Advances in 3D Generation: A Survey
Xiaoyu Li Qi Zhang Di Kang Weihao Cheng Yiming Gao Jingbo Zhang Zhihao Liang Jing Liao Yan-Pei Cao Ying Shan
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.