Hierarchical tunnel modeling from 3D raw LiDAR point cloud. (September 2019) - Yi, Cheng; Lu, Dening; Xie, Qian; Liu, Shuya; Li, Hu; Wei, Mingqiang; Wang, Jun
Abstract: Precise modeling of tunnel structures can be used to evaluate the performance and state of safety as an important construction analysis object. However, the problem of modeling on a large-scale tunnel is challenging, due to the poor-quality of the input data that is contaminated with severe missing areas, noise, outliers and the accessories. This work introduces the concept of hierarchical modeling to automatically derive a measured and accurate representation of a 3D shield tunnel. Our key observation is that the shield tunnel is composed of a certain number of segment pieces arranged in some designed patterns. We hereby apply hierarchical segmentation and element extraction, based on designed patterns, to intensity images without expensive computation. According to the corresponding relationship between the point cloud and the image, the segment pieces within the point cloud are extracted. Therefore, we construct the corresponding 3D models of the segment pieces to obtain the precise structure model of the tunnel. As a result, segment pieces are extracted robustly, even in the presence of noise and large occlusion. Quantitative and qualitative comparisons of the proposed algorithm are presented with state-of-the-art methods. We evaluate the modeling method on a variety of raw LiDAR scans, in terms of its robustness and accuracy. Moreover, our method has been successfully applied in a number of practical projects to discover the multi-level structures of the shield tunnels, which are used to evaluate the safety state of tunnels. Highlights: We propose an framework to model the tunnels from 3D raw LiDAR point clouds. We design a hierarchical method to detect and locate repetitions of the tunnel. We devise an image- and geometry-collaborative modeling approach on point clouds.