## 2 Related Works  
Many researchers have explored methods to detect leakage using tunnel point cloud data, which can be broadly categorized into two main approaches:  
1. Intensity threshold-based segmentation methods. These methods convert point clouds into grayscale images by utilizing the intensity values of laser scanning points [7; 9; 10]. Leakage is detected by establishing intensity thresholds derived from the difference between leakage and non-leakage regions in grayscale domain [11; 12]. For example, [PERSON], et al. [7] employed [PERSON]'s method to compute grayscale histogram and determine an optimal intensity threshold that maximizes inter-class variance between leakage and non-leakage areas, achieving optimal segmentation results. However, point cloud intensity is susceptible to biases induced by factors such as scanning distance, incidence angle, and surface roughness. [PERSON] and [PERSON] [13] investigated these influences, emphasizing the necessity of intensity correction models to mitigate such deviations [14]. [PERSON], et al. [15] proposed a two-step approach: first, correcting intensity values based on scanning distance and incidence angle; second, employing an intensity threshold to extract leakage and a distance threshold to eliminate noise points. Despite their effectiveness, these methods exhibit notable limitations. The segmentation process is highly sensitive to the intensity threshold, which is typically determined empirically or heuristically. This approach may not generalize well to diverse datasets, limiting its practical applicability. Furthermore, most implementations are confined to 2D leakage segmentation, lacking the capability to provide detailed 3D information regarding the depth and spatial extent of leakages. 2. Image-based supervised classification methods. These methods also involve transforming point clouds into grayscale images. Annotated samples are used to train deep learning models, which are then tested on grayscale images to detect leakage locations and areas. Recent advancements in convolutional neural networks (CNNs) have garnered attention for their capability to efficiently extract features. Deep learning frameworks derived from CNNs, such as ResNet [16], Faster R-CNN [17], R-FCN [18], Mask R-CNN [19], DeepLabV3+ [20], and YOLO [21], have been extensively applied to various object detection tasks. These tasks include crack detection [22], defect detection [23], leakage detection [24], etc. For example, [PERSON], et al. [25] combined Res2 Net with cascade modules and fully connected networks (FCNs) to detect leakage, leveraging multi-scale feature extraction and enhanced representation. Among these, Mask R-CNN, one of the most widely used algorithms for leakage instance segmentation, extends Faster R-CNN by integrating Region of Interest (RoI) Align [26] and FCN [27] modules. To enhance the segmentation accuracy, [PERSON] et al. [1] employed RDES-Net to effectively segment leakage on grayscale images. [PERSON] et al. [28] proposed an enhanced YOLO-V7 model that integrates attention mechanisms, edge refinement techniques, and mixed data augmentation strategies to achieve precise leakage segmentation. [PERSON] et al. [29] designed a lightweight leakage segmentation method using the DeepLabV3+ model with integrated channel attention, demonstrating enhanced accuracy and generalization performance in complex environments. To visualize leakage in 3D space, [PERSON] et al. [19] introduced a method to unfold point clouds into grayscale images using cylindrical voxels, enabling Mask R-CNN-based detection and subsequent mapping back to 3D space. Additionally, [PERSON] et al. [30] applied SfM-Deep Learning to map leakage textures onto 3D models, further enhancing spatial analysis capabilities. A comparative summary of these representative leakage detection methods is presented in Table 1, which details their utilized tunnel structures, data acquisition equipment, input data types, core techniques, reported accuracies, and key limitations.