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首页> 外文会议>International conference on graphic and image processing >A Curvature-based weighted Fuzzy C-Means Algorithm for Point Clouds De-noising
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A Curvature-based weighted Fuzzy C-Means Algorithm for Point Clouds De-noising

机译：基于曲率的加权模糊C均值算法用于点云降噪

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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.
机译：为了消除三维离散点云的噪声并平滑数据而又不损害尖锐的几何特征同时性，提出了一种新颖的算法。保留特征权重被添加到模糊c均值算法中，发明了曲率加权模糊c均值聚类算法。首先，通过对r个半径相邻点的统计来去除大规模的离群值。然后，该算法使用圆锥抛物线拟合方法估计点云数据的曲率，并计算曲率特征值。最后，提出的聚类算法适用于计算加权聚类中心。聚类中心被视为新点。实验结果表明，该方法对点云中不同尺度和强度的噪声都具有较高的精度，并且可以同时保持特征。同样，它对于不同的噪声模型也足够健壮。

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