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首页> 外文期刊>Photogrammetric Engineering & Remote Sensing: Journal of the American Society of Photogrammetry >An Efficient Point Cloud Management Method Based on a 3D R-Tree
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An Efficient Point Cloud Management Method Based on a 3D R-Tree

机译：基于3D R-Tree的高效点云管理方法

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摘要

Vehicle-borne laser-scanned point clouds have become increasingly important 3D data sources in fields such as digital city modeling and emergency response management. Aiming at reducing the technical bottlenecks of management and visualization of verylarge point cloud data sets, this paper proposes a new spatial organization method called 3dor-Tree, which integrates Octree and 3d R-Tree data structures. This method utilizes Octree's rapid convergence to generate R-Tree leaf nodes, which are inserteddirectly into the R-Tree, thus avoiding time-consuming point-by-point insertion operations. Furthermore, this paper extends the R-Tree structure to support lod (level of detail) models. Based on the extended structure, a practical data management methodis presented. Finally, an adaptive control method for wds of point clouds is illustrated. Typical experimental results show that our method possesses quasi-real-time index construction speed, a good storage utilization rate, and efficient visualizationperformance.
机译：车载激光扫描点云已成为数字城市建模和应急响应管理等领域中越来越重要的3D数据源。为了减少超大型点云数据集管理和可视化的技术瓶颈，本文提出了一种新的空间组织方法，称为3dor-Tree，该方法将Octree和3d R-Tree数据结构集成在一起。该方法利用Octree的快速收敛来生成R-Tree叶子节点，将其直接插入R-Tree中，从而避免了耗时的逐点插入操作。此外，本文扩展了R-Tree结构以支持lod（详细级别）模型。在扩展结构的基础上，提出了一种实用的数据管理方法。最后，说明了一种用于点云wds的自适应控制方法。典型实验结果表明，该方法具有准实时的索引构建速度，良好的存储利用率和高效的可视化性能。

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