Consistency-aware Self-Training for Iterative-based Stereo Matching

Jingyi Zhou, Peng Ye, Haoyu Zhang, Jiakang Yuan, Rao Qiang, Liu YangChenXu, Wu Cailin, Feng Xu, Tao Chen

Abstract:

Iterative-based methods have become mainstream in stereo
matching due to their high performance. However, these
methods heavily rely on labeled data and face challenges
with unlabeled real-world data. To this end, we propose
a consistency-aware self-training framework for iterativebased stereo matching for the first time, leveraging realworld unlabeled data in a teacher-student manner. We first
observe that regions with larger errors tend to exhibit more
pronounced oscillation characteristics during model prediction. Based on this, we introduce a novel consistencyaware soft filtering module to evaluate the reliability of
teacher-predicted pseudo-labels, which consists of a multiresolution prediction consistency filter and an iterative prediction consistency filter to assess the prediction fluctuations of multiple resolutions and iterative optimization respectively. Further, we introduce a consistency-aware softweighted loss to adjust the weight of pseudo-labels accordingly, relieving the error accumulation and performance
degradation problem due to incorrect pseudo-labels. Extensive experiments demonstrate that our method can improve
the performance of various iterative-based stereo matching
approaches in various scenarios. In particular, our method
can achieve further enhancements over the current SOTA
methods on several benchmark datasets.