Papers › Unsupervised Learning of Depth and Ego-Motion from Video
Unsupervised Learning of Depth and Ego-Motion from Video
Tinghui Zhou, Matthew Brown, Noah Snavely, David G. Lowe
We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. We achieve this by simultaneously training depth and camera pose estimation networks using the task of view synthesis as the supervisory signal. The networks are thus coupled via the view synthesis objective during training, but can be applied independently at test time. Empirical evaluation on the KITTI dataset demonstrates the effectiveness of our approach: 1) monocular depth performing comparably with supervised methods that use either ground-truth pose or depth for training, and 2) pose estimation performing favorably with established SLAM systems under comparable input settings.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Camera Pose Estimation | KITTI Odometry Benchmark | SfMLearner | Absolute Trajectory Error [m] | 72.57 | #6 of 7 | Archive leaderboard | report |
| Camera Pose Estimation | KITTI Odometry Benchmark | SfMLearner | Average Rotational Error er[%] | 12.26 | #6 of 7 | Archive leaderboard | report |
| Camera Pose Estimation | KITTI Odometry Benchmark | SfMLearner | Average Translational Error et[%] | 29.78 | #6 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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