  We present an approach which takes advantage of both structure and semantics
for unsupervised monocular learning of depth and ego-motion. More specifically,
we model the motion of individual objects and learn their 3D motion vector
jointly with depth and ego-motion. We obtain more accurate results, especially
for challenging dynamic scenes not addressed by previous approaches. This is an
extended version of Casser et al. [AAAI'19]. Code and models have been open
sourced at https://sites.google.com/corp/view/struct2depth.

Computer Vision and Pattern Recognition;Robotics