Paper Title:
Scatter Point Cloud Denoising Based on Self-Adaptive Optimal Neighborhood
  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. 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, J. Liang, C. Guo, "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
$32.00
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