The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
(2014) · Cited by 33,419
Layer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,762
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Eigen, D., Puhrsch, C., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1406.2283 · Cited by 3,442
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270
Flagged first, then by citations
Research works printed but not linked: 3
- schonberger, 2016, Structurefrom-motion revisited, CVPR
- godard, 2017, Unsupervised monocular depth estimation with leftright consistency, CVPR
- casser, 2019, Unsupervised learning of depth and ego-motion: A structured approach, AAAI-19
Cited by
Sorted by citations
Items 301–385 of 385
Monocular Depth Estimation with Traditional Stereo Matching Information
Ren, H., Gao, N., & Li, J. (2019) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai48974.2019.9010573 · Cited by 2
Deep Monocular Video Depth Estimation Using Temporal Attention
Ren, H., El‐Khamy, M., & Lee, J. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053408 · Cited by 2
Unsupervised Visual Learning: From Pixels to Seeing
Leordeanu, M. (2020) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-42128-1_1 · Cited by 2
Automatic Detection of LST-Type Polyp by CNN Using Depth Map
Iwahori, Y., Miyazaki, S., Usami, H., Bhuyan, M. K., Kijsirikul, B., Wang, A., Ogasawara, N., & Kasugai, K. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-79161-2_7 · Cited by 2
Potential of embedded vision platforms in development of spatial AI enabled CPS
Brkljač, B., Antic, B., & Mitrović, Z. (2022) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco55406.2022.9797078 · Cited by 2
Self-distilled Self-supervised Depth Estimation in Monocular Videos
Mendoza, J., & Pedrini, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09037-0_35 · Cited by 2
Does it work outside this benchmark? Introducing the rigid depth constructor tool
Pinard, C., & Antoine Manzanera, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14743-0 · Cited by 2
Improving Semantic Segmentation through Spatio-Temporal Consistency Learned from VideosPreprint
Pasad, A., Gordon, A. M., Lin, T.-Y., & Angelova, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.05324 · Cited by 2
Self-citationMonocular thermal imaging for pedestrian detection and ranging
Petilli, E. (2022) · Optical Engineering · doi:10.1117/1.oe.62.3.031212 · Cited by 1
He, J.-L., Chen, Y.-H., Putri, W. R., Huang, C.-I., Su, M.-H., Li, K.-C., Wang, J., Chen, S., Li, Y., & Wang, J. (2025) · Sensors · doi:10.3390/s25206404 · Cited by 1
Fang, J. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3611842 · Cited by 1
Dong, J., Chen, G., Piras, M., Bo, Y., Hu, M., Lin, Z., Wang, T., Zhang, Z., & Hu, L. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3663630 · Cited by 1
Wang, Z., Shen, M., & Chen, Q. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18382-x · Cited by 1
OD-MVSNet: Omni-dimensional dynamic multi-view stereo network
Pan, K., Li, K., Zhang, G., Zhu, Z., Wang, P., Wang, Z., Fu, C., Li, G., & Ding, Y. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309029 · Cited by 1
Zhang, C., Clarkson, M. J., & Islam, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13961-0_30 · Cited by 1
Depth estimation using an improved stereo network
Xu, W., Zou, L., Wu, L., Qi, Y., & Qian, Z. (2022) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2000676 · Cited by 1
Mapping the Ocular Surface from Monocular Videos with an Application to Dry Eye Disease Grading
Brahim, I., Lamard, M., Benyoussef, A., Conze, P.-H., Cochener, B., Cornec, D., & Quellec, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16525-2_7 · Cited by 1
Hirose, N., & Tahara, K. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981751 · Cited by 1
ADfM-Net: An Adversarial Depth-From-Motion Network Based on Cross Attention and Motion Enhanced
Long, Y., Yu, H., Xu, C., & Deng, Z. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3268885 · Cited by 1
Self-Supervised Steering and Path Labeling for Autonomous Driving
Mihalea, A., Samoilescu, R.-F., & Florea, A. M. (2023) · Sensors · doi:10.3390/s23208473 · Cited by 1
Improved Stereo Depth Estimation Using Smoothness and Geometrical Attention
Zarei, S.-P., Hamid Soltanian-zadeh, A., & Kalhor, A. (2024) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip62238.2024.10491151 · Cited by 1
Leveraging self-supervised label generation in ego-trajectory segmentation for self-driving vehicles
Mihalea, A., & Florea, A. M. (2023) · International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) · doi:10.1109/synasc61333.2023.00026 · Cited by 1
Wu, G., Chen, L., & Chen, Z. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521846 · Cited by 1
Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints
Chen, K., Pogue, A., Lopez, B. T., Agha–mohammadi, A., & Mehta, A. M. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636030 · Cited by 1
3D Reconstruction of Edge Line by ICP-based Matching with Geometric Constraints
Takeyama, K. (2020) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta51227.2020.9363373 · Cited by 1
Self-Supervised Visual Odometry with Ego-Motion Sampling
Slinko, I., Vorontsova, A., Zhukov, D., Barinova, O., & Konushin, A. (2020) · International Conference on Video, Signal and Image Processing (VSIP) · doi:10.1145/3442705.3442706 · Cited by 1
Parameterization of Ambiguity in Monocular Depth Prediction
Persson, P., Öström, L., Olsson, C., & Åström, K. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00085 · Cited by 1
Using Self-Supervised Depth and Uncertainty Predictions for Monocular Visual Odometry
Kuang, Y., Huang, P., & Lv, T. (2022) · International Conference on Image and Graphics Processing (ICIGP) · doi:10.1145/3512388.3512435 · Cited by 1
Test‐Time Optimization for Video Depth Estimation Using Pseudo Reference Depth
Zeng, L., & Kalantari, N. K. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14729 · Cited by 1
Akers, J., Buck, A. R., Anderson, D. T., Keller, J. M., Camaioni, R., Deardorff, M., & Luke, R. (2023) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr60534.2023.10440660 · Cited by 1
Depth Map Estimation of Dynamic Scenes Using Prior Depth InformationPreprint
Noraky, J., & Sze, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00297 · Cited by 1
Distilled Semantics for Comprehensive Scene Understanding from VideosPreprint
Tosi, F., Aleotti, F., Ramirez, P. Z., Poggi, M., Salti, S., Stefano, L. D., & Mattoccia, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14030 · Cited by 1
Increased-Range Unsupervised Monocular Depth EstimationPreprint
Imran, S. A., Khan, M. U. K., Mukarram, S. B., & Kyung, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12791 · Cited by 1
Xu, X., Yu, D. S., & Zhu, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.00398 · Cited by 1
Hirose, N., & Tahara, K. (2021) · arXiv (Cornell University) · Cited by 1
Adversarially Robust Frame Sampling with Bounded IrregularitiesPreprint
Li, H., & Wang, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.01147
Self-citationUnsupervised Learning of Monocular Depth from Videos
Haosheng, G., & Wang, T. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8996631
Self-Calibration Supported Robust Projective Structure-from-MotionPreprint
Gong, R., Paudel, D. P., Chhatkuli, A., & Gool, L. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02045
Practical Auto-calibration for Spatial Scene-understanding from Crowdsourced Dashcamera Videos
Chawla, H., Jukola, M., Marzban, S., Arani, E., & Zonooz, B. (2021) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010255808690880
Towards Continual, Online, Unsupervised Depth.Preprint
Khan, M. U. K. (2021) · arXiv (Cornell University)
Improved Point Transformation Methods For Self-Supervised Depth Prediction
Chen, Z., Guo, Z., & Weinman, J. J. (2021) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv52889.2021.00023
Robust Camera Motion Estimation for Point-of-View Video Stabilization
Lee, W., Yoo, B., Kim, D., Lee, J., Yim, S., Kwon, T., Lee, G.-I., & Jeong, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77599-5_25
Single Image Depth Prediction with Wavelet DecompositionPreprint
Ramamonjisoa, M., Firman, M., Watson, J., Lepetit, V., & Turmukhambetov, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02022
Mendes, R. D. Q. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.18.2021.tde-02092021-110320
Schmidt, M., Stannartz, N., & Bertram, T. (2021) · Proceedings · doi:10.1007/978-3-658-34752-9_17
Dimensions of Motion: Learning to Predict a Subspace of Optical Flow from a Single Image.Preprint
Bowen, R. S., Tucker, R. P., Ramin Zabih, A., & Snavely, N. (2021) · arXiv (Cornell University)
Mekhzoum, H., Bourja, O., Abdelali, H. A., & Derrouz, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1827874/v1
Monocular Depth Estimation of Indoor Environments Using Enhanced Rgb-D DatasetPreprint
Kim, J., Shin, Y.-G., & Chung, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4172041
Mekhzoum, H., Bourja, O., Abdelali, H. A., & Derrouz, H. (2022) · Research Square · doi:10.21203/rs.3.rs-1827874/v2
Self-Supervised Ground-Relative Pose Estimation
Muller, B. R., & Smith, W. A. P. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956720
A LiDAR Based Control Solution to Achieve High Precision in Autonomous Parking
Xu, X., Dong, Y., & Zhu, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20862-1_10
Ruler in Water: 6-DOF Underwater Rotating Platform Calibration for Laser Scanner
Gao, Y., Cong, Y., Gu, C., & Tang, X. (2022) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac57282.2022.10023915
Splatting-Based View Synthesis for Self-supervised Monocular Depth Estimation
Liu, J., Leng, J., Liu, B., Huang, W., & Sun, C. (2023) · International Conference on Electrical Engineering, Control and Robotics (EECR) · doi:10.1109/eecr56827.2023.10150161
Online Consistent Video Depth with Gaussian Mixture Representation
Liu, C., Eckart, B., & Kautz, J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160785
Road Scene Risk Estimation Using Driving Video
Kishimoto, M., & Iiyama, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35939-2_29
DIVS: A Real-Time Video Stabilization Framework for Consumer Camera
Kasyap, V. L. V. S. K. B., Sumathi, S., Adhikari, A., & Bhagavan, V. S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-4284-8_28
Replay-Based Online Adaptation for Unsupervised Deep Visual Odometry
Kuznietsov, Y., Proesmans, M., & Gool, L. V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49018-7_48
Self-supervised Monocular Depth Estimation on Unseen Synthetic Cameras
Diana-Albelda, C., Pérez-Villar, J. I. B., Montalvo, J., García‐Martín, Á., & Bescós, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49018-7_32
Xiang, X., Yan, Q., Qiao, Y., & Saddik, A. E. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4768854
Jiang, X., Zhu, H., An, N., Xie, B., Wei, H., Tang, F., & Wu, Y. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19663-1
A Breeding Animal Detection Method from Images With Grided Heatmaps
Zhang, H., Gu, D., & Liang, K. (2024) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma61710.2024.10633158
Vehicle Ego-Trajectory Segmentation Using Guidance Cues
Mihalea, A., & Florea, A. M. (2024) · Applied Sciences · doi:10.3390/app14177776
Singh, O., Biswas, A., & Paul, R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-5412-0_13
3D Geometry and Semantic Reconstruction
Yang, X., Yin, B., & Wei, X. (2025) · Springer eBooks · doi:10.1007/978-981-96-3177-3_1
Self-Supervised Multi-Camera Collaborative Depth Prediction With Latent Diffusion Models
Xu, J., Liu, X., Bai, Y., Jiang, J., & Ji, X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3571027 · Cited by 3
SPIdepth: A Novel Approach to Enhancing Monocular Depth Estimation with Strengthened Pose Networks
Лавренюк, М. С., & Лавренюк, А. М. (2024) · International Conference on Dependable Systems, Services and Technologies (DESSERT) · doi:10.1109/dessert65323.2024.11122216
Pu, Z., Chen, S., Zou, B., & Pu, B. (2023) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2023.19299
Huang, Z., Feng, X., & Niu, K. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357225
Wu, B., & Chen, J. (2026) · International Journal of Applied Science · doi:10.30560/ijas.v9n1p49
Xie, H., & Wu, Y. (2026) · IET Image Processing · doi:10.1049/ipr2.70309
On depth prediction for autonomous driving using self-supervised learning
Boulahbal, H. E. (2023) · theses.fr (ABES) · doi:10.70675/273dde48zd8b7z42b8z9c85z52bfc7aa4bba
Self-Supervised Monocular Depth Estimation With Multimodal Fusion and Pose Enhancement
Chen, H., Wang, Y., Jia, S., Huang, S., & Zhang, J. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3684347
Ge, B., Yang, C., Xia, C., Cheng, M., & Sun, R. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02356-0
Free3R-GS: Endoscopic SfM-free rendering with reflection repair using 4D Gaussian splatting
Li, T., Zhou, Y., Xia, G., Dai, Y., Zhang, J. L., Wang, R., & Gu, H. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113980
Adversarial Patches in 3D: A Stealth Threat to Monocular Depth Estimation
Pan, M. R., Dai, Y., Lai, J., & Xie, X. (2026) · Machine Intelligence Research · doi:10.1007/s11633-025-1617-6
Wang, A., Liao, G., Zhao, Z., Li, R., Fang, Z., Ma, S., & Hwang, J. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3711298
Rolling Shutter Camera Self-calibration
Zhang, Y., Rabbani, N., Liao, B., Wang, C., Lao, Y., & Bartoli, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37550-6_6
Towards fully neuromorphic star tracking for spacecraft jitter mitigation
Subramaniam, A., Bui, L. C., Chin, T.-J., Robertson, W. S. P., & Latif, Y. (2026) · Acta Astronautica · doi:10.1016/j.actaastro.2026.08.001
Twin-Depth: Self-Supervised Monocular Depth Learning in Challenging Dynamic Scenes
Zhong, J., Yu, X. P., Huang, X., & Ji, R. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3722769
Nguyen, Q. H., & Albertella, A. (2026) · Geomatics · doi:10.3390/geomatics6050102
Vggt4d: Mining Motion Cues in Visual Geometry Transformers for 4D Scene Reconstruction
Hu, Y., Cheng, C., Yu, S., Guo, X., & Wang, H. (2026) · doi:10.1109/cvprf71909.2026.00044
ReloDepth: Reprojection Loss Filtering for Self-Supervised Depth Estimation in Dynamic Environments
Chen, S., Liu, H., Li, W., Zhu, Y., Wu, J., & Wang, G. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729629
JEPADepth: Self-Supervised Monocular Depth Estimation via Joint-Embedding Predictive Architectures
Grigore-Atimuț, I.-O., & Popa, C.-A. (2026) · Mathematics · doi:10.3390/math14213846
Yao, P., Xiang, L., Wang, Y., & Fan, B. (2026) · Intelligent Marine Technology and Systems · doi:10.1007/s44295-026-00120-8
Metadata
- 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 › Computer Science › Computer Vision and Pattern Recognition › 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
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 ›
Are you an author, or do you see a problem with this page? Request a review
