Feature-aware filtering for point-set surface denoising

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 de...

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Bibliographic Details
Published in:Computers & graphics 2013-10, Vol.37 (6), p.589-595
Main Authors: Park, Min Ki, Lee, Seung Joo, Jang, In Yeop, Lee, Yong Yi, Lee, Kwan H.
Format: Article
Language:English
Subjects:
Applied sciences
Approximation
Artificial intelligence
Computational efficiency
Computer science
control theory
systems
Curvature
Exact sciences and technology
Feature detection
Feature-preserving denoising
Filtering
Filtration
Mathematical analysis
Noise reduction
Pattern recognition. Digital image processing. Computational geometry
Point-based geometry
Preserves
Projection
Second-order filtering
Tensor voting
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Summary: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. [Display omitted] •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.
ISSN:0097-8493
1873-7684
DOI:10.1016/j.cag.2013.05.004