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

• URL: http://arxiv.org/abs/2503.23747v1
• Date: Mon, 31 Mar 2025 05:58:25 GMT
• Title: Consistency-aware Self-Training for Iterative-based Stereo Matching
• Authors: Jingyi Zhou, Peng Ye, Haoyu Zhang, Jiakang Yuan, Rao Qiang, Liu YangChenXu, Wu Cailin, Feng Xu, Tao Chen,
• Abstract summary: We propose a consistency-aware self-training framework for iterative-based stereo matching.<n>We first observe that regions with larger errors tend to exhibit more pronounced oscillation characteristics during model prediction.<n>We introduce a consistency-aware soft-weighted loss to adjust the weight of pseudo-labels accordingly, relieving the error accumulation and performance degradation problem.
• Score: 13.079759982779013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 iterative-based stereo matching for the first time, leveraging real-world 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 consistency-aware soft filtering module to evaluate the reliability of teacher-predicted pseudo-labels, which consists of a multi-resolution 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 soft-weighted 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.

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