Scatter Point Cloud Denoising Based on Self-Adaptive Optimal Neighborhood

Abstract:

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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. How 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.

Info:

Periodical:

Advanced Materials Research (Volumes 97-101)

Edited by:

Zhengyi Jiang and Chunliang Zhang

Pages:

3631-3636

DOI:

10.4028/www.scientific.net/AMR.97-101.3631

Citation:

X. H. Liang et al., "Scatter Point Cloud Denoising Based on Self-Adaptive Optimal Neighborhood", Advanced Materials Research, Vols. 97-101, pp. 3631-3636, 2010

Online since:

March 2010

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Price:

$35.00

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