  Neural radiance fields enable novel-view synthesis and scene reconstruction
with photorealistic quality from a few images, but require known and accurate
camera poses. Conventional pose estimation algorithms fail on smooth or
self-similar scenes, while methods performing inverse rendering from unposed
views require a rough initialization of the camera orientations. The main
difficulty of pose estimation lies in real-life objects being almost invariant
under certain transformations, making the photometric distance between rendered
views non-convex with respect to the camera parameters. Using an equivalence
relation that matches the distribution of local minima in camera space, we
reduce this space to its quotient set, in which pose estimation becomes a more
convex problem. Using a neural-network to regularize pose estimation, we
demonstrate that our method - MELON - can reconstruct a neural radiance field
from unposed images with state-of-the-art accuracy while requiring ten times
fewer views than adversarial approaches.
