  We propose GeoNet, a jointly unsupervised learning framework for monocular
depth, optical flow and ego-motion estimation from videos. The three components
are coupled by the nature of 3D scene geometry, jointly learned by our
framework in an end-to-end manner. Specifically, geometric relationships are
extracted over the predictions of individual modules and then combined as an
image reconstruction loss, reasoning about static and dynamic scene parts
separately. Furthermore, we propose an adaptive geometric consistency loss to
increase robustness towards outliers and non-Lambertian regions, which resolves
occlusions and texture ambiguities effectively. Experimentation on the KITTI
driving dataset reveals that our scheme achieves state-of-the-art results in
all of the three tasks, performing better than previously unsupervised methods
and comparably with supervised ones.
