  With the widespread use of NeRF-based implicit 3D representation, the need
for camera localization in the same representation becomes manifestly apparent.
Doing so not only simplifies the localization process -- by avoiding an
outside-the-NeRF-based localization -- but also has the potential to offer the
benefit of enhanced localization. This paper studies the problem of localizing
cameras in NeRF using a diffusion model for camera pose adjustment. More
specifically, given a pre-trained NeRF model, we train a diffusion model that
iteratively updates randomly initialized camera poses, conditioned upon the
image to be localized. At test time, a new camera is localized in two steps:
first, coarse localization using the proposed pose diffusion process, followed
by local refinement steps of a pose inversion process in NeRF. In fact, the
proposed camera localization by pose diffusion (CaLDiff) method also integrates
the pose inversion steps within the diffusion process. Such integration offers
significantly better localization, thanks to our downstream refinement-aware
diffusion process. Our exhaustive experiments on challenging real-world data
validate our method by providing significantly better results than the compared
methods and the established baselines. Our source code will be made publicly
available.
