4 papers
Fused-Planes: Why Train a Thousand Tri-Planes When You Can Share?
Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi +5
Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar structures. This has made them the prevailing choice t…
RefinedFields: Radiance Fields Refinement for Planar Scene Representations
Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi +3
Planar scene representations have recently witnessed increased interests for modeling scenes from images, as their lightweight planar structure enables compatibility with image-bas…
Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder
Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi +5
While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides red…
Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi +5
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…