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Self-Supervised Monocular Depth Hints
Open accessAbstract
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser-scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground-truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. Here, we study the problem of ambiguous reprojections in depth-prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth-suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.
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“[27] uses a different split of the data, which is not the same as the Eigen split.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [27]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Stereo Processing by Semiglobal Matching and Mutual Information
Hirschmüller, H. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,199
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
Self-citationWhat Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Kendall, A., & Gal, Y. (2017) · arXiv · doi:10.48550/arxiv.1703.04977 · Cited by 3,088
Unsupervised Learning of Depth and Ego-Motion from Video
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.700 · Cited by 2,910
Loss Functions for Image Restoration With Neural Networks
Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2016) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2016.2644865 · Cited by 2,659
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- kuznietsov, 2017, Semisupervised deep learning for monocular depth map prediction, CVPR
- 10.1109/CVPR.2016.438 · DOI · Paper page
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Items 101–200 of 273
Bello, J. L. G., Moon, J.-H., & Kim, M. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3374045 · Cited by 18
SPIdepth: Strengthened Pose Information for Self-Supervised Monocular Depth Estimation
Лавренюк, М. С., & Лавренюк, А. М. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00091 · Cited by 25
Panoptic Segmentation Forecasting
Graber, C., Tsai, G., Firman, M., Brostow, G., & Schwing, A. G. (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/cvpr46437.2021.01233 · Cited by 15
Self-citationSPTR: Structure-Preserving Transformer for Unsupervised Indoor Depth Completion
Zhao, L., Zheng, W., Duan, Y., Zhou, J., & Lu, J. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3312664 · Cited by 15
Self-Supervised Adversarial Training of Monocular Depth Estimation Against Physical-World Attacks
Cheng, Z., Han, C., Liang, J., Wang, Q., Zhang, X., & Liu, D. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3412632 · Cited by 26
Monocular Depth Perception on Microcontrollers for Edge Applications
Peluso, V., Cipolletta, A., Calimera, A., Poggi, M., Tosi, F., Aleotti, F., & Mattoccia, S. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3077395 · Cited by 17
Monocular Depth Estimation: A Survey
Wang, D., Liu, Z., Shao, S., Wu, X., Chen, W., & Li, Z. G. (2023) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon51785.2023.10311687 · Cited by 16
SENSE: Self-Evolving Learning for Self-Supervised Monocular Depth Estimation
Li, G., Huang, R., Li, H., You, Z., & Chen, W. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3338053 · Cited by 15
SLAM Based on Camera-2D LiDAR Fusion
Lu, G. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611546 · Cited by 15
Self-Supervised Learning of Monocular Depth Estimation Based on Progressive Strategy
Wang, H., Sang, X., Chen, D., Wang, P., Yan, B., Qi, S., Ye, X., & Tong, Y. (2021) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2021.3069785 · Cited by 14
Energy-Quality Scalable Monocular Depth Estimation on Low-Power CPUs
Cipolletta, A., Peluso, V., Calimera, A., Poggi, M., Tosi, F., Aleotti, F., & Mattoccia, S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3080827 · Cited by 15
Self-supervised monocular depth estimation based on image texture detail enhancement
Li, Y., Luo, F., Li, W., Zheng, S., Wu, H., & Xiao, C. (2021) · The Visual Computer · doi:10.1007/s00371-021-02206-2 · Cited by 14
ES3Net: Accurate and Efficient Edge-based Self-Supervised Stereo Matching Network
Fang, I.-S., Wen, H.-C., Hsu, C.-L., Jen, P.-C., Chen, P.-Y., & Chen, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00470 · Cited by 15
Unsupervised Monocular Depth Perception: Focusing on Moving Objects
Jiang, H., Ding, L., Sun, Z., & Huang, R. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3109266 · Cited by 13
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving
Liu, Y., Xu, Z., Huang, H., Wang, L., & Liu, M. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3290348 · Cited by 16
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation
Baek, J., Kim, G., & Kim, S. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811802 · Cited by 16
Self-supervised coarse-to-fine monocular depth estimation using a lightweight attention module
Li, Y., Luo, F., & Xiao, C. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0279-3 · Cited by 13
Zheng, Q., Yu, T., & Wang, F. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.01.016 · Cited by 13
RAFT-MSF: Self-Supervised Monocular Scene Flow Using Recurrent Optimizer
Bayramli, B., Hur, J., & Lu, H. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01828-4 · Cited by 12
Resolution-sensitive self-supervised monocular absolute depth estimation
Zhou, Y., Zhang, C., Deng, L., Fu, J., Li, H., Xu, Z., & Zhang, J. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05414-0 · Cited by 13
The Second Monocular Depth Estimation Challenge
Spencer, J., Qian, C. S., Trescakova, M., Russell, C., Hadfield, S., Graf, E. W., Adams, W. J., Schofield, A. J., Elder, J., Bowden, R., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00308 · Cited by 13
P2D: a self-supervised method for depth estimation from polarimetry
Blanchon, M., Sidibé, D., Morel, O., Seulin, R., Braun, D., & Mériaudeau, F. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412441 · Cited by 12
URNet: An UNet-Based Model with Residual Mechanism for Monocular Depth Estimation
Duong, H., Chen, H., & Chang, C.-C. (2023) · Electronics · doi:10.3390/electronics12061450 · Cited by 11
HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decoder
Wang, F., & Cheng, J. S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3312721 · Cited by 11
Self-Supervised Monocular Depth Estimation via Binocular Geometric Correlation Learning
Peng, B., Sun, L., Lei, J., Liu, B., Shen, H., Li, W., & Huang, Q. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3663570 · Cited by 12
On the Synergies between Machine Learning and Stereo: a SurveyPreprint
Poggi, M., Tosi, F., Batsos, K., Mordohai, P., & Mattoccia, S. (2020) · arXiv (Cornell University) · Cited by 11
Park, H., & Park, S.-C. (2023) · Electronics · doi:10.3390/electronics12173730 · Cited by 10
High-Precision Self-supervised Monocular Depth Estimation with Rich-Resource Prior
Han, W., & Shen, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72751-1_9 · Cited by 11
Self-Supervised Monocular Depth Estimation From Videos via Adaptive Reconstruction Constraints
Ye, X., Ou, Y., Wu, B., Xu, R., & Li, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3492201 · Cited by 10
Semisupervised learning-based depth estimation with semantic inference guidance
Zhang, Y., Xiaopeng Fan, B., & Zhao, D. (2022) · Science China Technological Sciences · doi:10.1007/s11431-021-1948-3 · Cited by 10
AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation
Wu, Y., Liu, T. Y., Park, H., Soatto, S., Lao, D., & Wong, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73039-9_16 · Cited by 17
Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation
Fan, C., Yin, Z., Xu, F., Chai, A., & Zhang, F. (2021) · Sensors · doi:10.3390/s21216956 · Cited by 10
Learning Stereo from Single Images
Watson, J., Aodha, O. M., Turmukhambetov, D., Brostow, G., & Firman, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_42 · Cited by 16
Self-citationComputer vision-based solutions to overcome the limitations of wireless capsule endoscopy
Horovistiz, A. L., Oliveira, M., & Araújo, H. J. (2023) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2024.2302025 · Cited by 9
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches
Zhao, C., Li, Y., Wu, S., Tan, W., Zhou, S., & Pan, Q. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3472032 · Cited by 11
Joint prediction of monocular depth and structure using planar and parallax geometry
Xing, H., Cao, Y., Biber, M., Zhou, M., & Burschka, D. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108806 · Cited by 9
Self-Supervised Monocular Depth Estimation by Digging into Uncertainty Quantification
Li, Y.-Z., Zheng, S., Tan, Z.-X., Cao, T., Luo, F., & Xiao, C. (2023) · Journal of Computer Science and Technology · doi:10.1007/s11390-023-3088-y · Cited by 9
Self-Supervised Depth Estimation Via Implicit Cues from Videos
Wang, J., Zhang, G., Wu, Z., Li, X., & Liu, L. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413407 · Cited by 9
CbwLoss: Constrained Bidirectional Weighted Loss for Self-Supervised Learning of Depth and Pose
Wang, F., Cheng, J. S., & Liu, P. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3250744 · Cited by 9
Deep Learning based Monocular Depth Prediction: Datasets, Methods and ApplicationsPreprint
Li, Q., Zhu, J., Liu, J., Cao, R., Li, Q., Jia, S., & Qiu, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04123 · Cited by 12
Guesmi, A., Hanif, M. A., Alouani, I., Ouni, B., & Shafique, M. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802252 · Cited by 12
Swami, K., Muduli, A. K., Gurram, U., & Bajpai, P. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00112 · Cited by 8
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation
Zhu, J., Liu, L., Liu, Y., Li, W., Wen, F., & Zhang, H. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160534 · Cited by 8
Adaptive Semantic Fusion Framework for Unsupervised Monocular Depth Estimation
Li, R., Yu, H., Du, K., Xiao, Z., Yan, B., & Yuan, Z. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096716 · Cited by 7
Real-Time Single Image Depth Perception in the Wild with Handheld Devices
Aleotti, F., Zaccaroni, G., Bartolomei, L., Poggi, M., Tosi, F., & Mattoccia, S. (2020) · Sensors · doi:10.3390/s21010015 · Cited by 47
Panoptic Segmentation Forecasting
Graber, C., Tsai, G., Firman, M., Brostow, G., & Schwing, A. G. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00257 · Cited by 7
Self-citationSpatiotemporally Enhanced Photometric Loss for Self-Supervised Monocular Depth Estimation
Zhang, T., Zhu, D., Zhang, G., Shi, W., Liu, Y., Zhang, X., & Li, J. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981595 · Cited by 7
AGDF-Net: Learning Domain Generalizable Depth Features With Adaptive Guidance Fusion
Liu, L.-N., Song, X.-B., Wang, M.-M., Dai, Y., Liu, Y., & Zhang, L.-J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3342634 · Cited by 8
Sharma, M., Sharma, A., Tushar, K. R., & Panneer, A. (2020) · International Conference on 3D Immersion (IC3D) · doi:10.1109/ic3d51119.2020.9376327 · Cited by 7
Tan, Y., Ji, P., Zhang, Y., Ge, F., & Zhu, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109425 · Cited by 7
Multi-resolution distillation for self-supervised monocular depth estimation
Lee, S., Im, W., & Yoon, S. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.11.001 · Cited by 6
Self-Supervised Monocular Depth Estimation Based on High-Order Spatial Interactions
Wang, X., Yu, M., Wang, H., Lu, X., & Zhang, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3347585 · Cited by 6
Bridging local and global representations for self-supervised monocular depth estimation
Lin, M., Li, G., & Hao, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108277 · Cited by 6
Cong, R.-M., Wu, C., Song, X.-B., Zhang, W., Kwong, S., Li, H., & Ji, P. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3465034 · Cited by 10
Self-supervised monocular depth estimation with large kernel attention and dynamic scene perception
Xiang, X., Wang, Y., Li, X., Lei Zhang, A., & Zhen, X. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104413 · Cited by 6
Randomly connected neural networks for self-supervised monocular depth estimation
Tukra, S., & Giannarou, S. (2021) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2021.1997648 · Cited by 6
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images
Li, K., Zhang, J., Gao, J., & Qi, M. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00082 · Cited by 6
Depth Estimation Using Feature Pyramid U-Net and Polarized Self-Attention for Road Scenes
Tao, B., Shen, Y., Tong, X., Jiang, D., & Chen, B. (2022) · Photonics · doi:10.3390/photonics9070468 · Cited by 6
Xu, H., & Li, F. (2022) · SN Applied Sciences · doi:10.1007/s42452-022-05235-1 · Cited by 6
Self-supervised Multi-frame Monocular Depth Estimation with Pseudo-LiDAR Pose Enhancement
Wu, W., Wang, G., Zhong, J., Wang, H., & Liu, Z. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160391 · Cited by 6
BENCHMARKING THE EXTRACTION OF 3D GEOMETRY FROM UAV IMAGES WITH DEEP LEARNING METHODS
Nex, F., Zhang, N., Fabio Remondino, A., Farella, E. M., Qin, R., & Zhang, C. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-1-w3-2023-123-2023 · Cited by 6
Song, J., & Lee, S. J. (2023) · Scientific Reports · doi:10.1038/s41598-023-46178-w · Cited by 6
AggNet for Self-supervised Monocular Depth Estimation: Go An Aggressive Step Furthe
Chen, Z., Ye, X., Du, L., Yang, W., Huang, L., Tan, X., Shi, Z., Shen, F., & Ding, E. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475287 · Cited by 6
TRICKY 2024 Challenge on Monocular Depth from Images of Specular and Transparent Surfaces
Ramirez, P. Z., Costanzino, A., Tosi, F., Poggi, M., Stefano, L. D., Weibel, J.-B., Bauer, D., Antensteiner, D., Vincze, M., Li, J., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91569-7_16 · Cited by 5
Heightfields for Efficient Scene Reconstruction for AR
Watson, J., Vicente, S., Aodha, O. M., Godard, C., Brostow, G., & Firman, M. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00580 · Cited by 5
Self-citationChoudhary, R., Sharma, M., Uma, T. V., & Anil, R. (2023) · The Visual Computer · doi:10.1007/s00371-023-02912-z · Cited by 4
SAU-Net: Monocular Depth Estimation Combining Multi-Scale Features and Attention Mechanisms
Zhao, W., Song, Y., & Wang, T. (2023) · IEEE Access · doi:10.1109/access.2023.3339152 · Cited by 5
SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation
Pham, D.-H., Do, T., Nguyen-Ha, P., Hua, B., Nguyen, K., & Nguyen, R. M. H. (2025) · 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/cvpr52734.2025.01590 · Cited by 13
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth EstimationPreprint
Li, R., Mao, Q., Wang, P., He, X., Zhu, Y., Sun, J., & Zhang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.08048 · Cited by 9
Self-supervised monocular depth estimation with self-distillation and dense skip connection
Xiang, X., Li, W., Wang, Y., & Saddik, A. E. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104048 · Cited by 4
Xu, H., & Li, F. (2022) · Electronics · doi:10.3390/electronics11162615 · Cited by 4
LeanStereo: A Leaner Backbone based Stereo Network
Rahim, R., Woerz, S., & Zell, A. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191380 · Cited by 4
V2Depth: Monocular Depth Estimation via Feature-Level Virtual-View Simulation and Refinement
Wu, Z., Li, Z., Fan, Z.-G., Wu, Y., Pu, J., & Li, X. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611751 · Cited by 4
2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation
Sharma, M., Choudhary, R., & Anil, R. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00080 · Cited by 4
Suryanto, N., Adiputra, A. A., Kadiptya, A. Y., Kim, Y., & Kim, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-1624-4_23 · Cited by 4
The Fourth Monocular Depth Estimation Challenge
Obukhov, A., Poggi, M., Tosi, F., Arora, R. S., Spencer, J., Russell, C., Hadfield, S., Bowden, R., Wang, S., Ma, Z., et al. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00615 · Cited by 5
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Lee, S., Lee, C., Kim, H., & Kim, H. J. (2021) · International Journal of Control Automation and Systems · doi:10.1007/s12555-020-0654-8 · Cited by 4
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Liu, X., & Yan, W. Q. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26431-3_15 · Cited by 4
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Raghavendra, S., Kishore, R., Abhilash, S. K., Nookala, V. M., & Yadav, M. V. (2025) · IEEE Access · doi:10.1109/access.2025.3572107 · Cited by 3
Complete contextual information extraction for self-supervised monocular depth estimation
Zhou, D., Zhang, M., Gao, X., Zhang, Y., & Li, B. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104032 · Cited by 3
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Dana, A., Carmel, N., Shomer, A., Manela, O., & Peleg, T. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00447 · Cited by 3
Does depth estimation help object detection?
Çetinkaya, B., Kalkan, S., & Akbaş, E. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104427 · Cited by 3
Joint Self-Supervised Monocular Depth Estimation and SLAM
Xing, X., Cai, Y., Lu, T., Yang, Y., & Wen, D. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956576 · Cited by 3
A Hybrid Vision-Based Method of Encountered Wave Field Measurement for Navigating Surface Vehicles
Wei, H., Xiao, L., Tian, X., Zhang, B., & Lu, W. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3319028 · Cited by 3
Vision-guided three-dimensional range-gated imaging based on epistemic uncertainty estimation
Liu, X., Niu, Y., & Wang, X. (2023) · Optical Engineering · doi:10.1117/1.oe.62.12.123105 · Cited by 3
GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation
Fang, N., Qiu, L., Zhang, S., Wang, Z., Zhou, Z., & Hu, K. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3355738 · Cited by 3
Chen, G., Qin, H., & Huang, L. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02088-x · Cited by 3
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation
Han, K., Muhle, D., Wimbauer, F., & Cremers, D. (2024) · 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/cvpr52733.2024.00939 · Cited by 3
SDE-DualENet: A Novel Dual Efficient Convolutional Neural Network for Robust Stereo Depth Estimation
Anil, R., Sharma, M., & Choudhary, R. (2021) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip53242.2021.9675391 · Cited by 3
Qi, M., Remelli, E., Salzmann, M., & Fua, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11260 · Cited by 4
Revisit Self-supervised Depth Estimation with Local Structure-from-Motion
Zhu, S., & Liu, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73007-8_3 · Cited by 2
Boosting Depth Estimation for Self-Driving in a Self-Supervised Framework via Improved Pose Network
Dayoub, Y., Savchenko, A. V., & Makarov, I. (2024) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2024.3505876 · Cited by 2
Tan, H. P., Li, R., Zhang, J., Zhang, H., Qian, Y., Liu, J., & Gu, Z. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3646216 · Cited by 2
DMS: Diffusion-Based Multi-Baseline Stereo Generation for Improving Self-Supervised Depth Estimation
Liu, Z., Li, Y., Zhang, S., & Okutomi, M. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00580 · Cited by 2
Self-Supervised Monocular Depth Learning in Low-Texture Areas
Xu, W., Zou, L., Wu, L., & Fu, Z. (2021) · Remote Sensing · doi:10.3390/rs13091673 · Cited by 2
SeqDNet: Improving Missing Value by Sequential Depth Network
Hsu, H. W., Su, H.-T., Yeh, J.-F., Chung, C.-M., & Hsu, W. H. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897561 · Cited by 2
Choi, C., Oh, H. W., Han, J., & Shin, J. (2023) · IEEE Access · doi:10.1109/access.2023.3338649 · Cited by 2
REAL-NET: A Monochromatic Depth Estimation Using REgional Attention and Local Feature Mapping
Bhandari, H., & Palit, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0376-0_23 · Cited by 2
Apply Fuzzy Mask to Improve Monocular Depth Estimation
Chen, H., Chen, H.-C., Sun, C.-H., & Wang, W.-J. (2024) · International Journal of Fuzzy Systems · doi:10.1007/s40815-023-01657-0 · Cited by 2
Metadata
- Authors
- Watson, J., Firman, M., Brostow, G., & Turmukhambetov, D.
- Affiliations
- University College London (GB)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 2162-2171
- OpenAlex
- W2973531541
- Cited by
- 275
- References
- 46
- Citations by year
- 2026: 15 · 2025: 26 · 2024: 46 · 2023: 64 · 2022: 47 · 2021: 47 · 2020: 29 · 2019: 1
- FWCI
- 9.99
- 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)
- UCL Discovery (University College London) (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
- monocular depth estimation, self-supervised learning, depth discontinuities, thin structures, KITTI dataset
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