3D生成研究进展：综述
Advances in 3D Generation: A Survey
AI总结：
本综述梳理3D内容生成领域的最新进展，构建涵盖3D表示、生成方法、数据集与应用的结构化路线图，按算法范式分类总结各类生成方法，为领域研究者提供参考并推动后续发展。
摘要：
3D模型生成是计算机图形学的核心问题，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，已能生成质量更高、多样性更强的3D模型。该领域的快速发展使得研究者难以全面跟进最新进展。本综述旨在介绍3D生成方法的基础方法论，并构建结构化的研究路线图，涵盖3D表示、生成方法、数据集及相关应用。具体而言，本文介绍了作为3D生成核心支撑的各类3D表示方法。此外，本文对快速增长的生成方法相关文献进行了全面梳理，按算法范式类型分为前馈生成、基于优化的生成、过程式生成以及生成式新视角合成几大类。最后，本文讨论了现有可用数据集、应用场景以及待解决的开放性挑战。我们希望本综述能帮助读者探索这一前沿课题，并推动3D内容生成领域的进一步发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
