Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
Abstract
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 .
Origin : Files produced by the author(s)
licence : CC BY - Attribution
licence : CC BY - Attribution
