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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.03357 |
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| _version_ | 1866910529893695488 |
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| author | Petit, Doriand Bourgeois, Steve Pavel, Dumitru Gay-Bellile, Vincent Chabot, Florian Barthe, Loic |
| author_facet | Petit, Doriand Bourgeois, Steve Pavel, Dumitru Gay-Bellile, Vincent Chabot, Florian Barthe, Loic |
| contents | Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models. Rather than developing hard-to-combine and specific modules, another approach generally overlooked is to directly inject generic priors on the scene representation (also called inductive biases) into the NeRF architecture. Based on this idea, we propose the RING-NeRF architecture which includes two inductive biases : a continuous multi-scale representation of the scene and an invariance of the decoder's latent space over spatial and scale domains. We also design a single reconstruction process that takes advantage of those inductive biases and experimentally demonstrates on-par performances in terms of quality with dedicated architecture on multiple tasks (anti-aliasing, few view reconstruction, SDF reconstruction without scene-specific initialization) while being more efficient. Moreover, RING-NeRF has the distinctive ability to dynamically increase the resolution of the model, opening the way to adaptive reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03357 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RING-NeRF : Rethinking Inductive Biases for Versatile and Efficient Neural Fields Petit, Doriand Bourgeois, Steve Pavel, Dumitru Gay-Bellile, Vincent Chabot, Florian Barthe, Loic Computer Vision and Pattern Recognition Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models. Rather than developing hard-to-combine and specific modules, another approach generally overlooked is to directly inject generic priors on the scene representation (also called inductive biases) into the NeRF architecture. Based on this idea, we propose the RING-NeRF architecture which includes two inductive biases : a continuous multi-scale representation of the scene and an invariance of the decoder's latent space over spatial and scale domains. We also design a single reconstruction process that takes advantage of those inductive biases and experimentally demonstrates on-par performances in terms of quality with dedicated architecture on multiple tasks (anti-aliasing, few view reconstruction, SDF reconstruction without scene-specific initialization) while being more efficient. Moreover, RING-NeRF has the distinctive ability to dynamically increase the resolution of the model, opening the way to adaptive reconstruction. |
| title | RING-NeRF : Rethinking Inductive Biases for Versatile and Efficient Neural Fields |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.03357 |