基于高斯映射聚类的点云边缘提取算法

Point Cloud Edge-Extraction Algorithm Based on Gaussian Map Clustering

基于高斯映射聚类边缘提取算法提出了一种快速而精确的新方法,通过凝聚聚类和估计法线将高斯球中的法线进行聚类,通过分析每个点最近邻域点的协方差矩阵特征值来检测边缘特征。对不同的点云对象进行边缘提取对比实验,分别从边缘提取效果和提取时间进行对比分析。实验结果表明,所提方法能快速有效地提取点云的边缘特征,相比原高斯映射聚类边缘提取算法有很大的提升。

This study proposes a fast and accurate new edge-extraction method based on the Gaussian map clustering algorithm.First,the normals in the Gaussian sphere are clustered via agglomerative clustering and normal estimation.Then,the covariance matrix eigenvalues of the nearest neighbors of each point are analyzed to detect the edge features.The edge-extraction experiments are performed on different pointcloud objects,and the edge extraction effects and the extraction time are compared and analyzed.The experimental results indicate that the proposed method can quickly and efficiently extract the edge features from point clouds and its performance is improved compared with the edge-extraction algorithm based on original Gaussian map clustering.