Self-supervised learning of depth and motion from monocular images
Over the past few years, there has been increasing attention on the self-supervised learning of depth and motion estimation from a sequence of monocular images. Despite the ill-posed property of estimating depth or scene flow from monocular images, designed proxy losses enable to learn to estimate them without dense annotation. However, the accuracy of the current approaches highly depends on the proxy losses, which becomes one of the main limitations. In this talk, we are going to review the literature on estimating depth, optical flow and scene flow using monocular images, their proxy loss designs with limitations, and possible future direction.
Junhwa Hur has been working as a Ph.D. student at Technische Universität Darmstadt, advised by Prof. Stefan Roth. He received his MS degree from Seoul National University in 2013. His main research interests lie on dynamic scene understanding (e.g., jointly estimating motion, depth, and semantic segmentation) using self-supervised learning. He has been acknowledged as one of the outstanding reviewers multiple times in CVPR or ECCV.
2020-12-01 at 3:30 pm