基于八叉树编码的点云邻域搜索算法

Point cloud neighborhood search algorithm based on octree coding

针对散乱空间点云数据没有明显拓扑结构的问题,为提高其数据处理速度,提出一种快速搜寻邻近点集的算法。根据点云数据的范围、点的总数确定合适立方体包围盒,采用空间三方向二分划分方法,将包围盒划分成许多子立方体,应用二进制编码表对子立方体中每个数据点建立索引号,给出新的方法对数据点进行再编码,确定邻近点的最佳搜索范围。实验结果表明,该算法能显著提高大规模散乱空间点云邻近点的搜索效率,保证搜索结果的可靠性。

To improve the speed of data processing,a fast search algorithm for adjacent point set was proposed.According to the scope of point cloud data and the total number of points,the appropriate cube bounding box was determined.The bounding box was divided into many subcategories using spatial three-direction dichotomy,and the binary code table was used to establish the index number for each data point in the sub-cube.A new method was given to re-encode the data points to determine the optimal search range of the neighboring points.Experimental results show that the proposed algorithm can improve the search efficiency of the adjacent cloud point and ensure the reliability of the search results.