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
this https URL

该论文提出了一种基于级联和融合代价体的深度学习模型 CFNet，以解决大规模标注数据集的深度立体匹配模型的泛化能力问题，通过融合多个低分辨率的密集代价体以及逐步缩小视差搜索空间的方式，建立起一套定量的不确定性估计体系，实现了对跨数据集间的泛化匹配能力的优化。