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.

翻译：超越新视角合成，神经辐射场在现实世界交互的应用中具有重要价值。本文将其作为特定场景的隐式地图，并针对该表示提出一种相机重定位算法。所提方法能够在设备导航过程中，利用单目RGB相机实时精确计算其位置。与先前工作不同，我们不依赖位姿回归或光度对齐，而是通过体素渲染获取密集局部特征，这些特征通过自监督目标针对场景进行特化。结果表明，本算法精度优于现有方法，能够在光照条件变化的动态室外环境中稳定运行，并可便捷集成至任意体积神经渲染器中。