  Due to the ability to synthesize high-quality novel views, Neural Radiance
Fields (NeRF) have been recently exploited to improve visual localization in a
known environment. However, the existing methods mostly utilize NeRFs for data
augmentation to improve the regression model training, and the performance on
novel viewpoints and appearances is still limited due to the lack of geometric
constraints. In this paper, we propose a novel visual localization framework,
\ie, PNeRFLoc, based on a unified point-based representation. On the one hand,
PNeRFLoc supports the initial pose estimation by matching 2D and 3D feature
points as traditional structure-based methods; on the other hand, it also
enables pose refinement with novel view synthesis using rendering-based
optimization. Specifically, we propose a novel feature adaption module to close
the gaps between the features for visual localization and neural rendering. To
improve the efficacy and efficiency of neural rendering-based optimization, we
also develop an efficient rendering-based framework with a warping loss
function. Furthermore, several robustness techniques are developed to handle
illumination changes and dynamic objects for outdoor scenarios. Experiments
demonstrate that PNeRFLoc performs the best on synthetic data when the NeRF
model can be well learned and performs on par with the SOTA method on the
visual localization benchmark datasets.
