10 April 2018 A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106153E (2018) https://doi.org/10.1117/12.2303395
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
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.
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Xin Cui, Xin Cui, Shipeng Li, Shipeng Li, Xiutian Yan, Xiutian Yan, Xinhua He, Xinhua He, } "A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106153E (10 April 2018); doi: 10.1117/12.2303395; https://doi.org/10.1117/12.2303395
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