## 4 Discussion  
This study addressed the lack of semantic information in 3D models of oblique photography. A semantic segmentation method was proposed based on 3D mesh data obtained from oblique photography. A point set mapping method was adopted to convert the mesh data into point cloud data, and rough geometric segmentation was performed simultaneously. We adopted a neighborhood search method based on geodesic distances in the local neighborhood computation of semantic segmentation. During feature selection and calculation, superpoint theory was used to fuse local features, realize the feature extraction of the segmented object, and reduce the computational load of machine learning. Finally, the oblique photographic model was partitioned into ground, trees, buildings, and others to segment the scene visually. Our proposed technique can advance the 3D semantic segmentation of oblique photography based on the successful implementation results. The semantic segmentation method proposed in this study for oblique photography 3D data was validated in Section 3.1. From the implementation results, this method can segment the continuously surfaced 3D mesh model into different land object types, which is beneficial for the subsequent processing and application of oblique photography 3D data. To acquire 3D building monomers and conduct contour extraction using semantically segmented oblique photography, our technique included hierarchical \"slicing\" of a 3D model followed by similarity assessments of polygon geometry. This method used a 3D mesh model as the object, projected the triangular meshes of buildings onto different elevation planes, and then processed mesh binarization and mesh vector conversion to extract initial contour sets for buildings. Finally, a polygon space similarity discrimination method calculated a single body and extracted the adopted building contour from the contour set data. The proposed method for individualizing building models based on oblique photography 3D data was validated in Section 3.2. The implementation results show that this method can accurately extract individual components from oblique photography 3D models. This is beneficial for the subsequent object-oriented management of oblique photography 3D models. This study also analyzed and evaluated the time efficiency of the proposed method. In terms of 3D semantic segmentation, the computation time of the model presented in this paper is approximately 98 s/km\({}^{2}\). For building individualization, the computation time of the method proposed in this paper is approximately 90 s/km\({}^{2}\). Based on an area of approximately 200 km\({}^{2}\) for a medium-sized city, it is estimated that the model in this paper will take around 10.4 h to complete 3D semantic segmentation and individualization. This processing time is acceptable in practical applications. Compared to existing methods for semantic segmentation and individual extraction of urban 3D models, this study exhibits several characteristics and innovations, including the following:  
(1) Regarding 3D semantic segmentation, this study utilized oblique photography-based 3D models as the data source and conducted point cloud processing, forming superpoints through clustering to reduce computational complexity. A search method based on surface model geodesic distance was proposed for feature neighborhood searches. It differs from traditional Euclidean distance clustering methods for discrete point cloud data by leveraging spatial topological information of mesh data and providing a more rational approach to spatial distance measurement;  
(2) In terms of monomer and contour extraction of oblique photography-based 3D architectural models, this study innovatively proposes an approach based on high-level plane segmentation and geometric similarity analysis.