3D生成的进展：一项调研

摘要

生成三维模型是计算机图形学的核心，并且已经成为几十年研究的重点。随着先进的神经表示和生成模型的出现，三维内容生成领域正在迅速发展，使得能够创造出越来越高质量和多样化的三维模型。这一领域的快速增长使得跟上所有最新发展变得困难。在这项调查中，我们旨在介绍三维生成方法的基本方法论，并建立一个结构化的路线图，涵盖三维表示、生成方法、数据集以及相关应用。具体而言，我们介绍作为三维生成基础的三维表示。此外，我们提供了对生成方法快速增长文献的全面概述，按照算法范式类型进行分类，包括前馈生成、基于优化的生成、过程生成和生成新视角合成。最后，我们讨论可用的数据集、应用和面临的挑战。我们希望这项调查能帮助读者探索这一激动人心的主题，并促进三维内容生成领域的进一步发展。

English

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.

3D生成的进展：一项调研

Advances in 3D Generation: A Survey

摘要

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