基于样点拓扑近邻的散乱点云曲面拓扑重建

The topology reconstruction of unorganized point cloud based on the topology neighbors of sampling point

提出一种基于样点拓扑近邻的散乱点云曲面拓扑重建算法,对点云数据构建动态空间索引结构,采用动态扩展空心球算法查询样点k近邻,通过对样点的k近邻数据进行偏心扩展和自适应扩展获取样点的拓扑近邻参考数据,从中查询样点的拓扑近邻,从样点的同层拓扑近邻中获取符合Delaunay条件的匹配点,生成局部Delaunay三角网格,并通过增量扩展实现整个散乱点云的曲面拓扑重建.实例证明,该算法可对无隙、有边界等任意模型的散乱点云进行合理的曲面拓扑重建,有效解决了r-dense恰当采样点云中非均匀区域易产生非工艺孔洞的问题.

An algorithm about topology reconstruction of unorganized point cloud based on the topology neighbors of sampling point is proposed,which contains the following steps： First,a dynamic spatial index structure of the points is established with R＊S-tree and k-neighbors are obtained by dynamically enlarge hollow sphere.Second,the topological neighbors reference data of the sampling point are obtained through eccentric and adaptive expansion.Third,the topological neighbors are inquired according to Voronoi Diagram of topological neighbors reference data.Forth,the matching points are obtained from the homolateral topological neighbors.Fifth,local Delaunay triangular mesh is generated;sixth,the topology reconstruction of unorganized point cloud is realized through incremental extending based on the local mesh.It is proved that the results of reconstruction based on this algorithm are reasonable and the problems about the holes are solved effectively.