  Recently, the ever-increasing capacity of large-scale annotated datasets has
led to profound progress in stereo matching. However, most of these successes
are limited to a specific dataset and cannot generalize well to other datasets.
The main difficulties lie in the large domain differences and unbalanced
disparity distribution across a variety of datasets, which greatly limit the
real-world applicability of current deep stereo matching models. In this paper,
we propose CFNet, a Cascade and Fused cost volume based network to improve the
robustness of the stereo matching network. First, we propose a fused cost
volume representation to deal with the large domain difference. By fusing
multiple low-resolution dense cost volumes to enlarge the receptive field, we
can extract robust structural representations for initial disparity estimation.
Second, we propose a cascade cost volume representation to alleviate the
unbalanced disparity distribution. Specifically, we employ a variance-based
uncertainty estimation to adaptively adjust the next stage disparity search
space, in this way driving the network progressively prune out the space of
unlikely correspondences. By iteratively narrowing down the disparity search
space and improving the cost volume resolution, the disparity estimation is
gradually refined in a coarse-to-fine manner. When trained on the same training
images and evaluated on KITTI, ETH3D, and Middlebury datasets with the fixed
model parameters and hyperparameters, our proposed method achieves the
state-of-the-art overall performance and obtains the 1st place on the stereo
task of Robust Vision Challenge 2020. The code will be available at
https://github.com/gallenszl/CFNet.
