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.
@article{arxiv.2106.00318,
title = {Semi-Supervised Disparity Estimation with Deep Feature Reconstruction},
author = {Julia Guerrero-Viu and Sergio Izquierdo and Philipp Schröppel and Thomas Brox},
journal= {arXiv preprint arXiv:2106.00318},
year = {2021}
}