Weighted Poisson-disk Resampling on Large-Scale Point Clouds

TL;DR

The paper tackles the challenge of resampling large-scale point clouds while preserving geometric fidelity and enabling precise control of point density. It introduces Weighted Poisson-disk (WPD) resampling, a two-stage pipeline consisting of an efficient voxel-based initial Poisson resampling to accurately estimate the Poisson-disk radius and an iterative refinement, followed by a weighted tangent smoothing step that enforces isotropic distribution in the local tangent space with optional sharp feature preservation. Key contributions include voxel-based radius estimation, iterative radius refinement to meet target counts, and a cotangent-weighted Voronoi central-displacement scheme that preserves sharp features while improving isotropy. Experimental results across indoor and outdoor datasets demonstrate improved geometric consistency, density uniformity, and feature preservation, with competitive efficiency suitable for large-scale applications such as mesh reconstruction and semantic analysis.

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.

Figures (10)

• Figure 1: The pipeline of the proposed WPD on UrbanBIS Yang2023UrbanBIS.
• Figure 2: Voxel-based radius estimation and its corresponding initial resampling result for a large-scale scene. Left: original point cloud; middle: voxelization; right: initial Poisson resampling result.
• Figure 3: Tangent space mapping for a local region of an input point cloud.
• Figure 4: As a closed Voronoi cell (green), the related point position is optimized based on the central displacement. For the boundary point with an unclosed Voronoi cell (blue), its position should not be changed. The red lines and related labels represent the cotangent weight instance between two points.
• Figure 5: Sharp feature keeping in large-scale point cloud. Left: uniform resampling result; Right: uniform resampling result with sharp feature enhancement.