光学学报, 2019, 39 (12): 1211001, 网络出版: 2019-12-06
树状分层黎曼图约束的点云法向传播方法 下载: 917次
Normal Propagation of Point Clouds Constrained by Hierarchical Riemannian Graphs with Tree Structures
成像系统 法向传播 多层黎曼图 法向估计 多分辨率模型 海量点云 imaging systems normal propagation multi-layer Riemannian graph normal estimation multi-resolution model massive point clouds
摘要
针对现有曲面采样点云法向传播方法难以快速处理大规模数据的问题,提出了一种在多层黎曼图中统一点云法向的方法。该方法对点云进行子集递归划分得到核心点集,以核心点集的曲面变分程度控制递归次数,为点云构造树状多分辨率模型。自上而下遍历点云多分辨率模型的结点,为非叶结点包含的子集构建黎曼图,从而构成点云的多层黎曼图。以先序遍历的方法将顶层黎曼图中样点法向一致性向下逐层传递,在各黎曼图单元内,以最小生成树算法实现样点法向的一致性传播。实验结果表明,对于大规模点云,该方法能有效提高计算效率与内存利用率,且能保证样点法向在复杂特征区域传播的准确性。
Abstract
A method of unifying the normal orientation of point clouds in multi-layer Riemannian graphsis presented to address the challenges for existing normal propagation methods of point clouds sampled from curved surfaces in quick processing of massive data. In this method, the point clouds are recursively divided into subsets to obtain the core point sets. The surface variability of the core point sets controls the recurrence number, and a multi-resolution model of tree structure is constructed for the point clouds. The nodes of the point-cloud multi-resolution model are traversed from top to bottom, and the multi-layer Riemannian graph of the point clouds is thus constructed from the subset of non-leaf nodes. Using the sequential traversal method, the normal unification of the sample points in the top-layer Riemannian graph is transmitted downwards. For each Riemannian graph unit, the minimum spanning tree algorithm is used to obtain the normal unification of the sample points. The experimental results demonstrate that this method can effectively improve the computational efficiency and memory utilization in processing massive point clouds and ensure the accuracy of the normal propagation of sample points in complex feature areas.
梁增凯, 孙殿柱, 李延瑞, 沈江华, 张硕. 树状分层黎曼图约束的点云法向传播方法[J]. 光学学报, 2019, 39(12): 1211001. Zengkai Liang, Dianzhu Sun, Yanrui Li, Jianghua Shen, Shuo Zhang. Normal Propagation of Point Clouds Constrained by Hierarchical Riemannian Graphs with Tree Structures[J]. Acta Optica Sinica, 2019, 39(12): 1211001.