  We cast multiview reconstruction from unknown pose as a generative modeling
problem. From a collection of unannotated 2D images of a scene, our approach
simultaneously learns both a network to predict camera pose from 2D image
input, as well as the parameters of a Neural Radiance Field (NeRF) for the 3D
scene. To drive learning, we wrap both the pose prediction network and NeRF
inside a Denoising Diffusion Probabilistic Model (DDPM) and train the system
via the standard denoising objective. Our framework requires the system
accomplish the task of denoising an input 2D image by predicting its pose and
rendering the NeRF from that pose. Learning to denoise thus forces the system
to concurrently learn the underlying 3D NeRF representation and a mapping from
images to camera extrinsic parameters. To facilitate the latter, we design a
custom network architecture to represent pose as a distribution, granting
implicit capacity for discovering view correspondences when trained end-to-end
for denoising alone. This technique allows our system to successfully build
NeRFs, without pose knowledge, for challenging scenes where competing methods
fail. At the conclusion of training, our learned NeRF can be extracted and used
as a 3D scene model; our full system can be used to sample novel camera poses
and generate novel-view images.
