点云特征保留的精简方法研究  

Research on Simplification Method of Point Cloud Feature Preservation

作　　者：刘洋[1] 高磊 吴学群[1] 田赢 Liu Yang;Gao Lei;Wu Xuequn;Tian Ying(School of Land and Resources Engineering,Kunming University of Technology,Kunming 600093,Yunnan,China;Yunnan Investment Holding Group Co.,Ltd.,Kunming 650000,Yunnan,China)

机构地区：[1]昆明理工大学国土资源工程学院,云南昆明600093 [2]云南省投资控股集团有限公司,云南昆明650000

出　　处：《应用激光》2024年第1期144-154,共11页Applied Laser

基　　金：国家自然科学基金地区基金项目(41961053,41961039)。

摘　　要：针对传统点云精简算法在精简点云时特征丢失严重、空洞较多等问题,提出一种顾及点云特征和完整性的点云精简算法。该算法首先利用点云的邻域法向夹角提取出模型的整体特征点;其次利用模糊C-均值聚类算法并依据点云曲率和快速点特征直方图提取出局部特征点;再次对非局部特征点则利用改进的体素精简法进行下采样得到非特征点;最后将各步所得点云进行融合,进而得到最终精简的点云。将所提算法与传统的方法和其他文献中的方法进行对比,并用描述数据集之间误差的定量指标Hausdorff距离作为精简精度的评价指标。经试验证明,对于Bunny数据集和Skull数据集,所提算法的Hausdorff距离分别比随机精简法低约25%和39%,比曲率精简法低约86%和95%,比其他文献中的方法低约86%和81%。由此可见,所提精简算法具有较高的精简精度。Aiming at the problems of serious feature loss and more holes in the traditional point cloud simplification algorithm,a point cloud simplification algorithm considering the features and integrity of point clouds was proposed.Firstly,the whole feature points of the model are extracted by using the neighborhood normal Angle of the point cloud.Then the fuzzy C-means clustering algorithm is used to extract the local feature points according to the curvature of the point cloud and the fast point feature histogram.Then the non-local feature points are subsampled using the improved voxel reduction method to obtain the non-feature points.Finally,the point clouds obtained from each step are fused to obtain the final reduced point cloud.The proposed algorithm is compared with the traditional methods and method of other literature,and the Hausdorff distance,a quantitative index describing the error between datasets,is used as the evaluation index of the simplification accuracy.The experimental results show that the Hausdorff distance of the proposed algorithm on Bunny dataset and Skull dataset is about 25% and 39% lower than that of the random reduction method,about 86% and 95% lower than that of the curvature reduction method,and about 86% and 81% lower than method of other literature.It can be seen that the simplification algorithm in this paper has high simplification accuracy.

关 键 词：点云精简 邻域法向夹角 快速点特征直方图 模糊C-均值聚类算法 点云曲率 HAUSDORFF距离 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]