We introduce DiffRF, a novel approach for 3D radiance field synthesis based
on denoising diffusion probabilistic models. While existing diffusion-based
methods operate on images, latent codes, or point cloud data, we are the first
to directly generate volumetric radiance fields. To this end, we propose a 3D
denoising model which directly operates on an explicit voxel grid
representation. However, as radiance fields generated from a set of posed
images can be ambiguous and contain artifacts, obtaining ground truth radiance
field samples is non-trivial. We address this challenge by pairing the
denoising formulation with a rendering loss, enabling our model to learn a
deviated prior that favours good image quality instead of trying to replicate
fitting errors like floating artifacts. In contrast to 2D-diffusion models, our
model learns multi-view consistent priors, enabling free-view synthesis and
accurate shape generation. Compared to 3D GANs, our diffusion-based approach
naturally enables conditional generation such as masked completion or
single-view 3D synthesis at inference time.Comment: Project page: https://sirwyver.github.io/DiffRF/ Video:
https://youtu.be/qETBcLu8SUk - CVPR 2023 Highlight - updated evaluations
after fixing initial data mapping error on all method