  Identifying robust and accurate correspondences across images is a
fundamental problem in computer vision that enables various downstream tasks.
Recent semi-dense matching methods emphasize the effectiveness of fusing
relevant cross-view information through Transformer. In this paper, we propose
several improvements upon this paradigm. Firstly, we introduce affine-based
local attention to model cross-view deformations. Secondly, we present
selective fusion to merge local and global messages from cross attention. Apart
from network structure, we also identify the importance of enforcing spatial
smoothness in loss design, which has been omitted by previous works. Based on
these augmentations, our network demonstrate strong matching capacity under
different settings. The full version of our network achieves state-of-the-art
performance among semi-dense matching methods at a similar cost to LoFTR, while
the slim version reaches LoFTR baseline's performance with only 15% computation
cost and 18% parameters.
