Patch-Collaborative Spectral Point-Cloud Denoising

We present a new framework for point cloud denoising by patch‐collaborative spectral analysis. A collaborative generalization of each surface patch is defined, combining similar patches from the denoised surface. The Laplace–Beltrami operator of the collaborative patch is then used to selectively sm...

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Bibliographic Details
Published in:Computer graphics forum Vol. 32; no. 8; pp. 1 - 12
Main Authors: Rosman, G., Dubrovina, A., Kimmel, R.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.12.2013
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ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:We present a new framework for point cloud denoising by patch‐collaborative spectral analysis. A collaborative generalization of each surface patch is defined, combining similar patches from the denoised surface. The Laplace–Beltrami operator of the collaborative patch is then used to selectively smooth the surface in a robust manner that can gracefully handle high levels of noise, yet preserves sharp surface features. The resulting denoising algorithm competes favourably with state‐of‐the‐art approaches, and extends patch‐based algorithms from the image processing domain to point clouds of arbitrary sampling. We demonstrate the accuracy and noise‐robustness of the proposed algorithm on standard benchmark models as well as range scans, and compare it to existing methods for point cloud denoising. We present a new framework for point cloud denoising by patch‐collaborative spectral analysis. A collaborative generalization of each surface patch is defined, combining similar patches from the denoised surface. The Laplace‐Beltrami operator of the collaborative patch is then used to selectively smooth the surface in a robust manner that can gracefully handle high levels of noise, yet preserves sharp surface features.
Bibliography:istex:7335CA1C859302FE243F5603109A13C4FBF60135
ArticleID:CGF12139
ark:/67375/WNG-G4MLC6V7-J
European Community's FP7- ERC - No. 267414
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12139