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.
朱煜 康宝生 李洪安 史芳玲. 一种改进的点云数据精简方法[J]. 计算机应用, 2012, 32(02): 521-544.
ZHU Yu KANG Bao-sheng LI Hong-an SHI Fang-ling. Improved algorithm for point cloud data simplification. Journal of Computer Applications, 2012, 32(02): 521-544.
2012, Vol. 32 
