  In this work, we propose the use of Neural Radiance Fields (NeRF) as a scene
representation for visual localization. Recently, NeRF has been employed to
enhance pose regression and scene coordinate regression models by augmenting
the training database, providing auxiliary supervision through rendered images,
or serving as an iterative refinement module. We extend its recognized
advantages -- its ability to provide a compact scene representation with
realistic appearances and accurate geometry -- by exploring the potential of
NeRF's internal features in establishing precise 2D-3D matches for
localization. To this end, we conduct a comprehensive examination of NeRF's
implicit knowledge, acquired through view synthesis, for matching under various
conditions. This includes exploring different matching network architectures,
extracting encoder features at multiple layers, and varying training
configurations. Significantly, we introduce NeRFMatch, an advanced 2D-3D
matching function that capitalizes on the internal knowledge of NeRF learned
via view synthesis. Our evaluation of NeRFMatch on standard localization
benchmarks, within a structure-based pipeline, sets a new state-of-the-art for
localization performance on Cambridge Landmarks.
