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.

翻译：神经辐射场（NeRFs）已成为一种强大的神经3D表示方法，可从2D数据中生成物体和场景。然而，在许多场景下生成NeRFs仍然困难。例如，仅使用少量视角作为监督训练NeRF仍具挑战性，因为这本质上是一个欠约束问题。在此类设定中，需要某种归纳先验来滤除不良局部极小值。引入这类归纳先验的一种方式是学习针对特定场景类别的NeRF生成模型。本文提出使用扩散模型生成编码在正则化网格上的NeRF。我们证明该模型能采样出逼真的NeRF，同时允许在给定观测作为引导的条件下进行条件生成。