Similarity Based Filtering of Point Clouds

Julie Digne 1
1 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : Denoising surfaces is a a crucial step in the surface processing pipeline. This is even more challenging when no underlying structure of the surface is known, id est when the surface is represented as a set of unorganized points. In this paper, a denoising method based on local similarities is introduced. The contributions are threefold: first, we do not denoise directly the point positions but use a low/high frequency decomposition and denoise only the high frequency. Second, we introduce a local surface parameterization which is proved stable. Finally, this method works directly on point clouds, thus avoiding building a mesh of a noisy surface which is a difficult problem. Our approach is based on denoising a height vector field by comparing the neighborhood of the point with neighborhoods of other points on the surface. It falls into the non-local denoising framework that has been extensively used in image processing, but extends it to unorganized point clouds.
Type de document :
Communication dans un congrès
CVPR International Workshop on Point Cloud Processing, Jun 2012, Rhode Island, United States. pp.10, Proceedings of International Workshop on Point Cloud Processing
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https://hal.inria.fr/hal-01098019
Contributeur : Pierre Alliez <>
Soumis le : lundi 22 décembre 2014 - 15:59:58
Dernière modification le : samedi 27 janvier 2018 - 01:31:35

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  • HAL Id : hal-01098019, version 1

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Julie Digne. Similarity Based Filtering of Point Clouds. CVPR International Workshop on Point Cloud Processing, Jun 2012, Rhode Island, United States. pp.10, Proceedings of International Workshop on Point Cloud Processing. 〈hal-01098019〉

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