Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras
Open accessAbstract
We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal. Similarly to prior work, our method learns by applying differentiable warping to frames and comparing the result to adjacent ones, but it provides several improvements: We address occlusions geometrically and differentiably, directly using the depth maps as predicted during training. We introduce randomized layer normalization, a novel powerful regularizer, and we account for object motion relative to the scene. To the best of our knowledge, our work is the first to learn the camera intrinsic parameters, including lens distortion, from video in an unsupervised manner, thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale. We evaluate our results on the Cityscapes, KITTI and EuRoC datasets, establishing new state of the art on depth prediction and odometry, and demonstrate qualitatively that depth prediction can be learned from a collection of YouTube videos. The code will be open sourced once anonymity is lifted.
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Current research on depth estimation from monocular video mainly focuses on deep learning in an unsupervised or self-supervised way.
“In contrast to traditional matching and optimization-based methods, current research on this topic mainly focuses on deep learning in an unsupervised or self-supervised way [132].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [132]
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360MonoDepth: High-Resolution 360° Monocular Depth Estimation
Rey-Area, M., Yuan, M., & Richardt, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00374 · Cited by 89 · see citing works
Event-Based Angular Velocity Regression with Spiking Networks
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Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
Feng, Z., Yang, L., Jing, L., Wang, H., Tian, Y., & Li, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19824-3_14 · Cited by 88 · see citing works
Single Image Depth Prediction with Wavelet Decomposition
Ramamonjisoa, M., Firman, M., Watson, J., Lepetit, V., & Turmukhambetov, D. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01094 · Cited by 87 · see citing works
Focus on Defocus: Bridging the Synthetic to Real Domain Gap for Depth Estimation
Maximov, M., Galim, K., & Leal-Taixé, L. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00115 · Cited by 86 · see citing works
Kumar, V. R., Klingner, M., Yogamani, S. K., Milz, S., Fingscheidt, T., & Maeder, P. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00011 · Cited by 86 · see citing works
Distilled Semantics for Comprehensive Scene Understanding from Videos
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Ji, P., Li, R., Bhanu, B., & Xu, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01255 · Cited by 85 · see citing works
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark
Wang, K., Zhang, Z., Yan, Z., Li, X., Xu, B., Li, J., & Yang, J. J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01575 · Cited by 85 · see citing works
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
Liu, L., Song, X., Wang, M., Liu, Y., & Zhang, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01250 · Cited by 83 · see citing works
Factors of Influence for Transfer Learning Across Diverse Appearance Domains and Task Types
Mensink, T., Uijlings, J., Kuznetsova, A., Gygli, M., & Ferrari, V. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3129870 · Cited by 82 · see citing works
GCNDepth: Self-supervised monocular depth estimation based on graph convolutional network
Masoumian, A., Rashwan, H. A., Abdulwahab, S., Cristiano, J., Asif, M. S., & Puig, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.10.073 · Cited by 82 · see citing works
Toward Practical Monocular Indoor Depth Estimation
Wu, C.-Y., Wang, J., Hall, M., Neumann, U., & Su, S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00379 · Cited by 75 · see citing works
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion
Guizilini, V., Ambruş, R., Burgard, W., & Gaidon, A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01093 · Cited by 74 · see citing works
Fan, J., Wang, X., Zhou, C., Ou, Y., Jing, F., & Hou, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3235420 · Cited by 70 · see citing works
Pseudo RGB-D for Self-improving Monocular SLAM and Depth Prediction
Tiwari, L., Ji, P., Tran, Q.-H., Zhuang, B., Anand, S., & Chandraker, M. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_26 · Cited by 69 · see citing works
Enhancing optical-flow-based control by learning visual appearance cues for flying robots
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Detecting Deficient Coverage in Colonoscopies
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Self-citationRM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic Scenes
Hui, T.-W. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00172 · Cited by 69 · see citing works
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation
Wang, Y., Liang, Y., Xu, H., Jiao, S., & Yu, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28383 · Cited by 67 · see citing works
AnimePose: Multi-person 3D pose estimation and animation
Kumarapu, L., & Mukherjee, P. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.028 · Cited by 64 · see citing works
Petrovai, A., & Nedevschi, S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00163 · Cited by 64 · see citing works
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics
Varma, A., Chawla, H., Zonooz, B., & Arani, E. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010884000003124 · Cited by 63 · see citing works
Cui, B., Islam, M., Bai, L., Wang, A., & Ren, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72089-5_20 · Cited by 61 · see citing works
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesPreprint
Gonzalez, J. L., & Kim, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.03633 · Cited by 61 · see citing works
Full Surround Monodepth From Multiple Cameras
Guizilini, V., Vasiljevic, I., Ambruş, R., Shakhnarovich, G., & Gaidon, A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3150884 · Cited by 57 · see citing works
Learning Optical Flow, Depth, and Scene Flow Without Real-World Labels
Guizilini, V., Lee, K.-H., Ambruş, R., & Gaidon, A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3145057 · Cited by 55 · see citing works
On deep learning techniques to boost monocular depth estimation for autonomous navigation
Mendes, R. D. Q., Ribeiro, E. G., Rosa, N. D. S., & Grassi, V. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103701 · Cited by 55 · see citing works
Monocular Visual Traffic Surveillance: A Review
Zhang, X., Feng, Y., Angeloudis, P., & Demiris, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3147770 · Cited by 54 · see citing works
CoMoDA: Continuous Monocular Depth Adaptation Using Past Experiences
Kuznietsov, Y., Proesmans, M., & Gool, L. V. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00295 · Cited by 53 · see citing works
Liu, J., Li, Q., Cao, R., Tang, W., & Qiu, G. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.06.004 · Cited by 52 · see citing works
PlaneDepth: Self-Supervised Depth Estimation via Orthogonal Planes
Wang, R., Yu, Z., & Gao, S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02052 · Cited by 50 · see citing works
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
Schön, M., Buchholz, M., & Dietmayer, K. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01551 · Cited by 49 · see citing works
Hoyer, L., Dai, D., Wang, Q., Chen, Y., & Gool, L. V. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01799-6 · Cited by 48 · see citing works
Klingner, M., Bär, A., & Fingscheidt, T. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00168 · Cited by 47 · see citing works
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
Kumar, V. R., Klingner, M., Yogamani, S. K., Bach, M., Milz, S., Fingscheidt, T., & Mäder, P. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3088950 · Cited by 46 · see citing works
Self-supervised monocular depth estimation for gastrointestinal endoscopy
Liu, Y., & Zuo, S. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107619 · Cited by 43 · see citing works
3D Video Stabilization with Depth Estimation by CNN-based Optimization
Lee, Y.-C., Tseng, K.-W., Chen, Y., Chen, C., Chen, C., & Hung, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01048 · Cited by 42 · see citing works
Adversarial Patch Attacks on Monocular Depth Estimation Networks
Yamanaka, K., Matsumoto, R., Takahashi, K., & Fujii, T. (2020) · IEEE Access · doi:10.1109/access.2020.3027372 · Cited by 41 · see citing works
Stereo Visual SLAM for Autonomous Vehicles: A Review
Gao, B., Lang, H., & Jing Ren, A. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283161 · Cited by 41 · see citing works
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
Li, R., Gong, D., Yin, W., Chen, H., Zhu, Y., Wang, K., Chen, X., Sun, J., & Zhang, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02063 · Cited by 40 · see citing works
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
Watson, J., Aodha, O. M., Prisacariu, V. A., Brostow, G., & Firman, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00122 · Cited by 40 · see citing works
MedUCC: Medium-Driven Underwater Camera Calibration for Refractive 3-D Reconstruction
Gu, C., Cong, Y., Sun, G., Gao, Y., Tang, X., Zhang, T., & Fan, B. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3132146 · Cited by 39 · see citing works
Online Depth Learning Against Forgetting in Monocular Videos
Zhang, Z., Lathuilière, S., Ricci, E., Sebe, N., Yan, Y., & Yang, J. J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00455 · Cited by 38 · see citing works
Geometric Unsupervised Domain Adaptation for Semantic Segmentation
Guizilini, V., Li, J., Ambruş, R., & Gaidon, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00842 · Cited by 37 · see citing works
Bello, J. L. G., & Kim, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00678 · Cited by 37 · see citing works
Multi-modal Masked Pre-training for Monocular Panoramic Depth Completion
Yan, Z., 李翔, Wang, K., Zhang, Z., Li, J., & Yang, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19769-7_22 · Cited by 37 · see citing works
OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation
Li, P., Zhao, J., Wu, J., Deng, C., Han, Y., Wang, H., & Yu, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3296600 · Cited by 36 · see citing works
Adaptive confidence thresholding for monocular depth estimation
Choi, H., Lee, H., Kim, S. K., Kim, S., Kim, S., Sohn, K., & Min, D. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01257 · Cited by 36 · see citing works
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Miao, X., Bai, Y., Duan, H., Huang, Y., Wan, F., Xu, X., Long, Y., & Zheng, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3305776 · Cited by 35 · see citing works
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network
Han, W., Yin, J., & Shen, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00791 · Cited by 35 · see citing works
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation
Lee, S., Rameau, F., Pan, F., & Kweon, I. S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00482 · Cited by 35 · see citing works
Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem
Chen, X., Zhang, R., Jiang, J., Wang, Y., Li, G., & Li, T. H. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00573 · Cited by 35 · see citing works
Bangunharcana, A., Magd, A., & Kim, K.-S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00077 · Cited by 34 · see citing works
SimCol3D — 3D reconstruction during colonoscopy challenge
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Mapillary Planet-Scale Depth Dataset
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Crafting Monocular Cues and Velocity Guidance for Self-Supervised Multi-Frame Depth Learning
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Wang, G., Zhong, J., Zhao, S., Wu, W., Liu, Z., & Wang, H. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3215587 · Cited by 32 · see citing works
Self-Supervised Camera Self-Calibration from Video
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Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets
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Kwon, H., Abowd, G. D., & Plötz, T. (2021) · Sensors · doi:10.3390/s21248337 · Cited by 30 · see citing works
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A Learned Stereo Depth System for Robotic Manipulation in Homes
Shankar, K., Tjersland, M., Ma, J., Stone, K., & Bajracharya, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3143895 · Cited by 30 · see citing works
Deep geometry-aware camera self-calibration from video
Hagemann, A., Knorr, M., & Stiller, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00318 · Cited by 29 · see citing works
Self-Supervised Monocular Depth Estimation Using Hybrid Transformer Encoder
Hwang, S.-J., Park, S. J., Baek, J.-H., & Kim, B. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3199265 · Cited by 29 · see citing works
DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
Zhou, K., Hong, L., Chen, C., Xu, H., Ye, C., Hu, Q., & Li, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19842-7_8 · Cited by 29 · see citing works
- Authors
- Gordon, A., Li, H., Jonschkowski, R., & Angelova, A.
- Affiliations
- Google (United States) (US)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 8976-8985
- OpenAlex
- W2935920407
- Cited by
- 389
- References
- 52
- Citations by year
- 2026: 17 · 2025: 21 · 2024: 51 · 2023: 72 · 2022: 73 · 2021: 86 · 2020: 60 · 2019: 9
- FWCI
- 18.60
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- camera motion estimation, object motion estimation, lens distortion, occlusion handling
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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.
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