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
this https URL

OmniGlue 是第一个以广义化为核心原则设计的可学习图像匹配器，它利用视觉基础模型的广泛知识来指导特征匹配过程，提高对未知图像域的泛化能力，并且通过引入新颖的关键点位置引导的注意机制，分离空间和外观信息，从而实现了出色的匹配描述符。