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.

本研究提出使用神经辐射场（NeRF）作为视觉定位的场景表示，通过利用 NeRF 的内部特征建立精确的 2D-3D 匹配来提升定位性能。通过研究 NeRF 的隐式知识和探索不同的匹配网络架构、提取多层的编码器特征以及多种训练配置，引入了 NeRFMatch 作为高级的 2D-3D 匹配函数，并在 Cambridge Landmarks 等标准定位基准上取得了业界领先的定位性能。