  Scene segmentation via unsupervised domain adaptation (UDA) enables the
transfer of knowledge acquired from source synthetic data to real-world target
data, which largely reduces the need for manual pixel-level annotations in the
target domain. To facilitate domain-invariant feature learning, existing
methods typically mix data from both the source domain and target domain by
simply copying and pasting the pixels. Such vanilla methods are usually
sub-optimal since they do not take into account how well the mixed layouts
correspond to real-world scenarios. Real-world scenarios are with an inherent
layout. We observe that semantic categories, such as sidewalks, buildings, and
sky, display relatively consistent depth distributions, and could be clearly
distinguished in a depth map. Based on such observation, we propose a
depth-aware framework to explicitly leverage depth estimation to mix the
categories and facilitate the two complementary tasks, i.e., segmentation and
depth learning in an end-to-end manner. In particular, the framework contains a
Depth-guided Contextual Filter (DCF) forndata augmentation and a cross-task
encoder for contextual learning. DCF simulates the real-world layouts, while
the cross-task encoder further adaptively fuses the complementing features
between two tasks. Besides, it is worth noting that several public datasets do
not provide depth annotation. Therefore, we leverage the off-the-shelf depth
estimation network to generate the pseudo depth. Extensive experiments show
that our proposed methods, even with pseudo depth, achieve competitive
performance on two widely-used bench-marks, i.e. 77.7 mIoU on GTA to Cityscapes
and 69.3 mIoU on Synthia to Cityscapes.
