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收稿日期：2024-12-27，
修回日期：2025-02-19，
录用日期：2025-02-25，
网络出版日期：2025-02-26，
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城市3D Mesh模型解译是城市级实景三维建设的重要环节，有助于建筑设施、交通设施等城市设施的数字化和智能化、精细化管理，在城市更新、环境整治、城市仿真等城市行动中发挥积极作用。当前城市3D Mesh模型的语义化、单体化仍主要由人工勾勒实体轮廓，通过实体边界将每一个单独地物从城市3D Mesh模型中切割出来并赋予语义信息，然而城市3D Mesh模型通常是以瓦块的形式表达，在进行跨瓦块切割时容易让模型出现破碎、接缝、割裂等问题。为此，学者们开始研究基于深度神经网络的城市3D Mesh模型智能解译。然而，城市3D Mesh模型的智能解译却面临着巨大挑战，如城市3D Mesh模型不规则/非水密，传统卷积网络难以直接应用；城市3D Mesh模型多尺度特征获取困难等。虽然深度神经网络在城市3D Mesh模型解译方面的应用起步较晚，但该领域的研究依然取得了迅猛的发展。因此，本文以城市3D Mesh模型智能解译为主线，系统地回顾和总结了现有面向城市3D Mesh模型解译的深度神经网络方法，根据城市3D Mesh模型表达方式的不同，将面向城市3D Mesh模型解译的深度神经网络方法分为三类，即面向多视图表示的方法、面向质心点云表示的方法、面向3D Mesh模型元素的方法，并对这三类方法进行了详细比较和总结了当前面临的挑战；其次，梳理了城市3D Mesh模型智能解译常用的6个基准数据集，比较了多种方法在这些基准数据集中针对城市3D Mesh模型语义分割任务的性能表现；最后，对城市3D Mesh模型解译未来的发展方向和潜在应用前景进行了深入分析和讨论。
3D real scene serves as the spatial foundation and a unified spatial positioning framework and analysis basis for digital China. According to the content and hierarchy， 3D real scene can be categorized into terrain-level， city-level， and component-level. The city-level 3D real scene is mainly composed of 3D Mesh models derived from oblique photography， LiDAR point clouds， and texture images， which are semantically processed and integrated with real-time perception data. Urban 3D Mesh models are primarily composed of vertices， edges， triangular faces， and texture images. Compared to point clouds， 3D Mesh models not only display more detailed information about objects but also allow for easy control of the level of detail through adjusting the parameters of the 3D Mesh model. The focus of 3D real scene is on the digital mapping of production and living spaces， which can assist in the fine-grained management of cities and serve intelligent urban planning and construction. Interpreting 3D Mesh models of urban scenes， such as semantic segmentation and instance segmentation， is a crucial step in constructing city-level 3D real scene. Currently， the semantic segmentation and instance segmentation of urban 3D Mesh models primarily involves manually drawing the outline of object and cutting out each individual object from the 3D Mesh model using object boundaries， followed by assigning semantic information. However， urban 3D Mesh models are typically represented in a tile-based format， and cross-tile cutting can easily lead to issues such as fragmentation， seams， and discontinuities in the model. In recent years， deep learning technology has seen rapid development. Deep neural networks， due to their ability to learn discriminative high-level semantic features from given datasets， have been widely applied to the interpretation of image data and three-dimensional data （such as point clouds and 3D Meshes）. Additionally， with the continuous improvement in the performance of graphics processing units （GPUs） and the expansion of annotated datasets， the accuracy of deep neural networks in interpreting 2D images and 3D data has significantly improved. Despite significant progress in the interpretation of 3D Mesh models， most of these studies have focused on small-scale， toy， and simulated 3D Mesh models. Research on deep neural networks for interpreting complex urban 3D Mesh models is still in its early stages and faces many challenges and difficulties， primarily in the following three aspects： 1） Urban 3D Mesh models are often irregular and may contain holes or be non-watertight， making it difficult for traditional deep neural networks to directly apply to these models to extract highly discriminative features. 2） The efficiency of extracting multi-scale feature is low. Traditional 3D Mesh simplification methods （such as quadric error metrics）， which are used to generated hierarchy 3D Mesh， use greedy strategies which are hard to parallelize. When processing large-scale urban 3D Mesh models， these methods inevitably increase computational burden. 3） Compared to benchmark datasets for images and point clouds， publicly available benchmark datasets for urban 3D Mesh models are scarce. The structures of buildings， roads， and vegetation in urban scenes are complex and varied， making the annotation of urban 3D Mesh models not only require specialized knowledge but also consume a significant amount of time and human resources. Compared to the intelligent interpretation of images and point clouds， the application of deep neural networks in the interpretation of urban 3D Mesh models started later but has still seen rapid development. However， there are currently few review articles that systematically explore and summarize how different deep neural network architectures achieve the interpretation of urban 3D Mesh models. Therefore， this paper aims to systematically review and summarize existing deep neural network methods for interpreting urban 3D Mesh models and to highlight the open challenges currently faced by researchers， providing a reference for future research. For this purpose， we first survey vast literatures， and categorize the intelligent interpretation methods for urban 3D Mesh models into three classes， according to the types of representations used in processing urban 3D Mesh models： 1） Methods based on multi-view images attempted to project 3D Mesh models into 2D images from multiple viewpoints and used well-established 2D image deep learning methods to learn discriminative semantic features from the projected images. Subsequently， the semantic features learned from the projected images were mapped back to the 3D Mesh models. 2） Methods based on center of gravity （COG） point cloud representation convert each face of the urban 3D Mesh model into its COG point， thereby abstracting the entire 3D Mesh model into COG point clouds. Subsequently， intelligent interpretation algorithms designed for point clouds are used to process these COG point clouds. Unlike traditional point clouds， COG point clouds can inherit rich texture and geometric information from the urban 3D Mesh model. 3） Methods based on 3D Mesh elements aim to define learnable operations （such as convolution and pooling） directly on the 3D Mesh elements （vertices， edges， triangular faces）. This approach allows for the direct learning and extraction of rich high-level semantic features from the urban 3D Mesh model， thereby avoiding information loss that can result from preprocessing steps such as multi-view image projection and centroid point cloud abstraction. Subsequently， we conduct a detailed comparison of the four categories of methods and summarized their current challenges. Furthermore， we also summarize commonly used datasets for intelligent interpretation of urban 3D Mesh models and compare the interpreting performance of different methods on these datasets. Finally， based on the systematic survey and comprehensive performance comparison， we discussed some promising future research directions from aspects such as dataset creation， 3D large model construction， and application scenarios.
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