3D shape generation techniques utilizing deep learning are increasing attention from both computer vision and architectural design. This survey focuses on investigating and comparing the current latest approaches to 3D object generation with deep generative models (DGMs), including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), 3D-aware images, and diffusion models. We discuss 187 articles (80.7% of articles published between 2018-2022) to review the field of generated possibilities of architecture in virtual environments, limited to the architecture form. We provide an overview of architectural research, virtual environment, and related technical approaches, followed by a review of recent trends in discrete voxel generation, 3D models generated from 2D images, and conditional parameters. We highlight under-explored issues in 3D generation and parameterized control that is worth further investigation. Moreover, we speculate that four research agendas including data limitation, editability, evaluation metrics, and human-computer interaction are important enablers of ubiquitous interaction with immersive systems in architecture for computer-aided design Our work contributes to researchers' understanding of the current potential and future needs of deep learnings in generating virtual architecture.

翻译：利用深度学习的三维形状生成技术正日益受到计算机视觉和建筑设计领域的关注。本综述聚焦于调查并比较当前利用深度生成模型（DGMs）进行三维物体生成的最新方法，包括生成对抗网络（GANs）、变分自编码器（VAEs）、三维感知图像以及扩散模型。我们讨论了187篇文章（其中80.7%发表于2018-2022年），以回顾虚拟环境中建筑生成可能性的研究领域，并限定于建筑形态。我们概述了建筑研究、虚拟环境及相关技术方法，随后回顾了离散体素生成、从二维图像生成三维模型以及条件参数等近期趋势。我们突出了三维生成和参数化控制中值得进一步研究的未充分探索问题。此外，我们推测包括数据限制、可编辑性、评估指标和人机交互在内的四个研究议程，是建筑领域中计算机辅助设计与沉浸式系统实现无处不在交互的重要推动力。我们的工作有助于研究者理解当前深度学习在生成虚拟架构方面的潜力及未来需求。