# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 4 Discussion  
The tunnel point cloud structural disease detection technology based on mobile three-dimensional laser scanning equipment proposed in this study effectively enhances the intelligence and automation level of tunnel inspection. The algorithm relies solely on point cloud coordinate data, successfully extracting structural information from tunnels and performing comprehensive deformation detection, yielding good results on engineering data. However, both the TMatch technology and ring plane fitting technology depend on the GuSAC algorithm to remove non-structural point clouds from the tunnel. This chapter analyzes the ability of the GuSAC algorithm to remove non-tunnel structural point clouds from local planes, further demonstrating the feasibility of the proposed method. To validate the reliability of the proposed method, we compared the performance of the Least Squares Method, RANSAC, surface fitting, and GuSAC in tunnel structural point cloud recognition. We quantified their extraction capabilities in local regions using Chamfer Distance (CD) and RMSE. CD is a distance metric that measures the difference between the object edges in the point cloud and the predefined model. Considering that slight deformations may occur during tunnel operation, directly using the design axis as a reference might introduce errors, while removing non-tunnel structural point clouds could lead to human errors. Therefore, we manually removed non-tunnel structural point clouds from predefined local point clouds, established a standard dataset, and compared it with the algorithm output to assess the performance of GuSAC. In this paper, 10 local point clouds are extracted from the tunnel, and the average values of statistical indicators are calculated. The local point clouds are divided into Regular Local and Irregular Local categories, with some local point clouds shown in Figure 20. Since the tunnel point cloud data have been normalized during the calculation, the statistical indicators serve only as relative references, without measurement units. Detailed statistical results are presented in Table 3.  
Figure 20: Schematic diagram of a local point cloud of the tunnel. From the statistical indicators, in Regular Local, all four algorithms demonstrate good capability in removing non-tunnel structural point clouds, with CD values (center offset errors) and RMSE (Root Mean Square Error) being relatively small. This indicates that they can effectively fit the tunnel lining plane, and the overall error is well controlled. However, in Irregular Local, only RANSAC and GuSAC are able to accurately identify the tunnel structural point cloud plane, while the CD values and RMSE errors of the Least Squares Method and surface fitting methods significantly increase. This suggests that these two methods experience larger errors in the processing of complex structures and fail to effectively remove non-structural point clouds. The higher metric values of GuSAC compared to RANSAC may stem from errors in the manually created standard data. It is evident that using only statistical indicators to evaluate the actual removal capability of segmentation algorithms has certain limitations, as numerical metrics cannot fully reflect the true geometric features and spatial distribution of the point clouds. Therefore, this study further incorporates visual analysis to visually assess the algorithm's ability to retain tunnel structural point clouds and remove non-structural point clouds by comparing the point cloud distribution after processing. The specific results can be seen in Figure 21.  
Due to the significant impact of noise points on the Least Squares Method and surface fitting for surface recognition, these methods are highly susceptible to disturbances from irregular point clouds, resulting in poor visualization quality. Therefore, they are not included in the visualization comparison.