  In this work we study the mutual benefits of two common computer vision
tasks, self-supervised depth estimation and semantic segmentation from images.
For example, to help unsupervised monocular depth estimation, constraints from
semantic segmentation has been explored implicitly such as sharing and
transforming features. In contrast, we propose to explicitly measure the border
consistency between segmentation and depth and minimize it in a greedy manner
by iteratively supervising the network towards a locally optimal solution.
Partially this is motivated by our observation that semantic segmentation even
trained with limited ground truth (200 images of KITTI) can offer more accurate
border than that of any (monocular or stereo) image-based depth estimation.
Through extensive experiments, our proposed approach advances the state of the
art on unsupervised monocular depth estimation in the KITTI.
