无序点云的平坦度自适应增量网格重建算法

An Incremental Mesh Reconstruction Algorithm from Unordered Point Cloud Based on Self-Adapting Surface Flatness

设计了无序点云的平坦度自适应增量网格重建算法,通过对局部曲面平坦度的计算,根据预定义的公式,动态地调整自适应逼近误差参数,从而在保证网格质量的同时,过滤部分对重建效果意义不大的点,因此,适用于海量数据.该算法避免了基于三维Delaunay的四面体剖分带来的高复杂度及基于二维平面投影的三角剖分带来的变形和局限性.实验证明,能够高效、可靠地生成贴近原始曲面的三角网格,并取得较理想的绘制效果.

An algorithm of incremental mesh reconstruction from unordered point cloud is introduced, along with the self-adapting surface flatness. According to the local flatness of surface, this algorithm adjusts the approaching error parameter dynamically, so that the triangulation quality can be ensured although part of the points which are useless to the result of reconstruction have been filtered. Thus it performs well with dense scattered points. With this method, large computational complexity in tetrahedral generation and the mesh distortion and limitation resulted from the projection onto two dimensions can be avoided. Results of the examples show that the triangle mesh from the point cloud can be authentically reconstructed in an effective way.