We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion.
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.
Per-pixel ground-truth depth data is challenging to acquire at scale.
#7 best model for Monocular Depth Estimation on KITTI Eigen split
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community.
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner.
To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.
#13 best model for Monocular Depth Estimation on KITTI Eigen split (using extra training data)