  Beyond novel view synthesis, Neural Radiance Fields are useful for
applications that interact with the real world. In this paper, we use them as
an implicit map of a given scene and propose a camera relocalization algorithm
tailored for this representation. The proposed method enables to compute in
real-time the precise position of a device using a single RGB camera, during
its navigation. In contrast with previous work, we do not rely on pose
regression or photometric alignment but rather use dense local features
obtained through volumetric rendering which are specialized on the scene with a
self-supervised objective. As a result, our algorithm is more accurate than
competitors, able to operate in dynamic outdoor environments with changing
lightning conditions and can be readily integrated in any volumetric neural
renderer.
