【摘要】 为了实现点云模型的有意义分割,提出一种基于谱聚类的分割算法.首先用图G表示点云模型,将分割问题转化为图切割问题;然后根据归一化的非对称Laplacian矩阵构造谱聚类空间;最后通过移除掉多余的特征向量,在一个更低维的空间中找到了分割问题的松弛解.文中还给出了该算法相关定理的证明,并通过实验验证了算法的正确性和有效性.更多还原

【Abstract】 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.更多还原