Consensus-Guided Correspondence Denoising
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Correspondence selection between two groups of feature points aims to correctly recognize the consistent matches (inliers) from the initial noisy matches. The selection is generally challenging since the initial matches are generally extremely unbalanced, where outliers can easily dominate. Moreover, random distributions of outliers lead to the limited robustness of previous works when applied to different scenarios. To address this issue, we propose to denoise correspondences with a local-to-global consensus learning framework to robustly identify correspondence. A novel "pruning" block is introduced to distill reliable candidates from initial matches according to their consensus scores estimated by dynamic graphs from local to global regions. The proposed correspondence denoising is progressively achieved by stacking multiple pruning blocks sequentially. Our method outperforms state-of-the-arts on robust line fitting, wide-baseline image matching and image localization benchmarks by noticeable margins and shows promising generalization capability on different distributions of initial matches.
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