DualU: Modeling Dual Uncertainties for Correspondence Learning

Correspondence learning aims to estimate geometric models from putative correspondences contaminated by severe outliers and is fundamental to many computer vision tasks. Existing methods have substantially improved outlier rejection by predicting inlier scores for each correspondence. However, under repeated structures or insufficient geometric context, outliers may exhibit patterns similar to inliers and consequently receive high scores, while even correctly identified inliers may still have different localization errors and therefore provide geometric measurements of varying accuracy. An inlier score alone does not quantify the reliability of the prediction or the geometric error of an inlier. To address this limitation, we propose DualU, a correspondence learning method that explicitly models dual uncertainties (i.e., the reliability of inlier predictions and the geometric errors of inliers), and uses them to guide geometric model estimation. Extensive experiments demonstrate that DualU outperforms state-of-the-art methods across multiple tasks and exhibits stronger generalization ability.