Efficient L_0 resampling of point sets

摘要

The point data captured by laser scanners or consumer depth cameras are often contaminated with severe noises and outliers. In this paper, we propose a resampling method in an L-0 minimization framework to process such low quality data. Our framework can produce a set of clean, uniformly distributed, geometry-maintaining and feature-preserving oriented points. The L-0 norm improves the robustness to noises (outliers) and the ability to keep sharp features, but introduces a significant efficiency degradation. To further improve the efficiency of our L-0 point set resampling, we propose two accelerating algorithms including optimization-based local half-sampling and interleaved regularization. As demonstrated by the experimental results, the accelerated method is about an order of magnitude faster than the original, while achieves state-of-the-art point set consolidation performance. (C) 2019 Elsevier B.V. All rights reserved.