The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index.
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
Open accessOverview
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
“Garg et al. [90] have recently shown how a deep network for single-view depth estimation can be trained simply by observing a large corpus of stereo pairs, without the need to observe or calculate depth explicitly.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [90]
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
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
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,284
International Conference on Learning Representations (ICLR 2013)
松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286
Learning Deconvolution Network for Semantic Segmentation
Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422
Research works printed but not linked: 3
- Handa, A., Patraucean, V., Badrinarayanan, V., Stent, S., Cipolla, R.: Scenenet: understanding real world indoor scenes with synthetic data. arXiv preprint (2015). arXiv:1511.07041 · arXiv
- Horn, B.K., Schunck, B.G.: Determining optical flow. In: 1981 technical symposium east, pp. 319–331. International Society for Optics and Photonics (1981)
- Xie, J., Girshick, R., Farhadi, A.: Deep. 3d: fully automatic 2d-to-3d video conversion with deep convolutional neural networks. arXiv preprint (2016). arXiv:1604.03650 · arXiv
Cited by
Sorted by citations
Items 101–200 of 1,000
Semantically-Guided Representation Learning for Self-Supervised Monocular DepthPreprint
Guizilini, V., Hou, R., Li, J., Ambruş, R., & Gaidon, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12319 · Cited by 215
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation
Kundu, J. N., Uppala, P. K., Pahuja, A., & Babu, R. V. (2018) · 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/cvpr.2018.00281 · Cited by 185
Towards Real-Time Unsupervised Monocular Depth Estimation on CPU
Poggi, M., Aleotti, F., Tosi, F., & Mattoccia, S. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593814 · Cited by 188
DeepFactors: Real-Time Probabilistic Dense Monocular SLAM
Czarnowski, J., Laidlow, T., Clark, R., & Davison, A. J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2965415 · Cited by 188
Towards Better Generalization: Joint Depth-Pose Learning Without PoseNet
Zhao, W., Liu, S., Shu, Y., & Liu, Y. (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/cvpr42600.2020.00917 · Cited by 186
Learning Monocular Depth Estimation with Unsupervised Trinocular Assumptions
Poggi, M., Tosi, F., & Mattoccia, S. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00045 · Cited by 179
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
Wang, Y., Wang, P., Yang, Z., Luo, C., Yang, Y., & Xu, W. (2019) · 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/cvpr.2019.00826 · Cited by 180
Learning Joint 2D-3D Representations for Depth Completion
Chen, Y., Yang, B., Liang, M., & Urtasun, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01012 · Cited by 179
R-MSFM: Recurrent Multi-Scale Feature Modulation for Monocular Depth Estimating
Zhou, Z., Fan, X., Shi, P., & Xin, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01254 · Cited by 188
Dense Depth Estimation in Monocular Endoscopy With Self-Supervised Learning Methods
Liu, X., Sinha, A., Ishii, M., Hager, G. D., Reiter, A., Taylor, R. H., & Unberath, M. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2950936 · Cited by 174
Synthetic depth-of-field with a single-camera mobile phone
Wadhwa, N., Garg, R. D., Jacobs, D. E., Feldman, B. E., Kanazawa, N., Carroll, R., Movshovitz-Attias, Y., Barron, J. T., Pritch, Y., & Levoy, M. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201329 · Cited by 174
Self-citationTowards Real-Time Monocular Depth Estimation for Robotics: A Survey
Dong, X., Garratt, M., Anavatti, S. G., & Abbass, H. A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3160741 · Cited by 187
Monocular Depth Estimation Using Deep Learning: A Review
Masoumian, A., Rashwan, H. A., Cristiano, J., Asif, M. S., & Puig, D. (2022) · Sensors · doi:10.3390/s22145353 · Cited by 180
Deep learning-based robust positioning for all-weather autonomous driving
Almalıoğlu, Y., Turan, M., Trigoni, N., & Markham, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00520-5 · Cited by 170
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
Patil, V., Sakaridis, C., Liniger, A., & Gool, L. V. (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/cvpr52688.2022.00166 · Cited by 177
Unsupervised Learning of Geometry From Videos With Edge-Aware Depth-Normal Consistency
Yang, Z., Wang, P., Xu, W. H., Zhao, L., & Nevatia, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12257 · Cited by 156
InverseRenderNet: Learning Single Image Inverse Rendering
Yu, Y., & Smith, W. A. P. (2019) · 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/cvpr.2019.00327 · Cited by 156
Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask
Ponti, M. A., Ribeiro, L. S. F., de Nazaré, T. S., Bui, T., & Collomosse, J. P. (2017) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi-t.2017.12 · Cited by 160
Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review
Khan, F., Salahuddin, S., & Javidnia, H. (2020) · Sensors · doi:10.3390/s20082272 · Cited by 170
T$$^2$$Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks
Zheng, C., Cham, T., & Cai, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_47 · Cited by 154
Pilzer, A., Lathuilière, S., Sebe, N., & Ricci, E. (2019) · 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/cvpr.2019.01000 · Cited by 155
PhaseCam3D — Learning Phase Masks for Passive Single View Depth Estimation
Wu, Y., Boominathan, V., Chen, H., Sankaranarayanan, A. C., & Veeraraghavan, A. (2019) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccphot.2019.8747330 · Cited by 151
Almalıoğlu, Y., Saputra, M. R. U., de Gusmao, P. P. B., Markham, A., & Trigoni, N. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793512 · Cited by 166
Layer-Structured 3D Scene Inference via View Synthesis
Tulsiani, S., Tucker, R. P., & Snavely, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_19 · Cited by 152
Don’t Forget The Past: Recurrent Depth Estimation from Monocular Video
Patil, V., Gansbeke, W. V., Dai, D., & Gool, L. V. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3017478 · Cited by 162
A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data
Cebollada, S., Payá, L., Flores, M., Peidró, A., & Reinoso, Ó. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114195 · Cited by 170
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey
Tang, Y. S., Zhao, C., Wang, J., Zhang, C., Sun, Q., Zheng, W. X., Du, W., Qian, F., & Kurths, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3167688 · Cited by 170
Supervising the New with the Old: Learning SFM from SFM
Klodt, M., & Vedaldi, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_43 · Cited by 147
Single Image Depth Estimation Trained via Depth From Defocus Cues
Gur, S., & Wolf, L. (2019) · 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/cvpr.2019.00787 · Cited by 148
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
Qiao, S., Zhu, Y., Adam, H., Yuille, A., & Chen, L.-C. (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/cvpr46437.2021.00399 · Cited by 156
MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis
Li, J., Feng, Z., She, Q., Ding, H., Wang, C., & Lee, G. H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01235 · Cited by 158
Poggi, M., Tosi, F., Batsos, K., Mordohai, P., & Mattoccia, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3070917 · Cited by 168
Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference
Li, B., Dai, Y., & He, M. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.029 · Cited by 142
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T. W., Shen, C., & Reid, I. D. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794220 · Cited by 149
Self-citationConcrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning
McAllister, R., Gal, Y., Kendall, A., van der Wilk, M., Shah, A., Cipolla, R., & Weller, A. (2017) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.12760 · Cited by 144
Height estimation from single aerial images using a deep convolutional encoder-decoder network
Amirkolaee, H. A., & Arefi, H. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.01.013 · Cited by 140
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation
Wang, L., Zhang, J., Wang, O., Lin, Z., & Lu, H. (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/cvpr42600.2020.00062 · Cited by 141
Fang, H., Fang, H.-S., Xu, S., & Lu, C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3183256 · Cited by 139
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Wang, Q., Dai, D., Hoyer, L., Gool, L. V., & Fink, O. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00840 · Cited by 149
Unsupervised Monocular Depth and Ego-Motion Learning With Structure and Semantics
Casser, V., Pirk, S., Mahjourian, R., & Angelova, A. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00051 · Cited by 137
DeepSLAM: A Robust Monocular SLAM System With Unsupervised Deep Learning
Li, R., Wang, S., & Gu, D. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2982096 · Cited by 135
The Edge of Depth: Explicit Constraints Between Segmentation and Depth
Zhu, S., Brazil, G., & Liu, X. (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/cvpr42600.2020.01313 · Cited by 139
Self-Supervised Learning for Stereo Matching with Self-Improving AbilityPreprint
Zhong, Y., Dai, Y., & Li, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00930 · Cited by 134
Tung, H.-Y. F., Harley, A. W., Seto, W., & Fragkiadaki, K. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.467 · Cited by 141
Evaluation of CNN-Based Single-Image Depth Estimation Methods
Koch, T., Liebel, L., Fraundorfer, F., & Körner, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11015-4_25 · Cited by 132
Dense Depth Posterior (DDP) From Single Image and Sparse Range
Yang, Y., Wong, A., & Soatto, S. (2019) · 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/cvpr.2019.00347 · Cited by 131
iDisc: Internal Discretization for Monocular Depth Estimation
Piccinelli, L., Sakaridis, C., & Yu, F. (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/cvpr52729.2023.02057 · Cited by 144
Depth Estimation From a Single Image Using Deep Learned Phase Coded Mask
Haim, H., Elmalem, S., Giryes, R., Bronstein, A. M., & Marom, E. (2018) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2018.2849326 · Cited by 129
Hanocka, R., Fish, N., Wang, Z., Giryes, R., Fleishman, S., & Cohen-Or, D. (2018) · ACM Transactions on Graphics · doi:10.1145/3267347 · Cited by 77
Robust Learning Through Cross-Task Consistency
Zamir, A., (Sasha), A. S., Cheerla, N., Suri, R., Cao, Z., Malik, J., & Guibas, L. 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/cvpr42600.2020.01121 · Cited by 131
Deep Monocular Depth Estimation via Integration of Global and Local Predictions
Kim, Y., Jung, H., Min, D., & Sohn, K. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2836318 · Cited by 127
DIODE: A Dense Indoor and Outdoor DEpth DatasetPreprint
Vasiljevic, I., Kolkin, N., Zhang, S., Luo, R., Wang, H., Dai, F. Z., Daniele, A. F., Mostajabi, M., Basart, S., Walter, M. R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.00463 · Cited by 300
UniFuse: Unidirectional Fusion for 360° Panorama Depth Estimation
Jiang, H., Sheng, Z., Zhu, S., Dong, Z., & Huang, R. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3058957 · Cited by 133
Learning Single Camera Depth Estimation Using Dual-Pixels
Garg, R. D., Wadhwa, N., Ansari, S., & Barron, J. T. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00772 · Cited by 131
Self-citationSelf-supervised Learning of Motion CapturePreprint
Tung, H.-Y. F., Tung, H.-W., Yumer, E., & Fragkiadaki, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.01337 · Cited by 262
Geometry Meets Semantics for Semi-supervised Monocular Depth Estimation
Ramirez, P. Z., Poggi, M., Tosi, F., Mattoccia, S., & Stefano, L. D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_19 · Cited by 124
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement
Gan, Y., Xu, X., Sun, W., & Lin, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_14 · Cited by 122
Single image depth estimation: An overview
Mertan, A., Duff, D. J., & Unal, G. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103441 · Cited by 121
Han, L., Lin, Y., Du, G., & Lian, S. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968467 · Cited by 125
Generative Adversarial Networks for Unsupervised Monocular Depth Prediction
Aleotti, F., Tosi, F., Poggi, M., & Mattoccia, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_20 · Cited by 117
ActiveStereoNet: End-to-End Self-supervised Learning for Active Stereo Systems
Zhang, Y., Khamis, S., Rhemann, C., Valentin, J., Kowdle, A., Tankovich, V., Schoenberg, M., Izadi, S., Funkhouser, T. A., & Fanello, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_48 · Cited by 119
Estimating Depth from RGB and Sparse Sensing
Chen, Z., Badrinarayanan, V., Drozdov, G., & Rabinovich, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_11 · Cited by 117
SelfVIO: Self-supervised deep monocular Visual–Inertial Odometry and depth estimation
Almalıoğlu, Y., Turan, M., Saputra, M. R. U., de Gusmão, P. P. B., Markham, A., & Trigoni, N. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.005 · Cited by 127
Monocular Depth Estimation: A Thorough Review
Arampatzakis, V., Pavlidis, G. P., Mitianoudis, N., & Papamarkos, N. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3330944 · Cited by 135
Wang, K., Ma, S., Chen, J., Ren, F., & Lu, J. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3038898 · Cited by 114
“The method using unsupervised learning was first introduced in [45], which estimates the depth information from single-view images.”
MethodEvery Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding
Yang, Z., Wang, P., Wang, Y., Xu, W., & Nevatia, R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_43 · Cited by 109
DeepSFM: Structure from Motion via Deep Bundle Adjustment
Wei, X., Zhang, Y., Li, Z., Fu, Y., & Xue, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_14 · Cited by 112
Chen, C., Wang, B., Lu, C. X., Trigoni, N., & Markham, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12567 · Cited by 145
Welling, M., Sebe, N., Matas, J., & Leibe, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8 · Cited by 114
Depth from motion for smartphone AR
Valentin, J., Kowdle, A., Barron, J. T., Wadhwa, N., Dzitsiuk, M., Schoenberg, M., Verma, V., Csaszar, A., Turner, E., Dryanovski, I., et al. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275041 · Cited by 113
Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep Networks
Xu, D., Ricci, E., Ouyang, W., Wang, X., & Sebe, N. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2839602 · Cited by 104
Augmented reality navigation for liver resection with a stereoscopic laparoscope
Luo, H., Yin, D., Zhang, S., Xiao, D., He, B., Meng, F., Zhang, Y., Cai, W., He, S., Zhang, W., et al. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105099 · Cited by 108
Multi-Frame Self-Supervised Depth with Transformers
Guizilini, V., Ambruş, R., Chen, D., Zakharov, S., & Gaidon, A. (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/cvpr52688.2022.00026 · Cited by 108
Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning
Milz, S., Arbeiter, G., Witt, C., Abdallah, B., & Yogamani, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00062 · Cited by 107
Attention-based context aggregation network for monocular depth estimation
Chen, Y., Zhao, H., Hu, Z., & Peng, J. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01251-y · Cited by 105
Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation
Peng, R., Wang, R., Lai, Y., Tang, L., & Cai, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01527 · Cited by 115
SC-DepthV3: Robust Self-Supervised Monocular Depth Estimation for Dynamic Scenes
Sun, L., Bian, J.-W., Zhan, H., Yin, W., Reid, I. D., & Shen, C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3322549 · Cited by 119
Self-citationDepthNet: A Recurrent Neural Network Architecture for Monocular Depth Prediction
Kumar, A. C. S., Bhandarkar, S. M., & Prasad, M. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00066 · Cited by 112
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras
Mitrokhin, A., Ye, C., Fermuller, C. M., Aloimonos, Y., & Delbrück, T. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968520 · Cited by 108
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes
Wang, C., Lucey, S., Perazzi, F., & Wang, O. (2019) · 2019 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00046 · Cited by 103
Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks
Hao, Z., Li, Y., You, S., & Lu, F. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00043 · Cited by 105
Light Field Image Compression Using Generative Adversarial Network-Based View Synthesis
Jia, C., Zhang, X., Wang, S., Wang, S., & Ma, S. (2018) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2018.2886642 · Cited by 104
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency
Lee, S., Im, S., Lin, S. C.-F., & Kweon, I. S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16281 · Cited by 11
Dist-YOLO: Fast Object Detection with Distance Estimation
Vajgl, M., Hurtík, P., & Nejezchleba, T. (2022) · Applied Sciences · doi:10.3390/app12031354 · Cited by 104
Mask-SLAM: Robust Feature-Based Monocular SLAM by Masking Using Semantic Segmentation
Kaneko, M., Iwami, K., Ogawa, T., Yamasaki, T., & Aizawa, K. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00063 · Cited by 107
Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance
Liu, Z., Luo, W., Wu, B., Yang, X., Liu, W., & Cheng, K.-T. T. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01227-8 · Cited by 102
Fully neuromorphic vision and control for autonomous drone flight
Paredes-Vallés, F., Hagenaars, J. J., Dupeyroux, J., Stroobants, S., Xu, Y., & de Croon, G. (2024) · Science Robotics · doi:10.1126/scirobotics.adi0591 · Cited by 123
Smolyanskiy, N., Kamenev, A., & Birchfield, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00147 · Cited by 104
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
Spencer, J., Bowden, R., & Hadfield, S. (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/cvpr42600.2020.01441 · Cited by 110
gvnn: Neural Network Library for Geometric Computer Vision
Handa, A., Bloesch, M., Pătrăucean, V., Stent, S., McCormac, J., & Davison, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_9 · Cited by 99
Recovering 3D Planes from a Single Image via Convolutional Neural Networks
Yang, F., & Zhou, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_6 · Cited by 99
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Hoyer, L., Dai, D., Chen, Y., Köring, A., Saha, S., & Gool, L. V. (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/cvpr46437.2021.01098 · Cited by 101
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
Zhu, L., Mousavian, A., Yu, X., Mazhar, H., van Eenbergen, J., Debnath, S., & Fox, 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/cvpr46437.2021.00462 · Cited by 99
Towards Zero-Shot Scale-Aware Monocular Depth Estimation
Guizilini, V., Vasiljevic, I., Chen, D., Ambruş, R., & Gaidon, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00847 · Cited by 100
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
Wong, A., & Soatto, S. (2019) · 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/cvpr.2019.00579 · Cited by 97
Spherical View Synthesis for Self-Supervised 360° Depth Estimation
Zioulis, N., Karakottas, A., Zarpalas, D., Álvarez, F., & Daras, P. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00081 · Cited by 97
Deep Learning for Visual Localization and Mapping: A Survey
Chen, C., Wang, B., Lu, C. X., Trigoni, N., & Markham, A. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3309809 · Cited by 97
Unsupervised Monocular Depth Learning in Dynamic ScenesPreprint
Li, H., Gordon, A. M., Zhao, H., Casser, V., & Angelova, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.16404 · Cited by 111
Unsupervised Learning of Geometry with Edge-aware Depth-Normal ConsistencyPreprint
Yang, Z., Wang, P., Xu, W. H., Zhao, L., & Nevatia, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.03665 · Cited by 104
Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
Shen, T., Luo, Z., Zhou, L., Deng, H., Zhang, R., Fang, T., & Quan, L. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793479 · Cited by 91
Metadata
- Authors
- Garg, R., B.G., V. K., Carneiro, G., & Reid, I.
- Affiliations
- The University of Adelaide (AU)
- Corresponding author
- Garg, Ravi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 740-756
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2300779272
- Cited by
- 1,632
- References
- 40
- Citations by year
- 2026: 48 · 2025: 107 · 2024: 164 · 2023: 173 · 2022: 207 · 2021: 253 · 2020: 227 · 2019: 236 · 2018: 153 · 2017: 57 · 2016: 7
- FWCI
- 52.49
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (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 › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- single-view depth estimation, unsupervised learning, convolutional neural networks, autoencoder, stereoscopic images, camera movement, KITTI dataset
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
