Editing Implicit and Explicit Representations of Radiance Fields: A Survey
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929645316734976 |
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| author | Hubert, Arthur Elghazaly, Gamal Frank, Raphael |
| author_facet | Hubert, Arthur Elghazaly, Gamal Frank, Raphael |
| contents | Neural Radiance Fields (NeRF) revolutionized novel view synthesis in recent years by offering a new volumetric representation, which is compact and provides high-quality image rendering. However, the methods to edit those radiance fields developed slower than the many improvements to other aspects of NeRF. With the recent development of alternative radiance field-based representations inspired by NeRF as well as the worldwide rise in popularity of text-to-image models, many new opportunities and strategies have emerged to provide radiance field editing. In this paper, we deliver a comprehensive survey of the different editing methods present in the literature for NeRF and other similar radiance field representations. We propose a new taxonomy for classifying existing works based on their editing methodologies, review pioneering models, reflect on current and potential new applications of radiance field editing, and compare state-of-the-art approaches in terms of editing options and performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Editing Implicit and Explicit Representations of Radiance Fields: A Survey Hubert, Arthur Elghazaly, Gamal Frank, Raphael Computer Vision and Pattern Recognition Neural Radiance Fields (NeRF) revolutionized novel view synthesis in recent years by offering a new volumetric representation, which is compact and provides high-quality image rendering. However, the methods to edit those radiance fields developed slower than the many improvements to other aspects of NeRF. With the recent development of alternative radiance field-based representations inspired by NeRF as well as the worldwide rise in popularity of text-to-image models, many new opportunities and strategies have emerged to provide radiance field editing. In this paper, we deliver a comprehensive survey of the different editing methods present in the literature for NeRF and other similar radiance field representations. We propose a new taxonomy for classifying existing works based on their editing methodologies, review pioneering models, reflect on current and potential new applications of radiance field editing, and compare state-of-the-art approaches in terms of editing options and performance. |
| title | Editing Implicit and Explicit Representations of Radiance Fields: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.17628 |