  Leveraging synthetically rendered data offers great potential to improve
monocular depth estimation and other geometric estimation tasks, 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 attention module that learns to
identify and remove difficult 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.
