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3D形状生成技术正日益受到计算机视觉与建筑设计领域的关注。本综述聚焦于研究并比较当前最先进的基于深度生成模型（DGMs）的3D对象生成方法，包括生成对抗网络（GANs）、变分自编码器（VAEs）、3D感知图像以及扩散模型。我们讨论了187篇文献（其中80.7%发表于2018-2022年间），以综述虚拟环境中建筑形态生成的可能性，研究范围限定于建筑形式层面。本文首先概述了建筑学、虚拟环境及相关技术方法的研究现状，继而回顾了离散体素生成、基于2D图像生成3D模型以及条件参数控制等最新趋势。我们重点指出了3D生成与参数化控制领域中尚未充分探索、值得进一步研究的问题。此外，我们推测数据限制、可编辑性、评估指标与人机交互这四个研究方向，是推动计算机辅助设计在建筑学中实现沉浸式系统普适交互的关键赋能因素。本工作有助于研究人员理解深度学习在生成虚拟建筑领域当前的潜力与未来的需求。