  Correlation based stereo matching has achieved outstanding performance, which
pursues cost volume between two feature maps. Unfortunately, current methods
with a fixed model do not work uniformly well across various datasets, greatly
limiting their real-world applicability. To tackle this issue, this paper
proposes a new perspective to dynamically calculate correlation for robust
stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module
is introduced to robustly adapt the same model for different scenarios.
Specifically, a variance-based uncertainty estimation is employed to adaptively
adjust the sampling area during warping operation. Additionally, we improve the
traditional non-parametric warping with learnable parameters, such that the
position-specific weights can be learned. We show that by empowering the
recurrent network with the UGAC module, stereo matching can be exploited more
robustly and effectively. Extensive experiments demonstrate that our method
achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury
datasets when employing the same fixed model over these datasets without any
retraining procedure. To target real-time applications, we further design a
lightweight model based on UGAC, which also outperforms other methods over
KITTI benchmarks with only 0.6 M parameters.
