  Establishing dense correspondence between two images is a fundamental
computer vision problem, which is typically tackled by matching local feature
descriptors. However, without global awareness, such local features are often
insufficient for disambiguating similar regions. And computing the pairwise
feature correlation across images is both computation-expensive and
memory-intensive. To make the local features aware of the global context and
improve their matching accuracy, we introduce DenseGAP, a new solution for
efficient Dense correspondence learning with a Graph-structured neural network
conditioned on Anchor Points. Specifically, we first propose a graph structure
that utilizes anchor points to provide sparse but reliable prior on inter- and
intra-image context and propagates them to all image points via directed edges.
We also design a graph-structured network to broadcast multi-level contexts via
light-weighted message-passing layers and generate high-resolution feature maps
at low memory cost. Finally, based on the predicted feature maps, we introduce
a coarse-to-fine framework for accurate correspondence prediction using cycle
consistency. Our feature descriptors capture both local and global information,
thus enabling a continuous feature field for querying arbitrary points at high
resolution. Through comprehensive ablative experiments and evaluations on
large-scale indoor and outdoor datasets, we demonstrate that our method
advances the state-of-the-art of correspondence learning on most benchmarks.
