Weighted Poisson-disk Resampling on Large-Scale Point Clouds
Published in AAAI 2025 , 2025
Abstract: For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased effciency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and effciency for the processing. We frst design an initial Poisson resampling with a voxel-based estimation strategy. It is able to estimate a more accurate radius of the Poisson-disk while maintaining high effciency. Then, we design a weighted tangent smoothing step to further optimize the Voronoi diagram for each point. At the same time, sharp features are detected and kept in the optimized results with isotropic property. Finally, we achieve a resampling copy from the original point cloud with the specifed point number, uniform density, and high-quality geometric consistency. Experiments show that our method signifcantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution.
Recommended citation: Xianhe Jiao, Chenlei Lv, Junli Zhao, Ran Yi,Yu-Hui Wen, Zhenkuan Pan, Zhongke Wu,Yong-Jin Liu, Weighted Poisson-disk Resampling on Large-Scale Point Clouds. AAAI 2025.