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Estimating depth from a single image is a challenging visual task.
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2008
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2011
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2014
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2015
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F. Liu, C. Shen, and G. Lin, “Deep convolutional neural fields for depth estimation from a single image,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2015, pp. 5162–5170
2015
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2015
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2015
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D. Zoran, P. Isola, D. Krishnan, and W. T. Freeman, “Learning ordinal relationships for mid-level vision,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2015, pp. 388–396
2015
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S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 567–576
2015
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I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, “Deeper depth prediction with fully convolutional residual networks,” in 2016 Fourth international conference on 3D vision (3DV) . IEEE, 2016, pp. 239–248
2016
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2016
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J. Li, R. Klein, and A. Yao, “A two-streamed network for estimating fine-scaled depth maps from single rgb images,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 3372–3380
2017
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Y. Cao, Z. Wu, and C. Shen, “Estimating depth from monocular images as classification using deep fully convolutional residual networks,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 28, no. 11, pp. 3174–3182, 2017
2017
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J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox, and A. Geiger, “Sparsity invariant cnns,” in International Conference on 3D Vision (3DV) , 2017
2017
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J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 7132–7141
2018
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Z. Zhang, C. Xu, J. Yang, J. Gao, and Z. Cui, “Progressive hard-mining network for monocular depth estimation,” IEEE Transactions on Image Processing , vol. 27, no. 8, pp. 3691–3702, 2018
2018
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X. Qi, R. Liao, Z. Liu, R. Urtasun, and J. Jia, “Geonet: Geometric neural network for joint depth and surface normal estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 283–291
2018
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D. Xu, W. Ouyang, X. Wang, and N. Sebe, “Pad-net: Multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 675–684
2018
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T. Koch, L. Liebel, F. Fraundorfer, and M. Korner, “Evaluation of cnn-based single-image depth estimation methods,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops , 2018, pp. 0–0
2018
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C. Wang, S. Lucey, F. Perazzi, and O. Wang, “Web stereo video supervision for depth prediction from dynamic scenes,” in 2019 International Conference on 3D Vision (3DV) . IEEE, 2019, pp. 348–357
2019
Cited alongside, same era.
2019
Cited alongside, same era.
W. Yin, Y. Liu, C. Shen, and Y. Yan, “Enforcing geometric constraints of virtual normal for depth prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5684–5693
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 3, 2022
2022
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S. F. Bhat, I. Alhashim, and P. Wonka, “Localbins: Improving depth estimation by learning local distributions,” in European Conference on Computer Vision . Springer, 2022, pp. 480–496
2022
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W. Yuan, X. Gu, Z. Dai, S. Zhu, and P. Tan, “Neural window fully-connected crfs for monocular depth estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 3916–3925
2022
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J. Jun, J.-H. Lee, C. Lee, and C.-S. Kim, “Depth map decomposition for monocular depth estimation,” in European Conference on Computer Vision . Springer, 2022, pp. 18–34
2022
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I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
X. Yang, L. Zhou, H. Jiang, Z. Tang, Y. Wang, H. Bao, and G. Zhang, “Mobile3DRecon: Real-time monocular 3D reconstruction on a mobile phone,” IEEE Transactions on Visualization and Computer Graphics , vol. 26, no. 12, pp. 3446–3456, 2020
2020
Cited alongside, same era.
K. Xian, J. Zhang, O. Wang, L. Mai, Z. Lin, and Z. Cao, “Structure-guided ranking loss for single image depth prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 611–620
2020
Cited alongside, same era.
Z. Yu, L. Jin, and S. Gao, “P 2 net: Patch-match and plane-regularization for unsupervised indoor depth estimation,” in European Conference on Computer Vision . Springer, 2020, pp. 206–222
2020
Cited alongside, same era.
Y. Cabon, N. Murray, and M. Humenberger, “Virtual kitti 2,” arXiv preprint arXiv:2001.10773 , 2020
2020
Cited alongside, same era.
V. Guizilini, R. Ambrus, S. Pillai, A. Raventos, and A. Gaidon, “3d packing for self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2485–2494
2020
Cited alongside, same era.
M. Schön, M. Buchholz, and K. Dietmayer, “Mgnet: Monocular geometric scene understanding for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 804–15 815
2021
Cited alongside, same era.
M. K. Yucel, V. Dimaridou, A. Drosou, and A. Saa-Garriga, “Real-time monocular depth estimation with sparse supervision on mobile,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2428–2437
2021
Cited alongside, same era.
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 1290–1299
2022
Later among the works it cites.
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
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C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman et al. , “Laion-5b: An open large-scale dataset for training next generation image-text models,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 278–25 294, 2022
2022
Later among the works it cites.
L. Piccinelli, C. Sakaridis, and F. Yu, “idisc: Internal discretization for monocular depth estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
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J. Ning, C. Li, Z. Zhang, C. Wang, Z. Geng, Q. Dai, K. He, and H. Hu, “All in tokens: Unifying output space of visual tasks via soft token,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 19 900–19 910
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Shao, Z. Pei, W. Chen, X. Wu, and Z. Li, “Nddepth: Normal-distance assisted monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 7931–7940
2023
Later among the works it cites.
S. Shao, Z. Pei, X. Wu, Z. Liu, W. Chen, and Z. Li, “Iebins: Iterative elastic bins for monocular depth estimation,” in Advances in Neural Information Processing Systems (NeurIPS) , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
V. Guizilini, I. Vasiljevic, D. Chen, R. Ambruș, and A. Gaidon, “Towards zero-shot scale-aware monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9233–9243
2023
Later among the works it cites.
W. Yin, C. Zhang, H. Chen, Z. Cai, G. Yu, K. Wang, X. Chen, and C. Shen, “Metric3d: Towards zero-shot metric 3d prediction from a single image,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9043–9053
2023
Later among the works it cites.
Y. Ji, Z. Chen, E. Xie, L. Hong, X. Liu, Z. Liu, T. Lu, Z. Li, and P. Luo, “Ddp: Diffusion model for dense visual prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 21 741–21 752
2023
Later among the works it cites.
L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, “Depth anything: Unleashing the power of large-scale unlabeled data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 10 371–10 381
2024
Closest in time.
B. Ke, A. Obukhov, S. Huang, N. Metzger, R. C. Daudt, and K. Schindler, “Repurposing diffusion-based image generators for monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 9492–9502
2024
Closest in time.
Z. Li, X. Wang, X. Liu, and J. Jiang, “Binsformer: Revisiting adaptive bins for monocular depth estimation,” IEEE Transactions on Image Processing , vol. 33, pp. 3964–3976, 2024
2024
Closest in time.
R. Zhu, Z. Song, L. Liu, J. He, T. Zhang, and Y. Zhang, “Ha-bins: Hierarchical adaptive bins for robust monocular depth estimation across multiple datasets,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 6, pp. 4354–4366, 2024
2024
Closest in time.
Z. Li and N. Snavely, “Megadepth: Learning single-view depth prediction from internet photos,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2041–2050
2050
Closest in time.