节点文献
一种四叉树与KD树结合的海量机载LiDAR数据组织管理方法
A Method of Combing the Model of the Global Quadtree Index with Local KD-tree for Massive Airborne LiDAR Point Cloud Data Organization
【摘要】 针对海量机载LiDAR点云数据管理与可视化效率不高的问题,提出了一种四叉树和局部KD树相结合的混合空间索引结构以及内外存结合的数据调度模式。在全局,可以通过四叉树金字塔模型实现快速检索与调度;在局部,通过内存中构建的KD树实现高效的查询与显示。利用敦煌地区约10亿点的激光雷达数据进行了验证,达到30帧/s的显示效率,为大规模点云数据的可视化奠定了基础。
【Abstract】 This paper proposed a kind of hybrid index structure for organizing airborne LiDAR point cloud data to solve the problem of large data organization and visualization,which combines the global quadtree with local KD-tree.The global quadtree is used to index the upper level of point cloud data in global area.The KD-tree is used to index the data in a leaf of quadtree.This method not only can organize massive point cloud data,but also guarantee the indexing efficiency.To test our method,experiments using 1billion points of Dunhuang area are conducted,which reach a 30 frame rate visualization speed,providing aprimary step for large point cloud visualization.
【Key words】 LiDAR; point clouds; quadtree; KD-tree; hybrid index structure;
- 【文献出处】 武汉大学学报(信息科学版) ,Geomatics and Information Science of Wuhan University , 编辑部邮箱 ,2014年08期
- 【分类号】P225.1
- 【被引频次】46
- 【下载频次】708