  Stereo matching is a core task for many computer vision and robotics
applications. Despite their dominance in traditional stereo methods, the
hand-crafted Markov Random Field (MRF) models lack sufficient modeling accuracy
compared to end-to-end deep models. While deep learning representations have
greatly improved the unary terms of the MRF models, the overall accuracy is
still severely limited by the hand-crafted pairwise terms and message passing.
To address these issues, we propose a neural MRF model, where both potential
functions and message passing are designed using data-driven neural networks.
Our fully data-driven model is built on the foundation of variational inference
theory, to prevent convergence issues and retain stereo MRF's graph inductive
bias. To make the inference tractable and scale well to high-resolution images,
we also propose a Disparity Proposal Network (DPN) to adaptively prune the
search space of disparity. The proposed approach ranks $1^{st}$ on both KITTI
2012 and 2015 leaderboards among all published methods while running faster
than 100 ms. This approach significantly outperforms prior global methods,
e.g., lowering D1 metric by more than 50% on KITTI 2015. In addition, our
method exhibits strong cross-domain generalization and can recover sharp edges.
The codes at https://github.com/aeolusguan/NMRF
