Joint Structure Detection and Multi-Scale Clustering Filtering for Tunnel Lining Extraction From Point Clouds

Accurate extraction of tunnel lining is crucial for deformation monitoring, damage detection, and tunnel modeling. However, the extraction of tunnel lining is still challenging due to the different tunnel shapes and complicated construction scenes. As for tunnel point clouds, inconsistent local density and non-structural features also pose challenges. Previous research has shown that utilizing the centerline of a tunnel is the preferred method for extracting tunnel lining. However, imprecision often arises due to noise points and point cloud hole. To address these challenges, we propose a novel extraction framework for various tunnel shapes that seldom depends on the centerline. First, the structure detection module is designed to detect the lining structure from the raw point cloud. Following that, multi-scale clustering filtering is applied for fine extraction. The global clustering filtering procedure is then implemented to process the projected point cloud. Finally, the local filtering approach is created for confusion clustering. Experimental results show that the highest Kappa coefficient of the proposed method is 95.5%. Compared with the elliptic cylinder method, the angle threshold method, and the template method, the extraction accuracy of our method is improved by 6.2%, 10.9%, and 9.7%, respectively.