  With the advent of convolutional neural networks, stereo matching algorithms
have recently gained tremendous progress. However, it remains a great challenge
to accurately extract disparities from real-world image pairs taken by
consumer-level devices like smartphones, due to practical complicating factors
such as thin structures, non-ideal rectification, camera module inconsistencies
and various hard-case scenes. In this paper, we propose a set of innovative
designs to tackle the problem of practical stereo matching: 1) to better
recover fine depth details, we design a hierarchical network with recurrent
refinement to update disparities in a coarse-to-fine manner, as well as a
stacked cascaded architecture for inference; 2) we propose an adaptive group
correlation layer to mitigate the impact of erroneous rectification; 3) we
introduce a new synthetic dataset with special attention to difficult cases for
better generalizing to real-world scenes. Our results not only rank 1st on both
Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art
methods by a notable margin, but also exhibit high-quality details for
real-life photos, which clearly demonstrates the efficacy of our contributions.
