Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. The rapid growth of this field makes it difficult to stay abreast of all recent developments. In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. Furthermore, we provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms, including feedforward generation, optimization-based generation, procedural generation, and generative novel view synthesis. Lastly, we discuss available datasets, applications, and open challenges. We hope this survey will help readers explore this exciting topic and foster further advancements in the field of 3D content generation.

翻译：3D模型生成是计算机图形学的核心内容，也是数十年来的研究重点。随着先进神经表示与生成模型的出现，3D内容生成领域发展迅速，能够创建日益高质量且多样化的3D模型。该领域的快速增长使得人们难以全面掌握所有最新进展。本综述旨在介绍3D生成方法的基础方法论，并构建结构化路线图，涵盖3D表示、生成方法、数据集及相应应用。具体而言，我们介绍了作为3D生成支柱的3D表示方法。此外，我们按算法范式类型（包括前馈生成、基于优化的生成、程序化生成和生成式新视角合成）系统梳理了快速增长的生成方法文献。最后，我们讨论了现有数据集、应用及开放挑战。希望本综述能帮助读者探索这一令人兴奋的课题，并推动3D内容生成领域的进一步发展。