点云内在属性因子驱动的自适应滚球算法

A New Self-adaptive Ball Pivoting Algorithm Driven by an Intrinsic Property Factor of Point Cloud

采用滚球算法(ball pivoting algorithm,BPA)重建非均匀点云时会产生较多孔洞或冗余三角形,对此先定义一种点云内在属性因子,提出了一种自适应BPA算法,并用重建曲面表面积定量评价曲面重建质量。首先,根据点云法向、位置、点间距离、关系等信息选取3个恰当的孤立点,构建种子三角形;其次,计算每条拓展边的点云内在属性因子,并结合拓展边长等信息,自适应地确定滚球半径r;最后,将半径为r的滚球沿着拓展边滚动,选取合适的第三点拓展三角形网格。采用龙、兔点云进行曲面重建实验,实验结果表明,无论是均匀点云还是非均匀点云,此算法均能自适应地重建出点云表面模型,重建过程无需人工干预,算法稳健、高效,重建结果质量较高。

To solve the shortcoming that there will be some holes and/or redundant triangles when non-uniform point cloud is reconstructed by BPA(ball pivoting algorithm),a new self-adaptive ball pivoting algorithm is proposed.The improved algorithm is driven by an intrinsic property of point cloud,which is initially proposed.And according to the reconstructed surface area,a new method of surface reconstruction quality evaluation is also proposed.Firstly,three isolated points are selected to build a seed triangle,according to the points vectors,position,spacing,connection and so on.Then,the radius r of the pivoting ball is adaptively calculated based on the intrinsic property of point cloud and front edge length.Finally,a suitable third point is selected by pivoting the ball of radius r around the front edge,to expand the triangulation.Experiments on Dragon and Bunny point cloud show that the proposed algorithm can adaptively reconstruct the surface of both uniform and non-uniform point cloud.Moreover,it is robust,efficient and needs no manual intervention.The reconstructed surface is of high-quality according to the proposed method of surface reconstruction quality evaluation.