Abstract: 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 fitting. Then an error metric based on Hausdorff distance of principal curvature was used to keep and extract the feature points. Finally, through testing and analyzing some measured data with different features, the results show that the proposed 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.

Key words: data simplification, geometrical feature, Hausdorff distance, principal curvature

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。

关键词: 数据简化, 几何特征, Hausdorff距离, 主曲率