework for integrating area-features (i.e., clustered building points) and edge-features (line extraction result) for the purpose of representing the boundary of 3D building rooftops. The BSP-Tree algorithm used for building reconstruction consists of the three components: (1) hierarchical binary partitioning, (2) hypothesis-test optimization, and (3) polygon merging. More detailed description of each process is given as follows:
Electric vehicle regional management system based on the BSP model and multi-information fusion
Published in Systems Science & Control Engineering, 2021
To improve the performance of the BSP model as much as possible, the ratio of the number of local computations that can be completed by the processor per second to the amount of data that can be transmitted by the router per second can be expanded as much as possible.