水利水电技术（中英文）2024，Vol.55Issue(12)：159-170,12.DOI:10.13928/j.cnki.wrahe.2024.12.013
大规模工程点云数据的归并式八叉树管理及可视化
Merging-based hierarchical octree management and visualization for large-scale engineering point clouds
摘要
Abstract
[Objective]With the continuous advancement of intelligent water conservancy,3 D laser scanning technology has at-tracted significant attention due to its comprehensive monitoring capabilities,rapid scanning speed,and high precision.Howev-er,these point clouds,characterized by massive scale,unstructured nature,and uneven density,present formidable challenges in engineering data management and visualization.Aiming at the limitations of the"top-down,step-by-step subdividing"point cloud indexing construction method,which are not well-suited for massive point cloud chunking or multi-point cloud input scenar-ios,a"bottom-up,small-to-large"octree construction method for massive point data management and fast point cloud rendering was proposed.[Methods]First,a global voxel-based indexing and encoding conversion system is established based on the global information of point clouds.This framework facilitates the rapid octree indexing for massive point cloud data through operations such as point-by-point allocation and grid merging,which are executed in parallel by multi-threaded processing.Furthermore,a non-redundant sampling strategy is adopted to build the LOD(level of details)models,coupled with multi-threaded dynamic scheduling technology,to achieve high-quality visualization and rendering of massive point clouds.[Results]Experimental result demonstrated that the merge-based construction strategy could handle point cloud data exceeding 5 billion points.The peak mem-ory consumption during the indexing process was 2.78 GB,and the processing efficiency reached 2 million points per second.Compared to point cloud processing tools like Point Cloud Library(PCL)and CloudCompare,the proposed method exhibited superior performance in memory management and indexing struct construction efficiency,as well as point cloud rendering.[Conclusion]The management and visualization of large-scale engineering point cloud data are fundamental to advancing the full-process research of engineering point cloud monitoring.The key lies in optimizing the rapid construction of spatial indexes for ultra-large point cloud datasets.The"subdivide first,merge later"processing strategy effectively alleviates spatial overlap con-flicts in chunk data,with clear algorithm logic,making it particularly suitable for index structures based on spatial subdivision mappings such as quadtrees and octrees.关键词
三维激光扫描/海量点云/八叉树构造/内外存动态调度/归并算法/多层次细节模型/水利工程/数字孪生Key words
3D laser scanning/massive point cloud/octree/internal and external memory dynamic scheduling/merge algo-rithm/level of details/water conservancy/digital twin分类
建筑与水利引用本文复制引用
张宏阳,金银龙,张礼兵,刘全,王浩,庞小荣..大规模工程点云数据的归并式八叉树管理及可视化[J].水利水电技术（中英文）,2024,55(12):159-170,12.基金项目
云南省科技厅重大科技专项计划(202202AF080003) （202202AF080003）
国家自然科学基金项目(51879207) （51879207）
西藏自治区清洁能源科技重大专项(XZ202201ZD0003G01) （XZ202201ZD0003G01）
