  In this paper, we introduce a novel Gaussian mixture based evidential
learning solution for robust stereo matching. Diverging from previous
evidential deep learning approaches that rely on a single Gaussian
distribution, our framework posits that individual image data adheres to a
mixture-of-Gaussian distribution in stereo matching. This assumption yields
more precise pixel-level predictions and more accurately mirrors the real-world
image distribution. By further employing the inverse-Gamma distribution as an
intermediary prior for each mixture component, our probabilistic model achieves
improved depth estimation compared to its counterpart with the single Gaussian
and effectively captures the model uncertainty, which enables a strong
cross-domain generation ability. We evaluated our method for stereo matching by
training the model using the Scene Flow dataset and testing it on KITTI 2015
and Middlebury 2014. The experiment results consistently show that our method
brings improvements over the baseline methods in a trustworthy manner. Notably,
our approach achieved new state-of-the-art results on both the in-domain
validated data and the cross-domain datasets, demonstrating its effectiveness
and robustness in stereo matching tasks.
