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CLC number: TP391.7

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Received: 2007-09-02

Revision Accepted: 2008-03-28

Crosschecked: 0000-00-00

Cited: 6

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.6 P.807-815


Similarity-based denoising of point-sampled surfaces

Author(s):  Ren-fang WANG, Wen-zhi CHEN, San-yuan ZHANG, Yin ZHANG, Xiu-zi YE

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   wrfwpln@126.com, syzhang@cs.zju.edu.cn

Key Words:  Point-sampled surfaces (PSSs), Similarity, Geometry intensity, Geometry feature, Non-local filtering

Ren-fang WANG, Wen-zhi CHEN, San-yuan ZHANG, Yin ZHANG, Xiu-zi YE. Similarity-based denoising of point-sampled surfaces[J]. Journal of Zhejiang University Science A, 2008, 9(6): 807-815.

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publisher="Zhejiang University Press & Springer",

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T1 - Similarity-based denoising of point-sampled surfaces
A1 - Ren-fang WANG
A1 - Wen-zhi CHEN
A1 - San-yuan ZHANG
A1 - Yin ZHANG
A1 - Xiu-zi YE
J0 - Journal of Zhejiang University Science A
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A071465

A non-local denoising (NLD) algorithm for point-sampled surfaces (PSSs) is presented based on similarities, including geometry intensity and features of sample points. By using the trilateral filtering operator, the differential signal of each sample point is determined and called “geometry intensity”. Based on covariance analysis, a regular grid of geometry intensity of a sample point is constructed, and the geometry-intensity similarity of two points is measured according to their grids. Based on mean shift clustering, the PSSs are clustered in terms of the local geometry-features similarity. The smoothed geometry intensity, i.e., offset distance, of the sample point is estimated according to the two similarities. Using the resulting intensity, the noise component from PSSs is finally removed by adjusting the position of each sample point along its own normal direction. Experimental results demonstrate that the algorithm is robust and can produce a more accurate denoising result while having better feature preservation.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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