  Large-scale synthetic datasets are beneficial to stereo matching but usually
introduce known domain bias. Although unsupervised image-to-image translation
networks represented by CycleGAN show great potential in dealing with domain
gap, it is non-trivial to generalize this method to stereo matching due to the
problem of pixel distortion and stereo mismatch after translation. In this
paper, we propose an end-to-end training framework with domain translation and
stereo matching networks to tackle this challenge. First, joint optimization
between domain translation and stereo matching networks in our end-to-end
framework makes the former facilitate the latter one to the maximum extent.
Second, this framework introduces two novel losses, i.e., bidirectional
multi-scale feature re-projection loss and correlation consistency loss, to
help translate all synthetic stereo images into realistic ones as well as
maintain epipolar constraints. The effective combination of above two
contributions leads to impressive stereo-consistent translation and disparity
estimation accuracy. In addition, a mode seeking regularization term is added
to endow the synthetic-to-real translation results with higher fine-grained
diversity. Extensive experiments demonstrate the effectiveness of the proposed
framework on bridging the synthetic-to-real domain gap on stereo matching.
