Abstract: With the increasing demand for digitizing large-scale outdoor infrastructure, the method of deep learning-based Scan2BIM (Scanning to Building Information Modeling) substantially improves modeling precision and construction speed by leveraging its powerful feature learning capabilities and automated workflows, which play a crucial role in the 3D reconstruction of structurally complex outdoor scenes. This paper introduces the four main modules of Scan2BIM and their corresponding research advancements. Firstly, the development of 3D point cloud data acquisition is summarized from the perspectives of both acquisition devices and sources, with dedicated emphasis on the systematic organization and analytical evaluation of representative 3D point cloud datasets. Secondly, large-scale point cloud registration algorithms are systematically categorized into two principal classes, according to learning approaches, which are optimization-driven and deep learning-based, followed by the multidimensional comparative analysis of existing methodologies through critical performance metrics including alignment precision, computational efficiency, and operational robustness. Thirdly, the algorithms of point cloud segmentation for both point cloud panoramic segmentation and point cloud instance segmentation are analyzed based on standardized evaluation metrics. Fourthly, the core BIM interoperability standard framework is briefly outlined, and various geometric entity and relational modeling algorithms are categorized in the automated BIM modeling module. Finally, through in-depth analysis and prospective discussion, this paper pointed out the challenges in achieving a balanced integration of efficiency, accuracy, generalization, and uniformity for large-scale outdoor scene modeling. Future work will primarily focus on multi-source data fusion modeling, the synergistic optimization of accuracy and robustness, the construction of end-to-end general Scan2BIM frameworks, and the application and exploration of large models.