Consistency-Driven Confidence Estimation for Stereo Matching

Abstract

Confidence estimation for stereo matching is crucial for enhancing the reliability and accuracy of depth perception in real-world applications. Despite effectively capturing aleatoric uncertainty through probabilistic modeling and statistical aggregation, current regression-based confidence estimation methods neglect uncertainty arising from unstable training dynamics, resulting in over-confident predictions near occlusion boundaries, textureless regions, and reflective surfaces where errors are most severe. We propose a novel epoch-wise consistency accumulation algorithm that explicitly incorporates training dynamics into confidence estimation. Specifically, we design a full-image cross-epoch alignment mechanism to dynamically quantify pixel-wise training consistency between consecutive epochs, thereby significantly enhancing the estimation of confidence. We further propose a consistency-ranked evidential discrepancy loss, which aligns evidential uncertainty estimates with consistency-derived ordinal supervision, aiming to improve the correlation between confidence scores and actual prediction errors. Our approach is incorporated into MonSter, an advanced stereo baseline, achieving SOTA performance in confidence estimation across KITTI 2012, KITTI 2015 and Middlebury benchmarks.