Weighted Poisson-disk Resampling on Large-Scale Point Clouds

TL;DR

This paper introduces a weighted Poisson-disk resampling method for large-scale point clouds that enhances efficiency, accuracy, and feature preservation, enabling high-quality geometric processing with controlled point density.

Contribution

The paper presents a novel weighted Poisson-disk resampling approach that combines voxel-based estimation and tangent smoothing to improve large-scale point cloud resampling.

Findings

Significantly improves resampling efficiency for large-scale point clouds

Maintains high geometric accuracy and feature integrity

Produces uniform density with specified point counts

Abstract

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.