基于多参数k-means聚类的自适应点云精简

Adaptive Point Cloud Reduction Based on Multi Parameter k-Means Clustering

点云数据量十分庞大,合理地精简点云数据是点云数据处理的重要研究内容。针对传统点云精简算法存在的细节缺失、空洞等问题,提出一种基于多参数k-means聚类的自适应点云精简算法。该方法基于KD-Tree创建点云k邻域,结合曲面拟合对点云数据进行曲率和法向特征计算,运用多参数混合特征提取方法对点云特征及边界进行检测并保留;并由KD-Tree索引确定初始化聚类簇心,进行k-means聚类,聚类结果根据最大曲率偏差作细分精简。将本文算法、曲率采样法、均匀网格法与随机精简法分别应用于不同类型的点云模型中进行实验,结果表明,本文算法在复杂模型下的标准偏差均优于后三者,且可以较好地保留点云的细节特征信息,精简效果与模型完整性优于均匀网格法与曲率采样法。

The amount of point cloud data is very large,so it is an important research content to reduce the point cloud data reasonably.Aiming at the problems of missing details and containing holes in traditional point cloud reduction algorithm,this paper proposes an adaptive point cloud reduction algorithm based on multi parameter k-means clustering.In this method,k-neighborhood of point cloud is created based on KD tree,curvature and normal features of point cloud data are calculated by surface fitting,and point cloud features and boundaries are detected and preserved by multi parameter mixed feature extraction method;initial cluster center is determined by KD tree index,k-means clustering is conducted,and clustering results are refined according to maximum curvature deviation.This algorithm,curvature sampling method,uniform grid method and random reduction method are applied to different types of point cloud models for experiments.The results show that the proposed algorithm has the lower standard deviation than the latter three methods in complex model,and can retain the detailed feature information of point cloud.In addition,the reduction effect and model integrity of the proposed algorithm are better than those of uniform grid method and curvature sampling method.