OmniGlue: Generalizable Feature Matching with Foundation Model Guidance

Hanwen Jiang, Arjun Karpur, Bingyi Cao, Qixing Huang, Andre Araujo

Abstract: 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 outpe rforming the recent LightGlue method by 9.5% relatively. Code and model can be found at https://hwjiang1510.github.io/OmniGlue.