3DB-ROC Data Structure Organization for Three-Dimensional Building Models

With the rapid development of technologies such as smart city, digital twin, and metaverse, the data volume of three-dimensional building models is experiencing explosive growth. To address the issue of efficient organization and management of three-dimensional building model data, this paper proposes a 3DB-ROC data structure organization method for three-dimensional building models. This method combines the advantages of R-tree and octree. Firstly, using a single building as the basic unit, the R-tree organizes the 3D building model data in a coarse-grained manner. Then, in a fine-grained manner, the octree is employed to organize the individual building model data within a single building. This hierarchical strategy minimizes tree depth, balances structure, and boosts data handling and query performance. The proposed method was evaluated using three-dimensional building model data in the metaverse platform, and the experimental results show that: (1) It outperforms R-tree, octree, KD-tree, BVH, and 3DOR in construction and insertion speed ; (2) It achieves faster region retrieval than octree, particularly for small areas; (3) the method meets the requirements of efficient organization and management of three-dimensional building models, especially in applications that require frequent construction and insertion of three-dimensional building model data.