Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data. Generating NeRFs, however, remains difficult in many scenarios. For instance, training a NeRF with only a small number of views as supervision remains challenging since it is an under-constrained problem. In such settings, it calls for some inductive prior to filter out bad local minima. One way to introduce such inductive priors is to learn a generative model for NeRFs modeling a certain class of scenes. In this paper, we propose to use a diffusion model to generate NeRFs encoded on a regularized grid. We show that our model can sample realistic NeRFs, while at the same time allowing conditional generations, given a certain observation as guidance.
@article{arxiv.2304.14473,
title = {Learning a Diffusion Prior for NeRFs},
author = {Guandao Yang and Abhijit Kundu and Leonidas J. Guibas and Jonathan T. Barron and Ben Poole},
journal= {arXiv preprint arXiv:2304.14473},
year = {2023}
}