Improving Local Feature Matching by Entropy-inspired Scale Adaptability and Flow-endowed Local Consistency

ArXi:2604.06713v1 Announce Type: new Recent semi-dense image matching methods have achieved remarkable success, but two long-standing issues still impair their performance. At the coarse stage, the over-exclusion issue of their mutual nearest neighbor (MNN) matching layer makes them struggle to handle cases with scale difference between images. To this end, we comprehensively revisit the matching mechanism and make a key observation that the hint concealed in the score matrix can be exploited to indicate the scale ratio.