  Unsupervised learning for geometric perception (depth, optical flow, etc.) is
of great interest to autonomous systems. Recent works on unsupervised learning
have made considerable progress on perceiving geometry; however, they usually
ignore the coherence of objects and perform poorly under scenarios with dark
and noisy environments. In contrast, supervised learning algorithms, which are
robust, require large labeled geometric dataset. This paper introduces SIGNet,
a novel framework that provides robust geometry perception without requiring
geometrically informative labels. Specifically, SIGNet integrates semantic
information to make depth and flow predictions consistent with objects and
robust to low lighting conditions. SIGNet is shown to improve upon the
state-of-the-art unsupervised learning for depth prediction by 30% (in squared
relative error). In particular, SIGNet improves the dynamic object class
performance by 39% in depth prediction and 29% in flow prediction. Our code
will be made available at https://github.com/mengyuest/SIGNet
