  Training deep networks for semantic segmentation requires large amounts of
labeled training data, which presents a major challenge in practice, as
labeling segmentation masks is a highly labor-intensive process. To address
this issue, we present a framework for semi-supervised and domain-adaptive
semantic segmentation, which is enhanced by self-supervised monocular depth
estimation (SDE) trained only on unlabeled image sequences.
  In particular, we utilize SDE as an auxiliary task comprehensively across the
entire learning framework: First, we automatically select the most useful
samples to be annotated for semantic segmentation based on the correlation of
sample diversity and difficulty between SDE and semantic segmentation. Second,
we implement a strong data augmentation by mixing images and labels using the
geometry of the scene. Third, we transfer knowledge from features learned
during SDE to semantic segmentation by means of transfer and multi-task
learning. And fourth, we exploit additional labeled synthetic data with
Cross-Domain DepthMix and Matching Geometry Sampling to align synthetic and
real data.
  We validate the proposed model on the Cityscapes dataset, where all four
contributions demonstrate significant performance gains, and achieve
state-of-the-art results for semi-supervised semantic segmentation as well as
for semi-supervised domain adaptation. In particular, with only 1/30 of the
Cityscapes labels, our method achieves 92% of the fully-supervised baseline
performance and even 97% when exploiting additional data from GTA. The source
code is available at
https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth.
