CROSSFIRE：基于隐式表示自监督特征的相机重定位
CROSSFIRE: Camera Relocalization On Self-Supervised Features from an Implicit Representation
AI总结：
本研究提出CROSSFIRE相机重定位算法，以神经辐射场作为场景隐式地图，基于体渲染得到的自监督场景专用密集局部特征实现单目RGB相机的实时高精度重定位，适配动态光照室外环境且易集成到体积神经渲染器。
摘要：
除新视角合成外，神经辐射场（Neural Radiance Fields）还可应用于与真实世界交互的场景。本文将其作为给定场景的隐式地图，提出了一种适配该表示的相机重定位算法。该方法可在设备导航过程中，利用单目RGB相机实时计算设备的精确位置。与现有研究不同，本文方法不依赖位姿回归或光度对齐，而是使用通过体渲染获得的密集局部特征，这些特征通过自监督目标针对特定场景进行了专门优化。实验结果表明，该算法精度优于同类方法，能够在光照变化的动态室外环境中运行，且可轻松集成到任一体积神经渲染器中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
