Papers › Unsupervised Learning of Depth and Ego-Motion from Video

Unsupervised Learning of Depth and Ego-Motion from Video

25 Apr 2017CVPR 2017 7arXiv:1704.07813archive 2025-07-28

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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tinghuiz/SfMLearner officialmentioned in papertf report

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Tasks

Camera Pose EstimationDepth And Camera MotionDepth EstimationMotion EstimationPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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