  The image matching field has been witnessing a continuous emergence of novel
learnable feature matching techniques, with ever-improving performance on
conventional benchmarks. However, our investigation shows that despite these
gains, their potential for real-world applications is restricted by their
limited generalization capabilities to novel image domains. In this paper, we
introduce OmniGlue, the first learnable image matcher that is designed with
generalization as a core principle. OmniGlue leverages broad knowledge from a
vision foundation model to guide the feature matching process, boosting
generalization to domains not seen at training time. Additionally, we propose a
novel keypoint position-guided attention mechanism which disentangles spatial
and appearance information, leading to enhanced matching descriptors. We
perform comprehensive experiments on a suite of $7$ datasets with varied image
domains, including scene-level, object-centric and aerial images. OmniGlue's
novel components lead to relative gains on unseen domains of $20.9\%$ with
respect to a directly comparable reference model, while also outperforming the
recent LightGlue method by $9.5\%$ relatively.Code and model can be found at
https://hwjiang1510.github.io/OmniGlue
