Deep end-to-end learning based stereo matching methods have achieved great success as witnessed by the leaderboards across different benchmarking datasets (KITTI, Middlebury, ETH3D, etc). However, real scenarios not only require approaches to have state-of-the-art performance but also real-time speed and domain-across generalization, which cannot be satisfied by existing methods. In this paper, we propose MSMD-Net (Multi-Scale and Multi-Dimension) to construct multi-scale and multi-dimension cost volume. At the multi-scale level, we generate four 4D combination volumes at different scales and integrate them with an encoder-decoder process to predict an initial disparity estimation. At the multi-dimension level, we additionally construct a 3D warped correlation volume and use it to refine the initial disparity map with residual learning. These two dimensional cost volumes are complementary to each other and can boost the performance of disparity estimation. Additionally, we propose a switch training strategy to alleviate the overfitting issue appeared in the pre-training process and further improve the generalization ability and accuracy of final disparity estimation. Our proposed method was evaluated on several benchmark datasets and ranked first on KITTI 2012 leaderboard and second on KITTI 2015 leaderboard as of September 9. In addition, our method shows strong domain-across generalization and outperforms best prior work by a noteworthy margin with three or even five times faster speed. The code of MSMD-Net is available at https://github.com/gallenszl/MSMD-Net.
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