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Proceedings Paper

A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising
Author(s): Xin Cui; Shipeng Li; Xiutian Yan; Xinhua He
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Paper Abstract

In order to remove the noise of three-dimensional scattered point cloud and smooth the data without damnify the sharp geometric feature simultaneity, a novel algorithm is proposed in this paper. The feature-preserving weight is added to fuzzy c-means algorithm which invented a curvature weighted fuzzy c-means clustering algorithm. Firstly, the large-scale outliers are removed by the statistics of r radius neighboring points. Then, the algorithm estimates the curvature of the point cloud data by using conicoid parabolic fitting method and calculates the curvature feature value. Finally, the proposed clustering algorithm is adapted to calculate the weighted cluster centers. The cluster centers are regarded as the new points. The experimental results show that this approach is efficient to different scale and intensities of noise in point cloud with a high precision, and perform a feature-preserving nature at the same time. Also it is robust enough to different noise model.

Paper Details

Date Published: 10 April 2018
PDF: 10 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106153E (10 April 2018); doi: 10.1117/12.2303395
Show Author Affiliations
Xin Cui, Beijing Institute of Astronautical Systems Engineering (China)
Shipeng Li, Beijing Institute of Astronautical Systems Engineering (China)
Xiutian Yan, Beijing Institute of Astronautical Systems Engineering (China)
Xinhua He, Beijing Institute of Astronautical Systems Engineering (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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