LGFCTR: Local and Global Feature Convolutional Transformer for Image
  Matching
Wenhao Zhong Jie Jiang
Image matching that finding robust and accurate correspondences across images
is a challenging task under extreme conditions. Capturing local and global
features simultaneously is an important way to mitigate such an issue but
recent transformer-based decoders were still stuck in the issues that CNN-based
encoders only extract local features and the transformers lack locality.
Inspired by the locality and implicit positional encoding of convolutions, a
novel convolutional transformer is proposed to capture both local contexts and
global structures more sufficiently for detector-free matching. Firstly, a
universal FPN-like framework captures global structures in self-encoder as well
as cross-decoder by transformers and compensates local contexts as well as
implicit positional encoding by convolutions. Secondly, a novel convolutional
transformer module explores multi-scale long range dependencies by a novel
multi-scale attention and further aggregates local information inside
dependencies for enhancing locality. Finally, a novel regression-based
sub-pixel refinement module exploits the whole fine-grained window features for
fine-level positional deviation regression. The proposed method achieves
superior performances on a wide range of benchmarks. The code will be available
on https://github.com/zwh0527/LGFCTR.