一种三维点云聚类算法的研究

在重构曲面之前,需要对点云数据进行聚类处理,以保证后续3D重建工作准确、高效地进行。基于采用传统的聚类方法处理立体视觉形成的海量数据所存在的计算与存储瓶颈问题,提出一种新型的聚类算法,即基于包围盒的密度聚类算法。它首先利用包围盒算法对给定的海量点云进行过分聚类,然后对每个过分簇求中心,用中心点代替过分簇,最后在过分簇的级别上进行基于密度的聚类来完成对整体的聚类。结果显示该方法能够有效地实现海量点云的聚类,突破计算瓶颈。它实现了原始点云的大量删减,简化率高达96.75%,并最终在过分簇的级别上将原始点云分为5类。

A method of 3d point cloud clustering studing

The point cloud data should be clustered to ensure the accuracy and efficiency of 3D reconstruction work . According to the bottleneck of traditional clustering method when dealing with the mass data which produced by the stereoscopic vision system,this paper proposes an new method called density clustering algorithm based on bounding box. The algorithm first groups the given mass point cloud into lots of small clusters by using the bonding box,then completes the final clustering on the level of means of clusters.The result comes out that the new method is quite effective. It implements the simplification of the original point cloud,the simplified rate is as high as 96.75%, and finally divides the clusters into 5 classes.