  Under good conditions, Neural Radiance Fields (NeRFs) have shown impressive
results on novel view synthesis tasks. NeRFs learn a scene's color and density
fields by minimizing the photometric discrepancy between training views and
differentiable renderings of the scene. Once trained from a sufficient set of
views, NeRFs can generate novel views from arbitrary camera positions. However,
the scene geometry and color fields are severely under-constrained, which can
lead to artifacts, especially when trained with few input views.
  To alleviate this problem we learn a prior over scene geometry and color,
using a denoising diffusion model (DDM). Our DDM is trained on RGBD patches of
the synthetic Hypersim dataset and can be used to predict the gradient of the
logarithm of a joint probability distribution of color and depth patches. We
show that, these gradients of logarithms of RGBD patch priors serve to
regularize geometry and color of a scene. During NeRF training, random RGBD
patches are rendered and the estimated gradient of the log-likelihood is
backpropagated to the color and density fields. Evaluations on LLFF, the most
relevant dataset, show that our learned prior achieves improved quality in the
reconstructed geometry and improved generalization to novel views. Evaluations
on DTU show improved reconstruction quality among NeRF methods.
