Unsupervised domain adaptation (UDA) is important for applications where
large scale annotation of representative data is challenging. For semantic
segmentation in particular, it helps deploy on real "target domain" data models
that are trained on annotated images from a different "source domain", notably
a virtual environment. To this end, most previous works consider semantic
segmentation as the only mode of supervision for source domain data, while
ignoring other, possibly available, information like depth. In this work, we
aim at exploiting at best such a privileged information while training the UDA
model. We propose a unified depth-aware UDA framework that leverages in several
complementary ways the knowledge of dense depth in the source domain. As a
result, the performance of the trained semantic segmentation model on the
target domain is boosted. Our novel approach indeed achieves state-of-the-art
performance on different challenging synthetic-2-real benchmarks.

该工作提出了一种统一的深度感知无监督域自适应框架，通过在源领域中利用密集深度的多种补充方式，最大限度地利用了这种特权信息来训练 UDA 模型，从而提高了目标领域上训练的语义分割模型的性能，并在不同的具有挑战性的 synthetic-2-real 基准测试上实现了最先进的性能。