样点邻域同构曲面约束的散乱点云法向估计

doi:10.3901/JME.2019.19.146

机械工程学报 ›› 2019, Vol. 55 ›› Issue (19): 146-153.doi: 10.3901/JME.2019.19.146

• 数字化设计与制造 • 上一篇

样点邻域同构曲面约束的散乱点云法向估计

孙殿柱¹, 梁增凯¹, 薄志成¹, 李延瑞², 沈江华¹

1. 山东理工大学机械工程学院淄博 255049;
2. 西安交通大学机械工程学院西安 710049

收稿日期:2018-12-10 修回日期:2019-05-30 发布日期:2020-01-07
通讯作者: 孙殿柱(通信作者),男,1956年出生,博士,教授,博士研究生导师。主要研究方向为数字化设计与制造、逆向工程。E-mail:dianzhus@sdut.edu.cn
作者简介:梁增凯,男,1992年出生,硕士研究生。主要研究方向为数字化设计与制造、逆向工程。E-mail:zengkai27@gmail.com;薄志成,男,1991年出生,硕士研究生。主要研究方向为数字化设计与制造、逆向工程。E-mail:bozhicheng91@gmail.com;李延瑞,男,1979年出生,博士研究生。主要研究方向为数字化设计与制造、逆向工程。E-mail:liyanrui.m2@gmail.com;沈江华,男,1995年出生,硕士研究生。主要研究方向为数字化设计与制造、逆向工程。E-mail:jianghuash@126.com
基金资助:
国家自然科学基金（51575326）和山东省自然科学基金（ZR2015EM031）资助项目。

An Estimation Method for Normal of Unorganized Point Cloud Based on Local Isomorphic Surface

SUN Dianzhu¹, LIANG Zengkai¹, BO Zhicheng¹, LI Yanrui², SHEN Jianghua¹

1. School of Mechanical Engineering, Shandong University of Technology, Zibo 255049;
2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049

Received:2018-12-10 Revised:2019-05-30 Published:2020-01-07

摘要/Abstract

摘要： 针对现有点云法向估计算法难以兼顾估计结果的精度与稳健性问题，以局部采样区域同构曲面作为样点邻域点集所反映曲面形状约束，提出一种散乱点云法向估计方法。该方法将目标样点的邻域点集作为局部样本进行曲面重建，获取插值于采样点集并与采样表面拓扑同构的局部网格曲面；对曲面局部区域高斯映射结果进行聚类分析，获取目标样点的各向同性邻域面；基于面片的正则度以及面片至目标样点的测地距离，确定目标样点各向同性邻域面片法向的加权均值，并将所得结果作为目标样点的法向估计结果。试验结果表明，该方法在点云数据信噪比为40 dB的情况下可保证98%以上样点法向估计偏差在以内，可稳健处理含有噪声以及采样不均匀等缺陷的散乱点云法向估计问题，对于含尖锐特征的点云亦能准确估计样点法向，且具有较高的计算效率。

关键词: 样点邻域, 拓扑同构, 法向估计, 局部重建, 高斯映射

Abstract: For solve the problem that the current algorithms for estimating normals of point cloud are difficult to achieve a trade-off between accuracy and robustness of estimation results, a method for normal estimation based on local isomorphic surface is proposed. In this method, a set of neighborhood points of the target sample point is taken as the local sample to reconstruct the surface, and the local mesh surface interpolated in the sampling point set and isomorphic with the sampling surface topology is obtained. The results of Gauss mapping in the local area are clustered to obtain the isotropic neighborhood of the target sample point. Based on the regularity of the patches and the geodesic distance from the patches to the target sample point, the weighted mean of the normal of the patches in the isotropic neighborhood of the target sample point is determined, and the result obtained is taken as the normal estimation result of the target sample point. The experimental results show that the normal estimation deviation of more than 98% points can be guaranteed to be less than under the condition of point cloud data SNR of 40 dB. The proposed algorithm can robustly deal with the problem of normal estimation of scattered point clouds with noise and non-uniform sampling. It can also accurately estimate the normal of surface samples in sharp feature areas and has high computational efficiency.

Key words: neighborhood of sample point, topological isomorphism, normal estimation, local reconstruction, Gaussian mapping

中图分类号:

TP391

孙殿柱, 梁增凯, 薄志成, 李延瑞, 沈江华. 样点邻域同构曲面约束的散乱点云法向估计[J]. 机械工程学报, 2019, 55(19): 146-153.

SUN Dianzhu, LIANG Zengkai, BO Zhicheng, LI Yanrui, SHEN Jianghua. An Estimation Method for Normal of Unorganized Point Cloud Based on Local Isomorphic Surface[J]. Journal of Mechanical Engineering, 2019, 55(19): 146-153.

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样点邻域同构曲面约束的散乱点云法向估计

An Estimation Method for Normal of Unorganized Point Cloud Based on Local Isomorphic Surface

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