Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others. Such domain shift issue is usually addressed by substantial adaptation on costly target-domain ground-truth data, which cannot be easily obtained in practical settings. In this paper, we propose to dig into uncertainty estimation for robust stereo matching. Specifically, to balance the disparity distribution, we employ a pixel-level uncertainty estimation to adaptively adjust the next stage disparity searching space, in this way driving the network progressively prune out the space of unlikely correspondences. Then, to solve the limited ground truth data, an uncertainty-based pseudo-label is proposed to adapt the pre-trained model to the new domain, where pixel-level and area-level uncertainty estimation are proposed to filter out the high-uncertainty pixels of predicted disparity maps and generate sparse while reliable pseudo-labels to align the domain gap. Experimentally, our method shows strong cross-domain, adapt, and joint generalization and obtains \textbf{1st} place on the stereo task of Robust Vision Challenge 2020. Additionally, our uncertainty-based pseudo-labels can be extended to train monocular depth estimation networks in an unsupervised way and even achieves comparable performance with the supervised methods. The code will be available at https://github.com/gallenszl/UCFNet.

翻译：由于不同数据集之间存在领域差异和不均衡的视差分布，当前的立体匹配方法通常局限于特定数据集，且难以泛化到其他数据集。这种领域偏移问题通常需要通过大量昂贵的目标领域真值数据进行适配来解决，而在实际场景中这些数据难以获取。本文提出深入探索不确定性估计以实现鲁棒立体匹配。具体而言，为平衡视差分布，我们采用像素级不确定性估计自适应调整下一阶段的视差搜索空间，从而引导网络逐步剪除不可能匹配的空间。然后，为解决真值数据有限的问题，提出基于不确定性的伪标签方法，将预训练模型适配到新领域，其中通过像素级和区域级不确定性估计过滤预测视差图中的高不确定性像素，生成稀疏但可靠的伪标签以对齐领域差距。实验表明，本方法具有强大的跨领域、适配及联合泛化能力，在2020年鲁棒视觉挑战赛的立体任务中荣获第一名。此外，基于不确定性的伪标签可扩展用于以无监督方式训练单目深度估计网络，甚至达到与监督方法相当的性能。代码将开源至https://github.com/gallenszl/UCFNet。