Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
<|reference_start|>Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes: 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 .<|reference_end|>
@article{schnepf2024exploring,
    title={Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous
  Scenes},
    author={Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa,
  Flavian Vasile, Jeremie Mary, Andrew Comport, Val'erie Gouet-Brunet},
    journal={arXiv preprint arXiv:2403.11678},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11678},
    primaryClass={cs.CV cs.LG}
}