AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching

In this paper, we attempt to solve the domain adaptation problem for deep stereo matching networks. Instead of resorting to black-box structures or layers to find implicit connections across domains, we focus on investigating adaptation gaps for stereo matching. By visual inspections and extensive experiments, we conclude that low-level aligning is crucial for adaptive stereo matching, since main gaps across domains lie in the inconsistent input color and cost volume distributions. Correspondingly, we design a bottom-up domain adaptation method, in which two particular approaches are proposed, i.e. color transfer and cost regularization, that can be easily integrated into existing stereo matching models. The color transfer enables transferring a large amount of synthetic data to the same color spaces with target domains during training. The cost regularization can further constrain both the lower-layer features and cost volumes to domain-invariant distributions. Although our proposed strategies are simple and have no parameters to learn, they do improve the generalization ability of existing disparity networks by a large margin. We conduct experiments across multiple datasets, including Scene Flow, KITTI, Middlebury, ETH3D and DrivingStereo. Without whistles and bells, our synthetic-data pretrained models achieve state-of-the-art cross-domain performance compared to previous domain-invariant methods, even outperform state-of-the-art disparity networks fine-tuned with target domain ground-truths on multiple stereo matching benchmarks.