  Given a pair of partially overlapping source and target images and a keypoint
in the source image, the keypoint's correspondent in the target image can be
either visible, occluded or outside the field of view. Local feature matching
methods are only able to identify the correspondent's location when it is
visible, while humans can also hallucinate its location when it is occluded or
outside the field of view through geometric reasoning. In this paper, we bridge
this gap by training a network to output a peaked probability distribution over
the correspondent's location, regardless of this correspondent being visible,
occluded, or outside the field of view. We experimentally demonstrate that this
network is indeed able to hallucinate correspondences on pairs of images
captured in scenes that were not seen at training-time. We also apply this
network to an absolute camera pose estimation problem and find it is
significantly more robust than state-of-the-art local feature matching-based
competitors.
