Unsupervised Monocular Depth Estimation with Left-Right Consistency
Abstract
Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage. We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Ex-ploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.
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Monocular visual odometry recovers translation only up to an unknown scale because depth is unobservable from a monocular camera.
“Monocular VO approaches recover translation up to an unknown scale [13], [14], [15], [16] as depth is unobservable from monocular cameras and thus there is ambiguity in the scale of translational estimates.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [13]
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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
Learning From Synthetic Photorealistic Raindrop for Single Image Raindrop Removal
Hao, Z., You, S., Li, Y., Li, K., & Lu, F. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00534 · Cited by 54 · see citing works
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
Motion and Depth Augmented Semantic Segmentation for Autonomous Navigation
Rashed, H., El Sallab, A., Yogamani, S., & ElHelw, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00049 · Cited by 38 · 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
Duran, O., & Turan, B. (2022) · Measurement · doi:10.1016/j.measurement.2022.110732 · Cited by 25 · 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
- Authors
- Godard, C., Aodha, O. M., & Brostow, G. J.
- Affiliations
- University College London (GB)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 6602-6611
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2520707372
- Cited by
- 3,270
- References
- 61
- Citations by year
- 2026: 139 · 2025: 283 · 2024: 374 · 2023: 416 · 2022: 407 · 2021: 521 · 2020: 468 · 2019: 429 · 2018: 208 · 2017: 25
- FWCI
- 90.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/K023578/1, EP/K015664/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Keywords
- monocular depth estimation, unsupervised learning, left-right consistency, stereo disparity, epipolar geometry
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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