  Cross-scene model adaption is crucial for camera relocalization in real
scenarios. It is often preferable that a pre-learned model can be fast adapted
to a novel scene with as few training samples as possible. The existing
state-of-the-art approaches, however, can hardly support such few-shot scene
adaption due to the entangling of image feature extraction and scene coordinate
regression. To address this issue, we approach camera relocalization with a
decoupled solution where feature extraction, coordinate regression, and pose
estimation are performed separately. Our key insight is that feature encoder
used for coordinate regression should be learned by removing the distracting
factor of coordinate systems, such that feature encoder is learned from
multiple scenes for general feature representation and more important,
view-insensitive capability. With this feature prior, and combined with a
coordinate regressor, few-shot observations in a new scene are much easier to
connect with the 3D world than the one with existing integrated solution.
Experiments have shown the superiority of our approach compared to the
state-of-the-art methods, producing higher accuracy on several scenes with
diverse visual appearance and viewpoint distribution.
