  Simulators can efficiently generate large amounts of labeled synthetic data
with perfect supervision for hard-to-label tasks like semantic segmentation.
However, they introduce a domain gap that severely hurts real-world
performance. We propose to use self-supervised monocular depth estimation as a
proxy task to bridge this gap and improve sim-to-real unsupervised domain
adaptation (UDA). Our Geometric Unsupervised Domain Adaptation method (GUDA)
learns a domain-invariant representation via a multi-task objective combining
synthetic semantic supervision with real-world geometric constraints on videos.
GUDA establishes a new state of the art in UDA for semantic segmentation on
three benchmarks, outperforming methods that use domain adversarial learning,
self-training, or other self-supervised proxy tasks. Furthermore, we show that
our method scales well with the quality and quantity of synthetic data while
also improving depth prediction.
