一种改进的点云数据精简方法  

Improved algorithm for point cloud data simplification

作　　者：朱煜[1] 康宝生[1] 李洪安[1] 史芳玲[1] 

机构地区：[1]西北大学信息科学与技术学院,西安710127

出　　处：《计算机应用》2012年第2期521-523,544,共4页journal of Computer Applications

基　　金：西北大学研究生创新教育项目(10YSY02)

摘　　要：针对Kim的算法在简化散乱点云时经常丢失过多几何特征的不足,提出一种改进的精简方法。首先对点云进行最小二乘抛物面拟合求出所有点的主曲率;然后以数据点主曲率的Hausdorff距离为依据,提取并保留点云中的特征点;最后对具有不同特征的测量数据进行了精简分析。仿真实验结果表明,改进方法既能较大程度地简化数据点云,简化结果比较均匀,又具有不破坏细小特征的特点,能够充分保留原始点云中的几何特征;而且在保证简化质量的前提下提高了算法的效率。该方法能够为后续的三维重建提供有效的数据信息,节约后续工作的处理时间和硬件资源。Due to geometrical features always being excessively lost in Kim＇s simplification process of scattered point cloud, an improved simplification method was proposed. At first, principal curvatures of points in point cloud were estimated by the least square parabolic lilting. Then an error metric based on Hausdorff distance of principal curvature was used to keep and extract the teature points. Finally, through testing and analyzing some measured data with different features, the results show that the p,＇oposed method simplifies the point cloud data to a large exntent, and the simplification results are more uniform, and it can fully retain the original point cloud geometry without breaking the small features, and the quality and efficiency are both guaranteed. The method can provide effective data information for three-dimensional reconstruction to save processing time and hardware resources.

关 键 词：数据简化 几何特征 HAUSDORFF距离 主曲率 

分 类 号：TP317.4[自动化与计算机技术—计算机软件与理论]