Iteratively weighted principal component analysis and orientation consistency for normal estimation in point cloud

被引：0

作者：

Wen B. ^{[1
]}

Tao B. ^{[1
]}

Pan W. ^{[2
,3
]}

Jiang G. ^{[4
]}

机构：

[1] Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Hubei

[2] School of Mechanical and Automotive Engineering, South China University of Technology, Guangdong

[3] Department of R&D, OPT Machine Vision Tech Co., Ltd Guangdong

[4] Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Hubei

来源：

Tao, Bo (taoboq@wust.edu.cn) | 1600年 / Inderscience Publishers卷 / 19期

基金：

中国国家自然科学基金;

关键词：

Iterative weighting; Least squares; Local plane fitting; Minimal spanning tree; Orientation consistency; Point cloud normal; Principal component analysis;

D O I：

10.1504/IJWMC.2020.111213

中图分类号：

学科分类号：

摘要：

In this paper, we present a method to robustly estimate normal of unorganised point clouds, namely Iterative Weighted Principal Component Analysis (IWPCA). Since the neighbourhood of a point in a smooth region can be well approximated by a plane, the classical Principal Component Analysis (PCA) is a widely used approach for normal estimation. Iterations are applied and bilateral spatial normal weights are introduced in each iteration for the local plane fitting to enhance the reliability of the PCA results. Minimal Spanning Tree (MST) is used to reorient flipped normals. We demonstrate the effectiveness and robustness of the proposed method on a variety of examples. © 2020 Inderscience Enterprises Ltd.

引用

页码：267 / 275

页数：8

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