  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 limited. Addressing such problem, we present a novel
domain-adaptive approach called AdaStereo that aims to align multi-level
representations for deep stereo matching networks. Compared to previous
methods, 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 the gaps in output space.
We perform intensive ablation studies and break-down comparisons to validate
the effectiveness of each proposed module. With no extra inference overhead and
only a slight increase in training complexity, our AdaStereo models achieve
state-of-the-art cross-domain performance on multiple benchmarks, including
KITTI, Middlebury, ETH3D and DrivingStereo, even outperforming some
state-of-the-art disparity networks finetuned with target-domain ground-truths.
Moreover, based on two additional evaluation metrics, the superiority of our
domain-adaptive stereo matching pipeline is further uncovered from more
perspectives. Finally, we demonstrate that our method is robust to various
domain adaptation settings, and can be easily integrated into quick adaptation
application scenarios and real-world deployments.
