  Absolute Pose Regression (APR) methods use deep neural networks to directly
regress camera poses from RGB images. However, the predominant APR
architectures only rely on 2D operations during inference, resulting in limited
accuracy of pose estimation due to the lack of 3D geometry constraints or
priors. In this work, we propose a test-time refinement pipeline that leverages
implicit geometric constraints using a robust feature field to enhance the
ability of APR methods to use 3D information during inference. We also
introduce a novel Neural Feature Synthesizer (NeFeS) model, which encodes 3D
geometric features during training and directly renders dense novel view
features at test time to refine APR methods. To enhance the robustness of our
model, we introduce a feature fusion module and a progressive training
strategy. Our proposed method achieves state-of-the-art single-image APR
accuracy on indoor and outdoor datasets.
