基于二分K-means聚类的曲率分级点云数据精简优化算法研究  

The optimization algorithm for curvature graded point cloud data based on dichotomous K-means clustering

作　　者：李佩佩 崔凤英[1] Li Peipei;Cui Fengying(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学自动化与电子工程学院,青岛266061

出　　处：《电子测量技术》2022年第4期66-71,共6页Electronic Measurement Technology

基　　金：山东省研究生教育质量提升计划项目(SDYJD18029)资助。

摘　　要：针对单一精简算法无法精确保留模型特征信息、易造成点云表面孔洞等问题,提出了一种基于二分K-means聚类的曲率分级优化精简算法。首先采用最小二乘法对邻域进行曲面拟合,计算曲率值,依据曲率值划分显著特征区与非显著特征区,其次采用二分K-means聚类划分非显著特征区,依据子簇的曲率阈值筛选保留具有特征重要性的亚特征点,最后合并更新显著特征区的数据集和亚特征点,得到简化结果。通过仿真实验,从算法运行速度和信息熵两方面与空间包围盒法、曲率精简算法进行对比分析,结果表明,该算法在精简质量上优于其他两种算法,在点云数据重建方面具有一定的应用价值。For the problems of losing the model feature information and causing easily the point cloud surface holes in single simplification algorithms,a streamlined algorithm for curvature classification optimization based on dichotomous K-means clustering is proposed.First,the least squares method was used to fit the neighborhood surface,calculate the curvature value,and divide the significant and non-significant feature regions based on the curvature value,Second,dichotic K-means clustering was used to divide non-significant feature regions,select the subfeature points with feature importance retained according to the curvature threshold of subclusters,and finally the datasets and subfeature points were merged to obtain simplified results.The simplification algorithm is compared with the space surrounding box algorithm and the curvature reduction algorithm by the simulation experiments in terms ofspeed and information entropy.The results show that the proposed algorithm outperforms the other two algorithms in streamlining quality and has a certain application value in point cloud data reconstruction.

关 键 词：点云数据 二分K-means聚类 曲率简化 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]