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

作     者：Wen, Bo Tao, Bo Pan, Wei Jiang, Guozhang 

作者机构：Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei China School of Mechanical and Automotive Engineering South China University of Technology Guangdong China Department of R&D OPT Machine Vision Tech Co. Ltd Guangdong China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei China 

出 版 物：《International Journal of Wireless and Mobile Computing》 (Int. J. Wireless Mobile Comput.)

年 卷 期：2020年第19卷第3期

页      面：267-275页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：This project is supported by National Natural Science Foundation of China  under Grant No. 51505349 

主　　题：Principal component analysis 

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.