  Although recent semantic segmentation methods have made remarkable progress,
they still rely on large amounts of annotated training data, which are often
infeasible to collect in the autonomous driving scenario. Previous works
usually tackle this issue with Unsupervised Domain Adaptation (UDA), which
entails training a network on synthetic images and applying the model to real
ones while minimizing the discrepancy between the two domains. Yet, these
techniques do not consider additional information that may be obtained from
other tasks. Differently, we propose to exploit self-supervised monocular depth
estimation to improve UDA for semantic segmentation. On one hand, we deploy
depth to realize a plug-in component which can inject complementary geometric
cues into any existing UDA method. We further rely on depth to generate a large
and varied set of samples to Self-Train the final model. Our whole proposal
allows for achieving state-of-the-art performance (58.8 mIoU) in the GTA5->CS
benchmark benchmark. Code is available at
https://github.com/CVLAB-Unibo/d4-dbst.
