  Despite the success of deep learning in disparity estimation, the domain
generalization gap remains an issue. We propose a semi-supervised pipeline that
successfully adapts DispNet to a real-world domain by joint supervised training
on labeled synthetic data and self-supervised training on unlabeled real data.
Furthermore, accounting for the limitations of the widely-used photometric
loss, we analyze the impact of deep feature reconstruction as a promising
supervisory signal for disparity estimation.
