针对高速公路勘察设计中不规则海量激光点云数据高效管理的技术难题,提出一种基于四叉树和KD树两级索引相互集成的数据组织管理方法。首先,根据该方法生成多细节层次(LOD)点云模型,然后,利用双缓冲机制和动态调度方法,实现公路海量点云数据的高效管理和多尺度显示。试验结果表明,该方法能有效地对点云数据进行高效的可视化管理,便于大规模点云数据的后处理和应用。 In order to solve the technical problem of irregular massive laser point cloud data management in expressway survey and design, a data organization and management method based on four levels of quadtree and KD tree is proposed. First, a multi-level-of-detail (LOD) point cloud model is generated according to this method. Then, double buffer mechanism and dynamic scheduling method are used to achieve efficient management and multi-scale display of massive cloud data on the highway. The experimental results show that this method can effectively manage the point cloud data efficiently and facilitate the post-processing and application of large-scale point cloud data.