Surface reconstruction by parallel and unified particle-based resampling from point clouds

Abstract

This paper introduces a new unified particle-based formulation for resamplings with specific patterns from original point clouds. Given the input point clouds, the proposed Lp-Gaussian kernel function is defined to simulate the inter-particle energy and force to form the isotropic/adaptive/anisotropic hexagonal and quadrilateral sampling patterns. Then, the particle-based optimization can be easily formulated and computed in parallel scheme with the high-efficiency and the fast convergence, without any control of particle population. Finally, based on the optimized particle distribution, the high-quality surface meshes are reconstructed by computing the restricted Voronoi diagram and its dual mesh with the parallel implementation. The experimental results are demonstrated by using extensive examples and evaluation criteria as well as compared with the state-of-the-art in the point cloud resampling and reconstruction.