  We propose a semantics-driven unsupervised learning approach for monocular
depth and ego-motion estimation from videos in this paper. Recent unsupervised
learning methods employ photometric errors between synthetic view and actual
image as a supervision signal for training. In our method, we exploit semantic
segmentation information to mitigate the effects of dynamic objects and
occlusions in the scene, and to improve depth prediction performance by
considering the correlation between depth and semantics. To avoid costly
labeling process, we use noisy semantic segmentation results obtained by a
pre-trained semantic segmentation network. In addition, we minimize the
position error between the corresponding points of adjacent frames to utilize
3D spatial information. Experimental results on the KITTI dataset show that our
method achieves good performance in both depth and ego-motion estimation tasks.
