A Robust Normal Estimation Method for Terrestrial Laser Scanning Point Cloud Based on Minimum Covariance Determinant

FENG Lin; LI Binbing

doi:10.13203/j.whugis20170065

Volume 43 Issue 11

Nov. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(11): 1647-1653. > DOI: 10.13203/j.whugis20170065

FENG Lin, LI Binbing. A Robust Normal Estimation Method for Terrestrial Laser Scanning Point Cloud Based on Minimum Covariance Determinant[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1647-1653. DOI: 10.13203/j.whugis20170065

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A Robust Normal Estimation Method for Terrestrial Laser Scanning Point Cloud Based on Minimum Covariance Determinant

FENG Lin^1,,
LI Binbing¹

1.
Department of Information Engineering, Engineering University of People's Armed Police, Xi'an 710086, China

Funds:

The National Natural Science Foundation of China 41171224

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Author Bio:
FENG Lin, PhD candidate, specializes in LiDAR point cloud processing and 3D reconstruction. E-mail: FengLin_PAP@126.com
Received Date: July 24, 2017
Published Date: November 04, 2018

Graphical Abstract

Abstract

Abstract

A robust normal estimation method based on local plane fitting and minimum covariance determinant (MCD) is proposed for terrestrial laser scanning (TLS) point cloud with gross errors and non-uniform sampling. Firstly, fast library for approximate nearest neighbors algorithm is performed to retrieve k nearest neighbor point set. Then, robust estimation of its covariance is calculated by DetMCD (deterministic MCD) and multivariate Mahalanobis distance. Finally, robust estimation of normal vector is calculated through principal component analysis (PCA) method. Compared with PCA, robust PCA and random sample consensus based normal estimation method, on simulated TLS point cloud, experimental results show that the proposed method can get more accurate normal estimation under the influences of gross errors. And its parallel improvement can meet the requirement of efficiency for large scale TLS point cloud processing. Further experiment on real TLS data from natural terrain shows that the proposed method helps to better Poisson surface reconstruction and prove its effectiveness in practical application.

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[1]	李国俊, 李宗春, 孙元超, 等.利用Delaunay细分进行噪声点云曲面重建[J].武汉大学学报·信息科学版, 2017, 42(1):123-129 http://ch.whu.edu.cn/CN/abstract/abstract5645.shtml Li Guojun, Li Zongchun, Sun Yuanchao, et al. Using Delaunay Refinement to Reconstruct Surface from Noisy Point Clouds[J]. Geomatics and Information Science of Wuhan University, 2017, 42(1): 123-129 http://ch.whu.edu.cn/CN/abstract/abstract5645.shtml
[2]	谭凯, 程效军, 张吉星. TLS强度数据的入射角及距离效应改正方法[J].武汉大学学报·信息科学版, 2017, 42(2):223-228 http://ch.whu.edu.cn/CN/abstract/abstract5665.shtml Tan Kai, Cheng Xiaojun, Zhang Jixing. Correction for Incidence Angle and Distance Effects on TLS Intensity Data[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2): 223-228 http://ch.whu.edu.cn/CN/abstract/abstract5665.shtml
[3]	Hoppe H, DeRose T, Duchamp T, et al. Surface Reconstruction from Unorganized Points[C]. The 19th Annual Conference on Computer Graphics and Interactive Techniques, New York, USA, 1992 http://dl.acm.org/citation.cfm?id=134011
[4]	李宝, 程志全, 党岗, 等.三维点云法向量估计综述[J].计算机工程与应用, 2010, 46(23): 1-7 doi: 10.3778/j.issn.1002-8331.2010.23.001 Li Bao, Cheng Zhiquan, Dang Gang, et al. Survey on Normal Estimation for 3D Point Clouds[J]. Computer Engineering and Applications, 2010, 46(23): 1-7 doi: 10.3778/j.issn.1002-8331.2010.23.001
[5]	Yang M, Lee E. Segmentation of Measured Point Data Using a Parametric Quadric Surface Approximation[J]. Computer-Aided Design, 1999, 31(7): 449-457 doi: 10.1016/S0010-4485(99)00042-1
[6]	Alexa M, Behr J, Cohen-Or D, et al. Point Set Surfaces[C]. The Conference on Visualization'01, San Diego, California, USA, 2001
[7]	Pauly M, Keiser R, Kobbelt L P, et al. Shape Modeling with Point-Sampled Geometry[J]. ACM Transactions on Graphics, 2003, 22(3): 641-650 doi: 10.1145/882262
[8]	Fleishman S, Cohenor D, Silva C T. Robust Moving Least-Squares Fitting with Sharp Features[J]. ACM Transactions on Graphics, 2005, 24(3): 544-552 doi: 10.1145/1073204
[9]	Castillo E, Liang J, Zhao H K. Point Cloud Segmentation and Denoising via Constrained Nonlinear Least Squares Normal Estimates[M]// Breuß M, Bruckstein A, Maragos P. Innovations for Shape Analysis. Berlin, Heidelberg: Springer, 2013
[10]	Schnabel R, Wahl R, Klein R. Efficient RANSAC for Point-Cloud Shape Detection[J]. Computer Graphics Forum, 2007, 26(2): 214-226 doi: 10.1111/cgf.2007.26.issue-2
[11]	Yoon M, Lee Y, Lee S, et al. Surface and Normal Ensembles for Surface Reconstruction[J]. Compu-ter-Aided Design, 2007, 39(5): 408-420 doi: 10.1016/j.cad.2007.02.008
[12]	Li B, Schnabel R, Klein R, et al. Robust Normal Estimation for Point Clouds with Sharp Features[J]. Computers & Graphics, 2010, 34(2): 94-106 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ0215979225
[13]	Hubert M, Rousseeuw P J, Verdonck T. A Deterministic Algorithm for Robust Location and Scatter[J]. Journal of Computational & Graphical Statistics, 2012, 21(3): 618-637 doi: 10.1080/10618600.2012.672100
[14]	Muja M, Lowe D G. Scalable Nearest Neighbor Algorithms for High Dimensional Data[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 36(11): 2 227-2 240 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=CC0213214818
[15]	Mitra N J, Nguyen A, Guibas L. Estimating Surface Normals in Noisy Point Cloud Data[J]. International Journal of Computational Geometry & Applications, 2004, 14(4-5): 261-276 http://www.ams.org/mathscinet-getitem?mr=2087824
[16]	Rousseeuw P J. Multivariate Estimation with High Breakdown Point[J]. Mathematical Statistics and Applications, 1985, B: 283-297 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_1004.4883
[17]	Nurunnabi A, Belton D, West G. Diagnostic-Robust Statistical Analysis for Local Surface Fitting in 3D Point Cloud Data[C]. The 22nd Congress of International Society for Photogrammetry and Remote Sensing, Melbourne, Australia, 2012
[18]	McLachlan J. Discriminant Analysis and Statistical Pattern Recognition[J]. Technometrics, 1993, 88(422): 695-697 http://d.old.wanfangdata.com.cn/Periodical/rjxb200504005
[19]	王醒策, 蔡建平, 武仲科, 等.局部表面拟合的点云模型法向估计及重定向算法[J].计算机辅助设计与图形学学报, 2015, 27(4): 614-620 http://d.old.wanfangdata.com.cn/Periodical/jsjfzsjytxxxb201504007 Wang Xingce, Cai Jianping, Wu Zhongke, et al. Normal Estimation and Normal Orientation for Point Cloud Model Based on Improved Local Surface Fitting[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(4): 614-620 http://d.old.wanfangdata.com.cn/Periodical/jsjfzsjytxxxb201504007
[20]	Hubert M, Rousseeuw P J. ROBPCA: A New Approach to Robust Principal Component Analysis[J]. Technometrics, 2005, 47(1): 64-79 doi: 10.1198/004017004000000563
[21]	Fischler M A, Bolles R C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[J]. Communications of the ACM, 1981, 24(6): 381-395 doi: 10.1145/358669.358692

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