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Scatter Point Cloud Denoising Based on Self-Adaptive Optimal Neighborhood

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Author:

Liang, Xinhe (Liang, Xinhe.) | Liang, Jin (Liang, Jin.) | Guo, Cheng (Guo, Cheng.)

Indexed by:

CPCI-S EI Scopus

Abstract:

We present a scatter point cloud denoising method, which can reduce noise effectively, while preserving mesh features such as sharp edges and corners. The method consists of two stages. Firstly, noisy points normal are filtered iteratively; second, location noises of points are reduced. I-low to select proper denoising neighbors is a key problem for scatter point cloud denoising operation. The local shape factor which related to the surface feature is proposed. By using the factor, we achieved the shape adaptive angle threshold and adaptive optimal denoising neighbor. Normal space and location space is denoising using improved trilateral filter in adaptive angle threshold. A series of numerical experiment proved the new denoising algorithm in this paper achieved more detail feature and smoother surface.

Keyword:

Denoising Optimal Neighborhood Scatter Point Cloud Self-Adaptive

Author Community:

  • [ 1 ] [Liang, Xinhe; Liang, Jin; Guo, Cheng] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China

Reprint Author's Address:

  • Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China.

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Source :

MANUFACTURING SCIENCE AND ENGINEERING, PTS 1-5

ISSN: 1022-6680

Year: 2010

Volume: 97-101

Page: 3631-3636

Language: English

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count:

30 Days PV: 6

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