关键词: 数据简化, 几何特征, Hausdorff距离, 主曲率

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