Weighted Poisson-disk Resampling on Large-Scale Point Clouds
This addresses the challenge of balancing efficiency and accuracy in point cloud processing for applications like 3D scanning or computer vision, though it appears incremental as it builds on existing Poisson-disk methods.
The paper tackles the problem of resampling large-scale point clouds to control point number and density while maintaining geometric consistency, achieving improved efficiency and accuracy with a method that provides uniform density and high-quality results.
For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. % in related tasks. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased efficiency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing. We first 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 efficiency. 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 specified point number, uniform density, and high-quality geometric consistency. Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution.