Enhancing Scan-to-BIM in Bridge Engineering Through Point Cloud Completion

Automatic generation of Building Information Models (BIMs) from point clouds (i.e., Scan-to-BIM) plays a critical role in bridge maintenance and the development of Digital Twinning (DT). However, the problem of incomplete point cloud (e.g., caused by occlusions in laser scanning) significantly hinders the Scan-to-BIM accuracy. To overcome this challenge, we propose addressing the occlusion problems in bridge point clouds by introducing an additional point cloud completion task in the Scan-to-BIM process. This new task aims to take incomplete bridge point clouds, following segmentation, as input, and generate complete point clouds as output. The learning-based completion model, Point Completion Network (PCN), is adopted to validate the proposed strategy and show robust completion performance for bridge components with varying levels of occlusion. It can improve the average 11.92 Chamfer Distance (CD) and 11.82 F-score for coarse completion, 8.89 CD and 6.95 F-score for dense completion. This study contributes to Scan-to-BIM by refining the Scan-to-BIM framework in bridge engineering and defining a point cloud completion task to facilitate the development of bridge DT systems.