Point cloud resampling using centroidal Voronoi tessellation methods
Point cloud resampling using centroidal Voronoi tessellation methods
复制标题
使用质心 Voronoi 曲面细分方法进行点云重采样
DOI：
10.1016/j.cad.2018.04.010
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发表时间：
2018-09-01
影响因子：
4.3
通讯作者：
Wang, Cheng
中科院分区：
文献类型：
--
作者：
Chen, Zhonggui;Zhang, Tieyi;Wang, Cheng
提出了一种光滑表面点云重采样的新方法。本文的主要贡献是将质心Voronoi细分(CVT)推广到点云数据集上，使点的重采样变得实用和高效。具体地说，通过将Voronoi单元限制在底层曲面上来高效地计算点云上的CVT，该底层曲面由一组最佳拟合平面局部逼近。我们还提出了一种通过交错优化重采样点和更新拟合面来逐步提高重采样质量的方法。我们的通用框架能够从给定的点云生成具有各向同性或各向异性分布的高质量重采样结果。我们进行了大量的实验来证明我们的重采样方法的有效性和稳健性。(C)2018爱思唯尔有限公司。保留所有权利。
This paper presents a novel technique for resampling point clouds of a smooth surface. The key contribution of this paper is the generalization of centroidal Voronoi tessellation (CVT) to point cloud datasets to make point resampling practical and efficient. In particular, the CVT on a point cloud is efficiently computed by restricting the Voronoi cells to the underlying surface, which is locally approximated by a set of best-fitting planes. We also develop an efficient method to progressively improve the resampling quality by interleaving optimization of resampling points and update of the fitting planes. Our versatile framework is capable of generating high-quality resampling results with isotropic or anisotropic distributions from a given point cloud. We conduct extensive experiments to demonstrate the efficacy and robustness of our resampling method. (C) 2018 Elsevier Ltd. All rights reserved.