Domain Decluttering: Simplifying Images to Mitigate
Synthetic-Real Domain Shift and Improve Depth Estimation

1University of California, Irvine    2Carnegie Mellon University
In CVPR, 2020


Leveraging synthetically rendered data offers great potential to improve monocular depth estimation, but closing the synthetic-real domain gap is a non-trivial and important task. While much recent work has focused on unsupervised domain adaptation, we consider a more realistic scenario where a large amount of synthetic training data is supplemented by a small set of real images with ground-truth. In this setting we find that existing domain translation approaches are difficult to train and offer little advantage over simple baselines that use a mix of real and synthetic data. A key failure mode is that real-world images contain novel objects and clutter not present in synthetic training. This high-level domain shift isn't handled by existing image translation models.

Based on these observations, we develop an attentional module that learns to identify and remove (hard) out-of-domain regions in real images in order to improve depth prediction for a model trained primarily on synthetic data. We carry out extensive experiments to validate our attend-remove-complete approach (ARC) and find that it significantly outperforms state-of-the-art domain adaptation methods for depth prediction. Visualizing the removed regions provides interpretable insights into the synthetic-real domain gap.


Citing this work

If you find this work useful in your research, please consider citing:

            title={Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation},
            author={Zhao, Yunhan and Kong, Shu and Shin, Daeyun and Fowlkes, Charless},
            booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},


This research was supported by NSF grants IIS-1813785, IIS-1618806, a research gift from Qualcomm, and a hardware donation from NVIDIA