Point Cloud Segmentation Based on Spectral Clustering

A spectral clustering based method is proposed to segment point cloud into meaningful subparts.By representing the point cloud as a graph G,the segmentation problem can be turned into a graph min-cut problem.The nonsymmetric normalized Laplacian matrix is used to construct the spectral space.By removing redundant eigenvectors from the spectral domain,the segmentation solution is found in a lower dimensional space.The theoretical guarantee of the proposed method is proved.The accuracy and efficiency of the algorithm are verified by experimental results.