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Similarity Based Filtering of Point Clouds

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Julie Digne

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.
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Dates and versions

hal-01098019 , version 1 (22-12-2014)

Identifiers

  • HAL Id : hal-01098019 , version 1

Cite

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