  Accurately matching local features between a pair of images is a challenging
computer vision task. Previous studies typically use attention based graph
neural networks (GNNs) with fully-connected graphs over keypoints within/across
images for visual and geometric information reasoning. However, in the context
of feature matching, considerable keypoints are non-repeatable due to occlusion
and failure of the detector, and thus irrelevant for message passing. The
connectivity with non-repeatable keypoints not only introduces redundancy,
resulting in limited efficiency, but also interferes with the representation
aggregation process, leading to limited accuracy. Targeting towards high
accuracy and efficiency, we propose MaKeGNN, a sparse attention-based GNN
architecture which bypasses non-repeatable keypoints and leverages matchable
ones to guide compact and meaningful message passing. More specifically, our
Bilateral Context-Aware Sampling Module first dynamically samples two small
sets of well-distributed keypoints with high matchability scores from the image
pair. Then, our Matchable Keypoint-Assisted Context Aggregation Module regards
sampled informative keypoints as message bottlenecks and thus constrains each
keypoint only to retrieve favorable contextual information from intra- and
inter- matchable keypoints, evading the interference of irrelevant and
redundant connectivity with non-repeatable ones. Furthermore, considering the
potential noise in initial keypoints and sampled matchable ones, the MKACA
module adopts a matchability-guided attentional aggregation operation for purer
data-dependent context propagation. By these means, we achieve the
state-of-the-art performance on relative camera estimation, fundamental matrix
estimation, and visual localization, while significantly reducing computational
and memory complexity compared to typical attentional GNNs.
