  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 semantic segmentation,
which is enhanced by self-supervised monocular depth estimation from unlabeled
image sequences. In particular, we propose three key contributions: (1) We
transfer knowledge from features learned during self-supervised depth
estimation to semantic segmentation, (2) we implement a strong data
augmentation by blending images and labels using the geometry of the scene, and
(3) we utilize the depth feature diversity as well as the level of difficulty
of learning depth in a student-teacher framework to select the most useful
samples to be annotated for semantic segmentation. We validate the proposed
model on the Cityscapes dataset, where all three modules demonstrate
significant performance gains, and we achieve state-of-the-art results for
semi-supervised semantic segmentation. The implementation is available at
https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth.
