Papers › GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Zhichao Yin, Jianping Shi
We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos. The three components are coupled by the nature of 3D scene geometry, jointly learned by our framework in an end-to-end manner. Specifically, geometric relationships are extracted over the predictions of individual modules and then combined as an image reconstruction loss, reasoning about static and dynamic scene parts separately. Furthermore, we propose an adaptive geometric consistency loss to increase robustness towards outliers and non-Lambertian regions, which resolves occlusions and texture ambiguities effectively. Experimentation on the KITTI driving dataset reveals that our scheme achieves state-of-the-art results in all of the three tasks, performing better than previously unsupervised methods and comparably with supervised ones.
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Code
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Code Syntology ran Syntology
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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 | GeoNet | Absolute Trajectory Error [m] | 100.75 | #5 of 7 | Archive leaderboard | report |
| Camera Pose Estimation | KITTI Odometry Benchmark | GeoNet | Average Rotational Error er[%] | 9.40 | #5 of 7 | Archive leaderboard | report |
| Camera Pose Estimation | KITTI Odometry Benchmark | GeoNet | Average Translational Error et[%] | 26.31 | #5 of 7 | Archive leaderboard | report |
| Pose Estimation | KITTI 2015 | GeoNet | Average End-Point Error | 10.81 | #1 of 1 | 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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