Coarse to fine image matching by mining matchable regions and geometric cues

Detector-free image matchers have shown promising results in handling challenging cases of image matching. Their coarse-to-fine matching pipeline is particularly prone to incorrect matches in the coarse matching stage. This paper proposes to enhance coarse features by focusing attention learning more on matchable regions and to improve coarse match accuracy by exploring the geometric consistency among matches. For the enhanced feature extraction module, a regional attention mechanism is used in addition to the widely used global attention for self-/cross-feature interaction. For the feature matching module, a second-order geometric relation-induced matching confidence is proposed. These two modules respectively explore appearance and geometric cues to improve the quality of coarse matches and can be seamlessly integrated into existing coarse-to-fine matching pipelines. The effectiveness of the proposed method has been extensively validated on two popular coarse-to-fine matching pipelines (LoFTR and ASpanFormer), demonstrating improved performance on various image matching downstream tasks.