Change detection between as-planned building information modeling (BIM) and the as-is point cloud requires significant computational overhead because it must deal with every geometric face in the BIM and every point in the point cloud one-to-one. To address this problem, this study presents a high-performance algorithm to detect discrepancies between an as-planned BIM and the as-is point cloud automatically. This method is a data structure approach based on modifiable nested octree indexing of surface meshes and point clouds. The results of experiments showed a significant computation performance improvement: 25.3 and 12.1 times faster than the baseline method for a complex plant facility and a simple indoor building, respectively. Furthermore, it was demonstrated that as the number of meshes in the BIM geometry increased, the time complexity of the proposed approach could be represented as a big O-notation,O(logN), where N is the number of meshes in the BIM geometry.

规划建筑信息模型 (BIM) 和原样点云之间的变化检测需要大量计算开销，因为它必须一对一地处理 BIM 中的每个几何面和点云中的每个点。为了解决这个问题，本研究提出了一种高性能算法来自动检测计划的 BIM 和原样点云之间的差异。该方法是一种基于表面网格和点云的可修改嵌套八叉树索引的数据结构方法。实验结果显示计算性能显着提高：分别比复杂工厂设施和简单室内建筑的基线方法快 25.3 和 12.1 倍。此外，还证明随着 BIM 几何体中网格数量的增加，O (log N )，其中N是 BIM 几何体中的网格数。