Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous
  Scenes
Antoine Schnepf Karim Kassab Jean-Yves Franceschi Laurent Caraffa Flavian Vasile Jeremie Mary Andrew Comport Valérie Gouet-Brunet
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 .