散乱点云的自适应α-shape曲面重建  

Surface reconstruction for scattered point clouds with adaptive α-shape

作　　者：何华 李宗春[1] 李国俊 阮焕立 隆昌宇[3] 

机构地区：[1]信息工程大学地理空间信息学院,郑州450001 [2]北京卫星导航中心,北京100094 [3]北京卫星环境工程研究所,北京100094

出　　处：《计算机应用》2016年第12期3394-3397,3401,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(41274014);航天器高精度测量联合实验室基金资助项目(201501)~~

摘　　要：针对α-shape算法不适用于散乱非均匀点集曲面重建的问题,提出了一种基于点云数据局部特征尺寸(LFS)的自适应α-shape曲面重建改进算法。首先,以采样点的k-邻近点计算出负极点逼近曲面中轴(MA);然后,根据近似中轴计算曲面在采样点处的局部特征尺寸,并依据局部特征尺寸对原始点云进行非均匀降采样;最后,根据三角面片的外接球半径和对应的α值自适应重建出物体表面。与α-shape算法相比,所提算法可以有效合理地减少点云数据量,点云简化率达到70%左右,同时重建结果中冗余三角面片更少且基本没有孔洞。实验结果表明,所提算法能够自适应地重建出非均匀点集的表面。The α-shape algorithm is not suitable for surface reconstruction of scattered and non-uniformly sampled points. In order to solve the problem, an improved surface reconstruction algorithm with adaptive α-shape based on Local Feature Size （LFS） of point cloud data was proposed. Firstly, Medial Axis （MA） of the surface was approximated by the negative poles computed by k-nearest neighbors of sampled points. Secondly, the LFS of sampled points was calculated by the approximated MA, and the original point clouds were unequally simplified based on LFS. Finally, the surface was adaptively reconstructed based on the radius of circumscribed ball of triangles and the corresponding a value. In the comparison experiments with α- shape algorithm, the proposed algorithm could effectively and reasonably reduce the number of point clouds, and the simplification rate of point clouds achieved about 70%. Simultaneously, the reconstruction result were obtained with less redundant triangles and few holes. The experimental results show that the proposed algorithm can adaptively reconstruct the surface of non-uniformly sampled point clouds.

关 键 词：α-shape算法 局部特征尺寸 曲面重建 点云简化 自适应 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]