基于自适应八叉树的三维点云快速拾取方法研究

Quick picking method for 3D piont cloud based on adaptive octree

针对逆向工程中大规模点云数据快速拾取问题,对当前三维图形拾取基本方法进行了研究,对点云拾取的基本流程和点云快速拾取的关键问题进行了分析,提出了一种基于自适应八叉树的三维点云快速拾取方法。当用户在计算机屏幕上给出拾取多边形后,首先基于点云分布密度,对点云数据进行了自适应八叉树划分;然后对八叉树节点进行了投影,在屏幕上形成了八叉树节点的投影多边形,并对拾取多边形建立了矩形包围盒;接着对八叉树投影多边形和拾取多边形的矩形包围盒进行了相交检测,将不与矩形包围盒相交的八叉树节点包含的点云去除,从而缩小了点云拾取所需判断的范围,提升了拾取效率。最后对不同分布密度点云进行了定面积的拾取实验。实验结果表明,该点云拾取方法的点云分布密度越大,拾取时间相对越短,算法具有较高的拾取速度和准确度。

Aiming at the quick picking problem of massive point cloud data in reverse engineering,the basic picking method of 3D graphics currently was researched,the basic process of point cloud picking and the key problem of quick picking was analyzed,a quick picking algorithm for 3D point cloud based on adaptive octree was proposed. After the picking polygon was given by the user on screen,firstly,the adaptive octree division based on the distribution density of point cloud data was made,then the projection of the octree node was made and the octree projected polygon on the screen was formed,the rectangular bounding box of the picking polygon was established,then the intersection detection between octree projected polygon and rectangular bounding box of the picking polygon was executed,the point cloud of the octree node not intersected with the rectangular bounding box was removed,thereby the point cloud picking judgment was reduced and the picking efficiency was improved. Finally,a picking test under different point cloud distribution density was conducted. The results indicate that the greater the density of point cloud distribution,the picking time is relatively shorter,the algorithm has a high picking speed and accuracy.