Feature-aware filtering for point-set surface denoising

• We achieve perceptual grouping for a consistent neighborhood via tensor voting.
• We develop a novel higher-order filtering based on point normals and curvatures.
• The proposed method well-approximates either the first- or second-order surface approximation according to the local shape.

In this paper, we propose a simple and effective feature-aware filtering for point-set surface denoising, which can achieve a second-order surface approximation of the underlying surface. Our method consists of two stages: robust normal estimation considering sharp features and feature-preserving denoising using a local curvature based projection. The normal clustering based on neighborhood grouping allows the filter to preserve and respect several features during the denoising process. We demonstrate the strength of our method in terms of denoising, feature preservation and computational efficiency.

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