采用局部凸性和八叉树的点云分割算法

针对粗糙点云分割效果差的问题,提出了一种采用八叉树和局部凸性的点云分割算法.该算法首先通过仪器扫描得到仅包含坐标信息的点云数据,然后对点云进行法向量估算,并根据点云的法向量信息进行八叉树初始分割得到面片,最后根据面片之间的局部凸性特征进行融合,得到最终的分割结果.与其他同类算法相比,采用八叉树和局部凸性的点云分割算法不仅能有效地减少曲面数量,而且在曲面质量上也优于同类算法.采用塔身震落石块的点云数据进行的实验表明,该算法在处理分布较均匀的闭合点云数据时,能够有效减少最终的曲面个数,且面片的质量与手工分割拟合度达到90 ％以上.

A Point Cloud Segmentation Algorithm Using Local Convexity and Octree

An algorithm of point cloud segmentation using octree and local convexity is proposed to deal with the fact that normal segmentation methods have poor effect on coarse point clouds.The proposed algorithm is composed of three steps.Firstly,point clouds with 3D coordinates are obtained using special instruments.Then,normal estimation approaches are used to get the normal vector and to segment the point clouds into patches by octree.Finally,the neighboring patches are merged if local convexity is held.Compared with other works,the proposed algorithm can reduce the number of surface effectively and produce surfaces with better quality.Experimental results show that the proposed algorithm can better segment the nearly and equally distributed data into several meaningful surfaces and 90% of the surfaces is the same as that produced by human operation.