  Recently, records on stereo matching benchmarks are constantly broken by
end-to-end disparity networks. However, the domain adaptation ability of these
deep models is quite poor. Addressing such problem, we present a novel
domain-adaptive pipeline called AdaStereo that aims to align multi-level
representations for deep stereo matching networks. Compared to previous methods
for adaptive stereo matching, our AdaStereo realizes a more standard, complete
and effective domain adaptation pipeline. Firstly, we propose a non-adversarial
progressive color transfer algorithm for input image-level alignment. Secondly,
we design an efficient parameter-free cost normalization layer for internal
feature-level alignment. Lastly, a highly related auxiliary task,
self-supervised occlusion-aware reconstruction is presented to narrow down the
gaps in output space. Our AdaStereo models achieve state-of-the-art
cross-domain performance on multiple stereo benchmarks, including KITTI,
Middlebury, ETH3D, and DrivingStereo, even outperforming disparity networks
finetuned with target-domain ground-truths.
