  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.
