Kavli Affiliate: Zhuo Li
| First 5 Authors: Zhelun Shen, Zhuo Li, Chenming Wu, Zhibo Rao, Lina Liu
| Summary:
Recently, learning-based stereo matching methods have achieved great
improvement in public benchmarks, where soft argmin and smooth L1 loss play a
core contribution to their success. However, in unsupervised domain adaptation
scenarios, we observe that these two operations often yield multimodal
disparity probability distributions in target domains, resulting in degraded
generalization. In this paper, we propose a novel approach, Constrain
Multi-modal Distribution (CMD), to address this issue. Specifically, we
introduce textit{uncertainty-regularized minimization} and textit{anisotropic
soft argmin} to encourage the network to produce predominantly unimodal
disparity distributions in the target domain, thereby improving prediction
accuracy. Experimentally, we apply the proposed method to multiple
representative stereo-matching networks and conduct domain adaptation from
synthetic data to unlabeled real-world scenes. Results consistently demonstrate
improved generalization in both top-performing and domain-adaptable
stereo-matching models. The code for CMD will be available at:
href{https://github.com/gallenszl/CMD}{https://github.com/gallenszl/CMD}.
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