具有复杂拓扑结构的树木枝干重建问题是国内外研究的一个热点和难点. 本文提出了一种有效且鲁棒的树木枝干重建算法. 首先在原始树木点云上建立基于黎曼流形的Delaunay邻域关系,然后将所有顶点当作位置约束加Laplace方程,再迭代地解Laplace方程将点云收缩到我们预想的程度,然后利用聚类和连接算法得到一个初步的树木枝干,最后再通过修复得到最终的树木枝干. 本文的算法在对含笑树和樱花树上进行了验证,实验结果表明该算法有很好的重建效果.Currently,the problem of branches of trees with complex topology reconstruction is a hot and difficult domestic and international research. In this paper,we proposed an effective and robust algorithm for extraction curve-skeletons from point clouds. Firstly based on Riemannian manifolds Delaunay neighborhood relations,and constructed a Laplace matrix. We treated all points as positional constraints. We solved and updated the discrete Laplace system iteratively,until all points contracted to the positions we needed. Then we employed the Principle Component Analysis(PCA)to differentiate between joints and branches of the contracted points. We clustered the two kinds of regions separately to get the key nodes. Then we connected these key nodes by the connection surgery we proposed to get a raw curve-skeleton of the given point cloud. We constructed a graph on the curve-skeleton,and computed the Minimum Spanning Tree(MST). Finally,we refined the MST and gained the final curve-skeleton
