# Automated Tunnel Point Cloud Segmentation and Extraction Method
[17] introduced a method for extracting shield tunnel cross-section deformation from 3D point cloud data. This approach fits segment outlines using Fourier functions and calculates the displacement field of mesh nodes, allowing for highly accurate lining deformation feature extraction. Experimental and simulation validation demonstrated the high reliability of this method, making it suitable for practical engineering deformation detection. [PERSON] et al. [18] proposed a tunnel cross-section fitting and extraction method based on grayscale maps, which was applied to Suzhou Metro tunnels. By extracting horizontal convergence values and ellipticity from specific cross-sections and comparing them with total station measurement results, the accuracy and efficiency of the method were verified. Compared to traditional measurement methods, 3D laser scanning significantly improves the efficiency and comparability of long-term tunnel monitoring, aiding in the assessment of tunnel operational status and long-term deformation trends. However, the most commonly used cross-section extraction method still involves slicing the tunnel by taking normal vector planes along the designed tunnel axis and extracting point cloud data of a certain thickness. This method, however, requires highly accurate designed axis extraction. For this reason, many scientists have studied the extraction of the tunnel's central axis. For example, [PERSON] et al. [19] proposed an automatic highway tunnel cross-section extraction method based on 3D laser scanning. The method estimates the tunnel centerline using control network registration, point cloud projection, and skeletonization, then generates cross-section planes at intervals. [PERSON] et al. [20] introduced a tunnel deformation measurement method based on 3D point cloud data. By extracting the designed tunnel axis using 3D invariant moments, the method enables tunnel pose correction and cross-section feature extraction. Experimental validation demonstrated an accuracy of up to 2 mm, making it suitable for tunnel 3D point cloud analysis and information extraction. [PERSON] et al. [21] proposed a denoising algorithm for inclined tunnel point cloud data based on irregular contour features. This algorithm combines DBSCAN clustering and polynomial curve fitting to obtain continuous point cloud slices perpendicular to the inclined tunnel centerline, enabling the extraction of irregular tunnel inner wall shapes. [PERSON] et al. [22] introduced an automatic tunnel centerline and cross-section extraction method based on 3D laser scanning data. By constructing a k-dimensional tree (k-d tree) index using horizontal projection, the method calculates centerline points and extracts cross-section parameters, ultimately generating a 3D tunnel model for deformation visualization analysis. Experimental results showed that the minimum horizontal centerline extraction error was 0.011 cm, demonstrating high accuracy and a significant reference value. To achieve refined and intelligent tunnel management, relying solely on overall cross-section analysis is no longer sufficient to meet monitoring needs. There is an urgent need for precise segmentation of various tunnel lining structures to enable independent identification and monitoring of different lining units. This not only helps evaluate the structural health of the tunnel lining but also provides efficient and reliable technical support for localized disease detection, deformation analysis, and maintenance decision-making. Currently, segmentation is primarily performed through image and point cloud registration. For example, [PERSON] et al. [23] proposed a layered modeling method for accurately modeling 3D shield tunnel structures. This method segments point cloud data based on design patterns, extracts tunnel segment fragments using intensity images, and constructs an accurate 3D model. The algorithm can robustly extract tunnel segments even in the presence of noise and occlusions. [PERSON] et al.