Google Research

Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics

CVPR Workshop on Visual Odometry & Computer Vision Applications Based on Location Clues (VOCVALC) (2019)

Abstract

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/view/struct2depth.

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