Triangular Mesh Reconstruction from Spatial Point Clouds

This paper comprehensively describes the process of reconstructing triangular meshes from spatial point clouds. The methodology involves three key technical components: triangular mesh generation, multi-seed point selection, and prioritized growth algorithms for flat surface regions. Specifically, we investigate the transformation of point cloud data into mesh structures while implementing these three techniques. The implementation typically involves computing normal vectors using principal component analysis (PCA), establishing initial seed triangles through nearest neighbor searches, and applying region-growing algorithms with priority queues that favor flat areas. We also discuss optimization strategies for these methods, including KD-tree acceleration for spatial queries and normal vector smoothing techniques, to ensure better adaptability across diverse data scenarios in practical applications. Through this paper, readers will gain deep insights into point cloud reconstruction methodologies and their corresponding technical implementations.