## 2 Literature Review
### Three-Dimensional Laser Scan Techniques  
With the advancement of laser scanning and photogrammetry technologies, the use of 3D point cloud data in bridge inspection has grown. Three-dimensional point cloud data provides rich spatial information, contributing to enhanced crack detection accuracy. Common methods include point cloud registration, feature extraction, and crack detection. Research on point cloud-based crack identification focuses on preprocessing, normal vector computation, and curvature analysis to detect crack locations. By leveraging the spatial information contained in point cloud data, this method significantly improves crack detection precision. Additionally, by using segmentation and registration techniques combined with crack feature extraction, high-precision bridge crack detection can be achieved.  
Actually, 3D point cloud data have become increasingly important in various fields such as measurement, compression, segmentation, feature extraction, object detection, and construction infrastructure applications. [PERSON] et al. (2019) demonstrated the role of unsupervised learning in denoising 3D point clouds, emphasizing the importance of spatial priors in effective denoising processes [24]; [PERSON] et al. (2020) accurately and rapidly measured gears using 3D point cloud data, showcasing the advantages of non-contact measurement systems [25]; and [PERSON] et al. (2020) proposed a deep learning framework for point cloud geometric loss compression [26], achieving significant bitrate savings compared to the existing compression methods. On the other hand, [PERSON] et al. (2020) introduced a weakly supervised method for the semantic segmentation of 3D point clouds, highlighting the importance of using weak labels at the scene and sub-point cloud levels [27]. [PERSON] et al. (2021) proposed a robust method for extracting features from plant point clouds, which also provides insights for extracting features from bridge model point clouds [28]. [PERSON] et al. (2021) proposed a 3D object detection semantic segmentation model based on transfer learning, aiming to reduce the need for large-scale training datasets and training time [29]. Additionally, [PERSON] et al. (2022) reviewed the processing of 3D point cloud data for construction infrastructure applications and machine learning, highlighting the potential of machine learning techniques [30]. In terms of computer vision applications, [PERSON] et al. (2022) outlined various computer vision tasks on 3D point cloud data, discussing the latest technologies and datasets used in these tasks [31]; [PERSON] et al. (2022) introduced masked autoencoders for 3D point cloud representation learning, emphasizing self-supervised learning and methods for completing incomplete point clouds [32]. From denoising and compression to segmentation and feature extraction, 3D point cloud data have long-term development and research conditions for researchers, and this paper will also complete the practical application research on the potential of 3D point cloud data by integrating the above review content.  
The 3D laser scanning technology utilized in this study is characterized by its high resolution and accuracy, enabling the capture of the intricate details of bridge structures. With a considerable scanning range, it efficiently captures large-scale environments, such as bridge spans, in a single operation. The speed at which data are acquired is particularly advantageous for projects requiring rapid data collection. The scanner's robustness in various environmental conditions ensures its utility in field settings. The scanners' capabilities in processing large datasets into usable formats, including real-time data processing and visualization, are crucial for timely analysis. Furthermore, the portability and ease of use of the scanner make it an ideal tool for fieldwork.