摘要
车载激光扫描点云数据已经成为数字城市和危机管理等领域越来越重要的三维空间信息源,针对大规模点云数据高效管理的技术瓶颈,提出一种八叉树和三维R树集成的空间索引方法——3DOR树,充分利用八叉树的良好收敛性创建R树叶节点,避免逐点插入费时过程,同时R树平衡结构保证良好的数据检索效率。并还扩展R树结构生成多细节层次(LOD)点云模型,提出一种支持缓存的多细节层次点云数据组织方法。试验证明,该方法具有良好的空间利用率和空间查询效率,支持多细节层次描述能力和数据缓存机制,可应用于大规模点云数据的后处理与综合应用。
Vehicle-borne laser point cloud data has become key 3D spatial information source in fields such as digital city and crisis management.Aiming at technical bottleneck of large-scale point cloud data management,a new spatial index method-3DOR-tree is presented,which integrates octree and 3D R-tree.This method utilizes octree to forbid point-by-point insertion and generates leaf nodes of R-tree efficiently.R-tree structure is extended to present levels of detail（LOD） generation algorithm of point cloud models.Finally,a data organization approach is put forward for large-scale point cloud,which easily uses file mapping technique to accelerate data access.Experiments prove that this approach has fine space utilization and spatial query efficience with LOD representation capability and data cache mechanism,which lays a solid foundation for post-processing and comprehensive practices of large-scale point cloud data.
出处
《测绘学报》
EI
CSCD
北大核心
2012年第4期597-604,共8页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(41001222)
国家973计划(2010CB731801
2011CB302306)
国家863计划(SS2012AA121001)
江西师范大学青年英才培育资助计划
江西省教育厅科技项目(GJJ12188)
关键词
八叉树
三维R树
激光扫描
点云
数据管理
octree； 3D R-tree； laser-scanning； point cloud； data management
