Depth And Camera Motion
13 papers with code • 0 benchmarks • 1 datasets
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We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences.
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.
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.
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community.
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
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.
We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences.
Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO).