Revisiting Poisson-disk Subsampling for Massive Point Cloud Decimation

Abstract

Scanning devices often produce point clouds exhibiting highly uneven distributions of point samples across the surfaces being captured. Different point cloud subsampling techniques have been proposed to generate more evenly distributed samples. Poisson-disk sampling approaches assign each sample a cost value so that subsampling reduces to sorting the samples by cost and then removing the desired ratio of samples with the highest cost. Unfortunately, these approaches compute the sample cost using pairwise distances of the points within a constant search radius, which is very costly for massive point clouds with uneven densities. In this paper, we revisit Poisson-disk sampling for point clouds. Instead of optimizing for equal densities, we propose to maximize the distance to the closest point, which is equivalent to estimating the local point density as a value inversely proportional to this distance. This algorithm can be efficiently implemented using k nearest-neighbors searches. Besides a kd-tree, our algorithm also uses a voxelization to speed up the searches required to compute per-sample costs. We propose a new strategy to minimize cost updates that is amenable for out-of-core operation. We demonstrate the benefits of our approach in terms of performance, scalability, and output quality. We also discuss extensions based on adding orientation-based and color-based terms to the cost function.