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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.17807 |
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| _version_ | 1866913217039564800 |
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| author | Li, Xiaoyu Zhang, Qi Kang, Di Cheng, Weihao Gao, Yiming Zhang, Jingbo Liang, Zhihao Liao, Jing Cao, Yan-Pei Shan, Ying |
| author_facet | Li, Xiaoyu Zhang, Qi Kang, Di Cheng, Weihao Gao, Yiming Zhang, Jingbo Liang, Zhihao Liao, Jing Cao, Yan-Pei Shan, Ying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17807 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advances in 3D Generation: A Survey Li, Xiaoyu Zhang, Qi Kang, Di Cheng, Weihao Gao, Yiming Zhang, Jingbo Liang, Zhihao Liao, Jing Cao, Yan-Pei Shan, Ying Computer Vision and Pattern Recognition Graphics 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. |
| title | Advances in 3D Generation: A Survey |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2401.17807 |