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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/2403.11678 |
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| _version_ | 1866914799546269696 |
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| author | Schnepf, Antoine Kassab, Karim Franceschi, Jean-Yves Caraffa, Laurent Vasile, Flavian Mary, Jeremie Comport, Andrew Gouet-Brunet, Valérie |
| author_facet | Schnepf, Antoine Kassab, Karim Franceschi, Jean-Yves Caraffa, Laurent Vasile, Flavian Mary, Jeremie Comport, Andrew Gouet-Brunet, Valérie |
| contents | We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes. We combine two techniques to improve the required training time and memory cost per scene. First, we learn a 3D-aware latent space in which we train Tri-Plane scene representations, hence reducing the resolution at which scenes are learned. Moreover, we present a way to share common information across scenes, hence allowing for a reduction of model complexity to learn a particular scene. Our method reduces effective per-scene memory costs by 44% and per-scene time costs by 86% when training 1000 scenes. Our project page can be found at https://3da-ae.github.io . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11678 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes Schnepf, Antoine Kassab, Karim Franceschi, Jean-Yves Caraffa, Laurent Vasile, Flavian Mary, Jeremie Comport, Andrew Gouet-Brunet, Valérie Computer Vision and Pattern Recognition Machine Learning We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes. We combine two techniques to improve the required training time and memory cost per scene. First, we learn a 3D-aware latent space in which we train Tri-Plane scene representations, hence reducing the resolution at which scenes are learned. Moreover, we present a way to share common information across scenes, hence allowing for a reduction of model complexity to learn a particular scene. Our method reduces effective per-scene memory costs by 44% and per-scene time costs by 86% when training 1000 scenes. Our project page can be found at https://3da-ae.github.io . |
| title | Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.11678 |