基于二维Delaunay近邻的空间散乱数据曲面重建算法

Surface Reconstruction from Scattered 3D Points Based on 2D Delaunay Neighbors

给出了一种新的散乱数据曲面重建算法。算法基于曲面的局平特性 ,通过二维Delaunay三角剖分到三维空间的映射 ,快速查找空间任意点的Delaunay近邻 ,然后根据散乱数据重建三角网格中顶点互为Delaunay近邻的原理 ,进行曲面拓扑重建。应用新的求解k-近邻和二维Delaunay近邻的算法 ,提高了曲面重建的算法效率。实验表明 ,该算法高效、稳定 。

A novel algorithm for surface reconstruction from unorganized points is proposed in this paper. Based on the local planner feature of the surface, 3D Delaunay neighbors of any point are searched by the map from 2D triangulation to space scattered points. After that, topological reconstruction was obtained according that the vertex of triangular mesh was Delaunay neighbor each other. The efficiency of surface reconstruction algorithm was improved because of the application of new methods for finding k-neighbors and calculating 2D Delaunay neighbors. Experimental results show that this algorithm is effective, robust and adaptive to non-uniform data points.