一种基于协方差矩阵的点云特征曲线提取算法

A Feature Curve Extraction Algorithm for Point Cloud Based on Covariance Matrix

针对现有特征曲线提取算法对模型细微特征不敏感、时间代价高及抗噪性差的缺点,提出一种基于协方差矩阵与投影映射的提取算法。根据协方差矩阵的特征值进行区域增长聚类分割形成多个带状聚类,在各个聚类内部根据主方向提取关键特征点,并将其投影到经移动最小二乘法拟合的以该关键点为中心的局部曲面上,构成特征曲线。实验结果表明,该方法可有效提高运行效率,并且具有强抗噪性,能够得到光滑的特征曲线。

To solve the problems existing in previous feature curve algorithms, such as insensitivity to detailed features of the model, high time cost and poor anti-noise performance, an algorithm for extracting feature curve from point cloud based on covariance matrix and projection mapping is proposed in this paper. Firstly, the eigenvalues of the covariance matrix are used for regional growth clustering segmentation to cluster the point cloud into multiple band clusters. Then, key feature points are extracted in accordance with the principle direction within each cluster. The key feature points are projected onto the local surface,which is around the key feature point and fitted by the Moving Least Squares （MLS） method, Finally, the feature curves are achieved. Experimental results show that the proposed algorithm has higher efficiency and stronger anti-noise performance, meanwhile getting smooth feature curves.