  Many applications require a camera to be relocalised online, without
expensive offline training on the target scene. Whilst both keyframe and sparse
keypoint matching methods can be used online, the former often fail away from
the training trajectory, and the latter can struggle in textureless regions. By
contrast, scene coordinate regression (SCoRe) methods generalise to novel poses
and can leverage dense correspondences to improve robustness, and recent work
has shown how to adapt SCoRe forests between scenes, allowing their
state-of-the-art performance to be leveraged online. However, because they use
features hand-crafted for indoor use, they do not generalise well to harder
outdoor scenes. Whilst replacing the forest with a neural network and learning
suitable features for outdoor use is possible, the techniques used to adapt
forests between scenes are unfortunately harder to transfer to a network
context. In this paper, we address this by proposing a novel way of leveraging
a network trained on one scene to predict points in another scene. Our approach
replaces the appearance clustering performed by the branching structure of a
regression forest with a two-step process that first uses the network to
predict points in the original scene, and then uses these predicted points to
look up clusters of points from the new scene. We show experimentally that our
online approach achieves state-of-the-art performance on both the 7-Scenes and
Cambridge Landmarks datasets, whilst running in under 300ms, making it highly
effective in live scenarios.
