基于Delaunay三角化的噪声点云非均匀采样  

Delaunay-based Non-uniform sampling for noisy point cloud

作　　者：李国俊[1] 李宗春[1] 侯东兴[1] 

机构地区：[1]信息工程大学地理空间信息学院,郑州450052

出　　处：《计算机应用》2014年第10期2922-2924,2929,共4页journal of Computer Applications

摘　　要：针对基于Delaunay三角化曲面重建方法要求点云密度满足ε-sample条件,提出了一种基于Delaunay三角化的噪声点云非均匀采样算法。首先,利用k-邻近点的Voronoi顶点计算出各点的负极点来逼近曲面中轴(MA);然后,根据近似中轴估计出曲面局部特征尺度(LFS);最后,结合Bound Cocone算法,删除多余的非边界点。实例表明,该算法可以准确、稳健地简化噪声点云,同时可以很好地保留曲面边界特征,经简化后的点云适用于基于Delaunay三角化的曲面重建方法。To satisfy ε-sample condition for Delaunay-based triangulation surface reconstruction algorithm, a Delaunay- based non-uniform sampling algorithm for noisy point clouds was proposed. Firstly, the surface Medial Axis （MA） was approximated by the negative poles computed by k-nearest neighbors＇ Voronoi vertices. Secondly, the Local Feature Size （LFS） of surface was estimated with the approximated medial axis. Finally, combined with the Bound Coeone algorithm, the unwanted interior points were removed. Experiments show that the new algorithm can simplify noisy point clouds accurately and robustly while keeping the boundary features well. The simplified point clouds are suitable for Delaunay-based triangulation surface reconstruction algorithm.

关 键 词：DELAUNAY 边界探测 非均匀采样 曲面重建 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]