Self-Supervised Learning of Domain Invariant Features for Depth Estimation

@article{Akada2021SelfSupervisedLO,
  title={Self-Supervised Learning of Domain Invariant Features for Depth Estimation},
  author={Hiroyasu Akada and S. Bhat and Ibraheem Alhashim and Peter Wonka},
  journal={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2021},
  pages={997-1007}
}
We tackle the problem of unsupervised synthetic-to-real domain adaptation for single image depth estimation. An essential building block of single image depth estimation is an encoder-decoder task network that takes RGB images as input and produces depth maps as output. In this paper, we propose a novel training strategy to force the task network to learn domain invariant representations in a self-supervised manner. Specifically, we extend self-supervised learning from traditional… 

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