LGFCTR: Local and Global Feature Convolutional Transformer for Image Matching
This work addresses image matching for computer vision applications, presenting an incremental improvement by combining transformers and convolutions.
The paper tackles the problem of image matching under extreme conditions by proposing a convolutional transformer that captures both local and global features, achieving superior performance on multiple benchmarks.
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.