Motion (physics)

Psychoanalysis

Set (abstract data type)

Computer science

Semantics (computer science)

Code (set theory)

Psychology

Id, ego and super-ego

Artificial intelligence

Programming language

Monocular

Unsupervised learning

Computer vision

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 motions 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. [1]. Code and models have been open sourced at: https://sites.google.com/corp/view/struct2depth.

Unsupervised Monocular Depth and Ego-Motion Learning With Structure and Semantics