Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. BIM models designed from software are rich in geometrical information but often lack accurate geo-referenced locations. In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes. A state-of-the-art deep learning framework and graph theory are first combined for LiDAR point cloud segmentation. A coarse-to-fine matching program is then developed to integrate object point clouds with corresponding BIM models. Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of BIM models are 0.023 m for pole-like objects and 0.156 m for buildings, demonstrating the effectiveness of the method in segmentation and matching processes. This work offers a practical solution for rapid and accurate urban GeoBIM construction.

翻译：三维现实世界的发展推动了地理信息与建筑信息模型（BIM）的融合，即城市建造中的GeoBIM。集成全球导航卫星系统的光探测与测距（LiDAR）可提供地理参考空间信息。然而，构建精细化城市GeoBIM面临LiDAR数据质量的挑战。软件设计的BIM模型虽富含几何信息，但缺乏精确的地理参考位置。本文提出一种互补策略，通过将LiDAR点云与设计BIM模型融合，实现城市场景重建。首先结合前沿深度学习框架与图论进行LiDAR点云分割，继而开发粗到细匹配程序，将目标点云与对应BIM模型整合。结果表明，LiDAR数据集整体分割精度达90%，杆状物体与建筑物的BIM模型平均定位精度分别为0.023米和0.156米，验证了该方法在分割与匹配过程中的有效性。本研究为快速精准的城市GeoBIM构建提供了实用方案。