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Article: 3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model

Title3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model
Authors
KeywordsThree-dimensional displays
Manifolds
Noise reduction
Laplace equations
Surface treatment
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83
Citation
IEEE Transactions on Image Processing, 2019, v. 29, p. 3474-3489 How to Cite?
Abstract3D point cloud-a new signal representation of volumetric objects-is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfect acquisition processes of 3D point cloud-e.g., stereo-matching from multiple viewpoint images or depth data acquired directly from active light sensors-imply non-negligible noise in the data. In this paper, we extend a previously proposed low-dimensional manifold model for the image patches to surface patches in the point cloud, and seek self-similar patches to denoise them simultaneously using the patch manifold prior. Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer, and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise. We show that our graph Laplacian regularizer leads to speedy implementation and has desirable numerical stability properties given its natural graph spectral interpretation. Extensive simulation results show that our proposed denoising scheme outperforms state-of-the-art methods in objective metrics and better preserves visually salient structural features like edges.
Persistent Identifierhttp://hdl.handle.net/10722/288099
ISSN
2019 Impact Factor: 9.34
2015 SCImago Journal Rankings: 2.727

 

DC FieldValueLanguage
dc.contributor.authorZeng, J-
dc.contributor.authorCheung, G-
dc.contributor.authorNg, M-
dc.contributor.authorPang, J-
dc.contributor.authorYang, C-
dc.date.accessioned2020-10-05T12:07:51Z-
dc.date.available2020-10-05T12:07:51Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Image Processing, 2019, v. 29, p. 3474-3489-
dc.identifier.issn1057-7149-
dc.identifier.urihttp://hdl.handle.net/10722/288099-
dc.description.abstract3D point cloud-a new signal representation of volumetric objects-is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfect acquisition processes of 3D point cloud-e.g., stereo-matching from multiple viewpoint images or depth data acquired directly from active light sensors-imply non-negligible noise in the data. In this paper, we extend a previously proposed low-dimensional manifold model for the image patches to surface patches in the point cloud, and seek self-similar patches to denoise them simultaneously using the patch manifold prior. Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer, and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise. We show that our graph Laplacian regularizer leads to speedy implementation and has desirable numerical stability properties given its natural graph spectral interpretation. Extensive simulation results show that our proposed denoising scheme outperforms state-of-the-art methods in objective metrics and better preserves visually salient structural features like edges.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83-
dc.relation.ispartofIEEE Transactions on Image Processing-
dc.rightsIEEE Transactions on Image Processing. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectThree-dimensional displays-
dc.subjectManifolds-
dc.subjectNoise reduction-
dc.subjectLaplace equations-
dc.subjectSurface treatment-
dc.title3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model-
dc.typeArticle-
dc.identifier.emailNg, M: michael.ng@hku.hk-
dc.identifier.authorityNg, M=rp02578-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIP.2019.2961429-
dc.identifier.scopuseid_2-s2.0-85079647239-
dc.identifier.hkuros315737-
dc.identifier.volume29-
dc.identifier.spage3474-
dc.identifier.epage3489-
dc.publisher.placeUnited States-

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