Robust normal estimation in unstructured 3D point clouds by selective normal space exploration

We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and outliers and can preserve sharp features in the input model while being significantly faster than the current state-of-the-art alternatives. The ke...

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Vydané v:	The Visual computer Ročník 34; číslo 6-8; s. 961 - 971
Hlavní autori:	Mura, Claudio, Wyss, Gregory, Pajarola, Renato
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2018 Springer Nature B.V
Predmet:	Artificial Intelligence Computer Graphics Computer Science Estimation Image Processing and Computer Vision Methods Neighborhoods Original Article Outliers (statistics) Robustness Space exploration Three dimensional models Robust statistics Normal estimation Point cloud processing
ISSN:	0178-2789, 1432-2315
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Abstract	We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and outliers and can preserve sharp features in the input model while being significantly faster than the current state-of-the-art alternatives. The key idea to this is a novel strategy for the exploration of the normal space: First, an initial candidate normal vector, optimal under a robust least median norm, is selected from a discrete subregion of this space, chosen conservatively to include the correct normal; then, the final robust normal is computed, using a simple, robust procedure that iteratively refines the candidate normal initially selected. This strategy allows us to reduce the computation time significantly with respect to other methods based on sampling consensus and yet produces very reliable normals even in the presence of noise and outliers as well as along sharp features. The validity of our approach is confirmed by an extensive testing on both synthetic and real-world data and by a comparison against the most relevant state-of-the-art approaches.
AbstractList	We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and outliers and can preserve sharp features in the input model while being significantly faster than the current state-of-the-art alternatives. The key idea to this is a novel strategy for the exploration of the normal space: First, an initial candidate normal vector, optimal under a robust least median norm, is selected from a discrete subregion of this space, chosen conservatively to include the correct normal; then, the final robust normal is computed, using a simple, robust procedure that iteratively refines the candidate normal initially selected. This strategy allows us to reduce the computation time significantly with respect to other methods based on sampling consensus and yet produces very reliable normals even in the presence of noise and outliers as well as along sharp features. The validity of our approach is confirmed by an extensive testing on both synthetic and real-world data and by a comparison against the most relevant state-of-the-art approaches.
Author	Wyss, Gregory Pajarola, Renato Mura, Claudio
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References_xml	– reference: Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., Silva, C.T.: Point set surfaces. In: Proceedings IEEE Visualization, pp. 21–28 (2001) – reference: ZhengYFuHAuOKCTaiCLBilateral normal filtering for mesh denoisingIEEE Trans. Visual Comput. Graph.201117101521513010.1109/TVCG.2010.264 – reference: LiBSchnabelRKleinRChengZDangGShiyaoJRobust normal estimation for point clouds with sharp featuresComput. Graph.20103429410610.1016/j.cag.2010.01.004 – reference: BotschMKobbeltLReal-time shape editing using radial basis functionsComput. Graph. Forum200524361162110.1111/j.1467-8659.2005.00886.x – reference: Huber, P.J., Ronchetti, E.M.: Robust Statistics. Wiley Series in Probability and Statistics. Wiley (2009) – reference: PaulyMKeiserRKobbeltLGrossMShape modeling with point-sampled geometryACM Trans. Graph.200322364165010.1145/882262.882319 – reference: WangYFengHYDelormeFEEnginSAn adaptive normal estimation method for scanned point clouds with sharp featuresComput. Aided Des.201345111333134810.1016/j.cad.2013.06.003 – reference: FleishmanSCohen-OrDSilvaCTRobust moving least-squares fitting with sharp featuresACM Trans. Graph.200524354455210.1145/1073204.1073227 – reference: Miller, J.V., Stewart, C.V.: MUSE: Robust surface fitting using unbiased scale estimates. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 300–306 (1996) – reference: LiuXZhangJCaoJLiBLiuLQuality point cloud normal estimation by guided least squares representationComput. Graph.201551Supplement C10611610.1016/j.cag.2015.05.024 – reference: MitraNJNguyenAGuibasLEstimating surface normals in noisy point cloud dataInt. J. Comput. Geom. Appl.2004144–5261276208782410.1142/S02181959040014701056.94504 – reference: BorrmannDElsebergJLingemannKNüchterAThe 3D hough transform for plane detection in point clouds: a review and a new accumulator design3D Res.20112232:132:1310.1007/3DRes.02(2011)3 – reference: ZhangJCaoJLiuXWangJLiuJShiXPoint cloud normal estimation via low-rank subspace clusteringComput. Graph.201337669770610.1016/j.cag.2013.05.008 – reference: CazalsFPougetMEstimating differential quantities using polynomial fitting of osculating jetsComput. Aided Geom. Des.2005222121146211609810.1016/j.cagd.2004.09.0041084.65017 – reference: Mitra, N.J., Nguyen, A.: Estimating surface normals in noisy point cloud data. In: Proceedings ACM Symposium on Computational Geometry, pp. 322–328 (2003) – reference: BoulchAMarletRFast and robust normal estimation for point clouds with sharp featuresComput. Graph. Forum20123151765177410.1111/j.1467-8659.2012.03181.x – reference: FischlerMABollesRCRandom sample consensus: a paradigm for model fitting with applications to image analysis and automated cartographyCommun. ACM198124638139561815810.1145/358669.358692 – reference: JonesTRDurandFZwickerMNormal improvement for point renderingIEEE Comput. Graph. Appl.2004244535610.1109/MCG.2004.14 – reference: Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: International Conference on Robotics and Automation (ICRA), pp. 1–4 (2011) – reference: BoulchAMarletRDeep learning for robust normal estimation in unstructured point cloudsComput. Graph. Forum201635528129010.1111/cgf.12983 – reference: KobbeltLBotschMA survey of point-based techniques in computer graphicsComput. Graph.200428680181410.1016/j.cag.2004.08.009 – reference: AvronHSharfAGreifCCohen-OrDl1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_1$$\end{document}-Sparse reconstruction of sharp point set surfacesACM Trans. Graph.2010295135:1135:1210.1145/1857907.1857911 – reference: KazhdanMHoppeHScreened poisson surface reconstructionACM Trans. Graph.201332329:129:1310.1145/2487228.24872371322.68228 – reference: Sainz, M., Pajarola, R., Lario, R.: Points reloaded: point-based rendering revisited. In: Proceedings Eurographics/IEEE VGTC Symposium on Point-Based Graphics, pp. 121–128 (2004) – reference: SchnabelRWahlRKleinREfficient RANSAC for point-cloud shape detectionComput. Graph. 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Snippet	We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and...
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SubjectTerms	Artificial Intelligence Computer Graphics Computer Science Estimation Image Processing and Computer Vision Methods Neighborhoods Original Article Outliers (statistics) Robustness Space exploration Three dimensional models
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Title	Robust normal estimation in unstructured 3D point clouds by selective normal space exploration
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