基于改进八叉树的三维点云压缩算法 下载: 1522次
Three-Dimensional Point Cloud Compression Algorithm Based on Improved Octree
1 东南大学自动化学院, 江苏 南京 210096
2 复杂工程系统测量与控制教育部重点实验室, 江苏 南京 210096
摘要
针对大数据环境下,三维模型的传输和存储需求,提出了一种基于八叉树的三维点云有损压缩算法。该算法改进了八叉树分割的停止条件,可以在适当的深度停止分割并确保体素大小合适。同时在分割的基础上通过建立K邻域,利用简单有效的统计方法去除原始点云的离群点。在数据结构上,对每个节点分配位掩码,通过操纵位掩码,在遍历时对数据查询和操作,并优化随后的点位置编码。该算法可以有效地移除离群点和表面杂点,并在区间编码上提高了点云压缩效率。实验结果表明,该算法较完整地保留了三维点云数据的关键信息,取得了良好的压缩率并缩短了压缩时间。
Abstract
Aiming at the transmission and storage requirements of three-dimensional model in the large data environment, a three-dimensional point cloud lossy compression algorithm based on the octree is presented. The stop condition of the octree segmentation is improved, so the segmentation can be stopped at an appropriate depth, and the proper size of voxel is ensured. At the same time,the K neighborhood is established based on the segmentation and the outliers of original point cloud are removed by simple and effective statistical method. In the data structure, each node is assigned to a bit mask. The data query and manipulation are traversed by manipulating the bit mask. Then the subsequent point position coding are optimized. The proposed algorithm effectively removes the outliers and miscellaneous points on the surface, and improves the efficiency of point cloud compression in range encoding. The experimental results show that this algorithm can preserve the key information of three-dimensional point cloud data more completely, obtain a good compression rate and shorten compression time.
黄源, 达飞鹏, 唐林. 基于改进八叉树的三维点云压缩算法[J]. 光学学报, 2017, 37(12): 1210003. Yuan Huang, Feipeng Da, Lin Tang. Three-Dimensional Point Cloud Compression Algorithm Based on Improved Octree[J]. Acta Optica Sinica, 2017, 37(12): 1210003.