Focus on Defocus: Bridging the Synthetic to Real Domain Gap for Depth Estimation

Maxim Maximov, Kevin Galim, Laura Leal-Taixe; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1071-1080

Abstract


Data-driven depth estimation methods struggle with the generalization outside their training scenes due to the immense variability of the real-world scenes. This problem can be partially addressed by utilising synthetically generated images, but closing the synthetic-real domain gap is far from trivial. In this paper, we tackle this issue by using domain invariant defocus blur as direct supervision. We leverage defocus cues by using a permutation invariant convolutional neural network that encourages the network to learn from the differences between images with a different point of focus. Our proposed network uses the defocus map as an intermediate supervisory signal. We are able to train our model completely on synthetic data and directly apply it to a wide range of real-world images. We evaluate our model on synthetic and real datasets, showing compelling generalization results and state-of-the-art depth prediction. The dataset and code are available at https://github.com/dvl-tum/defocus-net.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Maximov_2020_CVPR,
author = {Maximov, Maxim and Galim, Kevin and Leal-Taixe, Laura},
title = {Focus on Defocus: Bridging the Synthetic to Real Domain Gap for Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}