通过自适应尺度与流一致性优化，提升图像匹配精度与鲁棒性
Improving Local Feature Matching by Entropy-inspired Scale Adaptability and Flow-endowed Local Consistency
- 基于得分矩阵设计尺度感知匹配模块，缓解尺度差异导致的误删问题
- 将精细匹配重构为级联光流优化问题，引入梯度损失增强局部一致性
- 在多个下游任务中实现更稳定准确的匹配结果，适合高精度视觉任务
近期半密集图像匹配方法取得显著进展，但仍面临两个长期存在的问题。在粗粒度阶段，其互近邻（MNN）匹配层存在过度排除现象，难以应对图像间尺度差异。为此，我们重新审视匹配机制，发现得分矩阵中隐藏的线索可指示尺度比。据此提出一种尺度感知匹配模块，效果显著且计算开销极低。在细粒度阶段，现有方法忽视最终匹配的局部一致性，削弱了鲁棒性。为此，不独立预测每个源像素的对应关系，而是将细粒度阶段重构为级联光流精修问题，并引入新型梯度损失以促进光流场的局部一致性。大量实验表明，所提出的匹配流程在下游任务中实现了稳健且精确的匹配性能。
原文摘要 · Abstract (English)
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. Based on this, we propose a scale-aware matching module which is exceptionally effective but introduces negligible overhead. At the fine stage, we point out that existing methods neglect the local consistency of final matches, which undermines their robustness. To this end, rather than independently predicting the correspondence for each source pixel, we reformulate the fine stage as a cascaded flow refinement problem and introduce a novel gradient loss to encourage local consistency of the flow field. Extensive experiments demonstrate that our novel matching pipeline, with these proposed modifications, achieves robust and accurate matching performance on downstream tasks.
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