###### Abstract  
Existing 3D city reconstruction via oblique photography can only produce surface models, lacking semantic information about the urban environment and the ability to incorporate all individual buildings. Here, we propose a method for the semantic segmentation of 3D model data from oblique photography and for building monomer construction and implementation. Mesh data were converted into and mapped as point sets clustered to form superpoint sets via rough geometric segmentation, facilitating subsequent feature extractions. In the local neighborhood computation of semantic segmentation, a neighborhood search method based on geodesic distances, improved the rationality of the neighborhood. In addition, feature information was retained via the superpoint sets. Considering the practical requirements of large-scale 3D datasets, this study offers a robust and efficient segmentation method that combines traditional random forest and Markov random field models to segment 3D scene semantics. To address the need for modeling individual and unique buildings, our methodology utilized 3D mesh data of buildings as a data source for specific contour extraction. Model monomer construction and building contour extractions were based on mesh model slices and assessments of geometric similarity, which allowed the simultaneous and automatic achievement of these two processes.  
oblique photography; 3D semantic segmentation; machine learning; building monomer  
of 3D scenes and models to remedy this situation. Proposed methods include semantic classification based on manual annotation [5; 6], a 3D point cloud classification based on deep learning [7; 8; 9; 10; 11], semantic segmentation of imagery based on dimension reduction [12; 13], and a semantic classification based on 3D voxels [14; 15]. However, mature and effective theoretical methods, technical systems, and software platforms have not yet been developed for semantic segmentation of urban 3D scenes acquired via oblique photography.  
Due to its modeling principles and technical limitations, a model constructed using oblique photography presents a continuous whole, such as a triangular mesh. This is commonly known as a \"surface\" model, which cannot distinguish between individual buildings in geometric space and does not allow the objectified management of such models. To solve this challenge, it is often necessary to conduct monomer processing in subsequent stages [16; 17] via physical cutting [18], dynamic labeling based on 2D vector graphics, manual ID labeling, and semiautomatic model replacement. However, in practice, the effect of these methods is not ideal, and they require considerable manual intervention or rely on third-party data sources (such as planning, or the construction management data related to building outlines). That is, it is difficult to satisfy the requirements of objectification without building outline data and a high degree of automation. Developing effective automated monomer processing in 3D models established via oblique photography is critical for achieving these goals.  
This study explored methods for semantic classification, automatic individualization, and contour extraction using 3D model data from oblique photography. It proposes corresponding implementation paths and specific algorithm procedures and conducts experimental analysis. This research aimed to explore a beneficial method for processing and applications in urban 3D reconstruction using oblique photography. This study reflects innovation in the point set processing of oblique photography 3D data, super-point construction, and feature fusion. A unique method for elevation plane cutting and polygon similarity determination is proposed for building model monomers, which can simultaneously achieve model monomer and contour extraction without relying on any third-party data.