Unsupervised Learning of Depth and Ego-Motion from Video
Open accessAbstract
We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. In common with recent work [10, 14, 16], we use an end-to-end learning approach with view synthesis as the supervisory signal. In contrast to the previous work, our method is completely unsupervised, requiring only monocular video sequences for training. Our method uses single-view depth and multiview pose networks, with a loss based on warping nearby views to the target using the computed depth and pose. The networks are thus coupled by the loss 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 performs comparably with supervised methods that use either ground-truth pose or depth for training, and 2) pose estimation performs favorably compared to established SLAM systems under comparable input settings.
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The main supervision signal in the self-supervised framework is photometric consistency across multiple views.
“Instead of using ground truth supervisions, the main supervision signal in the selfsupervised framework is photometric consistency across multiple-views.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Zhou et al. 2017
Deep neural networks can learn scene depths and relative camera motion in a self-supervised manner without ground truth labels.
“Recent studies show that deep neural networks can learn scene depths and relative camera in a self-supervised manner without acquiring ground truth labels.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Zhou et al. 2017
The online error correction modules increase VIO robustness to spatial misalignments between camera and IMU that affect state-of-the-art traditional VIO methods.
“Additionally, we show how the OEC modules provide increased VIO robustness against spatial misalignments between a camera and IMU that plague SoA traditional approaches to VIO [3], [4], [5].”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [3]
OKVIS is an optimization-based keyframe visual-inertial SLAM method for monocular and stereo cameras, and it does not include loop closure.
“OKVIS [3] is a optimization-based keyframe visual-inertial SLAM approach for monocular and stereo cameras.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [3]
Progressive expansion and contraction in a fully invertible manner, without discarding uninformative input variability, increases performance across many tasks.
“In fact, progressive expansion and contraction in a fully invertible manner, without discarding”
From 3D Packing for Self-Supervised Monocular Depth Estimation (2020) · cites this paper as [26]
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3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers
Shin, D., Ren, Z., Sudderth, E., & Fowlkes, C. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00226 · Cited by 57 · see citing works
Barnes, D., Maddern, W., Pascoe, G., & Posner, I. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460564 · Cited by 57 · see citing works
Multimodal fusion and vision–language models: A survey for robot vision
Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works
“Super-vised methods excel in constrained settings by utilizing labeled datasets, while self-supervised approaches leverage geometric and photometric consistency to adapt effectively to unseen environments [139].”
Shamwell, E. J., Leung, S., & Nothwang, W. D. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593573 · Cited by 41 · see citing works
DF-VO: What Should Be Learnt for Visual Odometry?
Zhan, H., Weerasekera, C. S., Bian, J.-W., Garg, R., & Reid, I. (2021) · arXiv · doi:10.48550/arxiv.2103.00933 · Cited by 31 · see citing works
Kwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., & Ploetz, T. (2020) · arXiv · doi:10.48550/arxiv.2006.05675 · Cited by 14 · see citing works
Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
NVIDIA, :, Alhaija, H. A., Alvarez, J., Bala, M., Cai, T., Cao, T., Cha, L., Chen, J., Chen, M., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.14492 · Cited by 2 · see citing works
Stereo Magnification: Learning View Synthesis using Multiplane Images
Zhou, T., Tucker, R., Flynn, J., Fyffe, G., & Snavely, N. (2018) · arXiv · doi:10.48550/arxiv.1805.09817 · Cited by 150 · see citing works
Self-citation
- Authors
- Zhou, T., Brown, M., Snavely, N., & Lowe, D. G.
- Affiliations
- University of California, Berkeley (US)
- Google (United States) (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 6612-6619
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2609883120
- Cited by
- 2,910
- References
- 57
- Citations by year
- 2026: 146 · 2025: 271 · 2024: 324 · 2023: 383 · 2022: 359 · 2021: 453 · 2020: 407 · 2019: 366 · 2018: 183 · 2017: 18
- FWCI
- 77.95
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1539099)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- monocular depth estimation, camera motion estimation, camera pose estimation, unsupervised learning, view synthesis, end-to-end learning, monocular video
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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