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There has been a recent surge of interest in learning to perceive depth from monocular videos in an unsupervised fashion.
H. Li, A. Gordon, H. Zhao, V. Casser, and A. Angelova, “Unsupervised Monocular Depth Learning in Dynamic Scenes,” in Proceedings of the 2020 Conference on Robot Learning . PMLR, 2021, pp. 1908–1917
1917
Earlier work this paper cites.
Z. Yin and J. Shi, “GeoNet: Unsupervised learning of dense depth, optical flow and camera pose,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 1983–1992
1992
Earlier work this paper cites.
R. I. Hartley and P. Sturm, “Triangulation,” Computer Vision and Image Understanding , vol. 68, no. 2, pp. 146–157, 1997
1997
Earlier work this paper cites.
Z. Wang et al. , “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
A. Saxena et al. , “Make3D: Learning 3d scene structure from a single still image,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 31, no. 5, pp. 824–840, 2008
2008
Earlier work this paper cites.
J. Deng et al. , “ImageNet: A large-scale hierarchical image database,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
H. Fu et al. , “Deep ordinal regression network for monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 2002–2011
2011
Earlier work this paper cites.
A. Geiger et al. , “Are we ready for autonomous driving? the KITTI vision benchmark suite,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2012, pp. 3354–3361
2012
Earlier work this paper cites.
A. Geiger et al. , “Vision meets robotics: The KITTI dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
D. Eigen et al. , “Depth map prediction from a single image using a multi-scale deep network,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 27, 2014
2014
Earlier work this paper cites.
F. Liu et al. , “Learning depth from single monocular images using deep convolutional neural fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38, no. 10, pp. 2024–2039, 2015
2015
Earlier work this paper cites.
M. Menze and A. Geiger, “Object scene flow for autonomous vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 3061–3070
2015
Earlier work this paper cites.
R. Garg et al. , “Unsupervised cnn for single view depth estimation: Geometry to the rescue,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2016, pp. 740–756
2016
Earlier work this paper cites.
K. He et al. , “Deep residual learning for image recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
T. Zhou et al. , “Unsupervised learning of depth and ego-motion from video,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 1851–1858
2017
Earlier work this paper cites.
C. Godard et al. , “Unsupervised monocular depth estimation with left-right consistency,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 270–279
2017
Earlier work this paper cites.
Y. Cao et al. , “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
Earlier work this paper cites.
J. Uhrig et al. , “Sparsity invariant CNNs,” in 2017 International Conference on 3D Vision (3DV) . IEEE, 2017, pp. 11–20
2017
Earlier work this paper cites.
Y. Zou et al. , “Df-net: Unsupervised joint learning of depth and flow using cross-task consistency,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 36–53
2018
Earlier work this paper cites.
C. Godard et al. , “Digging into self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 3828–3838
2019
Cited alongside, same era.
J. Hu et al. , “Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries,” in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE, 2019, pp. 1043–1051
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. Ranjan et al. , “Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 12 240–12 249
2019
Cited alongside, same era.
2021
Later among the works it cites.
J. Yan et al. , “Channel-wise attention-based network for self-supervised monocular depth estimation,” in 2021 International Conference on 3D vision (3DV) . IEEE, 2021, pp. 464–473
2021
Later among the works it cites.
C. Zhao et al. , “MonoViT: Self-supervised monocular depth estimation with a vision transformer,” in 2022 International Conference on 3D Vision (3DV) . IEEE, 2022, pp. 668–678
2022
Later among the works it cites.
Y. Zhang et al. , “Self-supervised monocular depth estimation with multiscale perception,” IEEE Transactions on Image Processing , vol. 31, pp. 3251–3266, 2022
2022
Later among the works it cites.
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X. Luo et al. , “Consistent video depth estimation,” ACM Transactions on Graphics , vol. 39, no. 4, pp. 71–1, 2020
2020
Cited alongside, same era.
Z. Teed and J. Deng, “RAFT: Recurrent all-pairs field transforms for optical flow,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 402–419
2020
Cited alongside, same era.
Y. Zhang et al. , “Unsupervised multi-view constrained convolutional network for accurate depth estimation,” IEEE Transactions on Image Processing , vol. 29, pp. 7019–7031, 2020
2020
Cited alongside, same era.
V. Guizilini et al. , “3D packing for self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 2485–2494
2020
Cited alongside, same era.
H. Caesar et al. , “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 11 621–11 631
2020
Cited alongside, same era.
2021
Cited alongside, same era.
X. Xu et al. , “Multi-scale spatial attention-guided monocular depth estimation with semantic enhancement,” IEEE Transactions on Image Processing , vol. 30, pp. 8811–8822, 2021
2021
Cited alongside, same era.
X. Ye et al. , “Unsupervised monocular depth estimation via recursive stereo distillation,” IEEE Transactions on Image Processing , vol. 30, pp. 4492–4504, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
L. Sun et al. , “SC-DepthV3: Robust self-supervised monocular depth estimation for dynamic scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 1, pp. 497–508, 2023
2023
Later among the works it cites.
N. Zhang et al. , “Lite-Mono: A lightweight CNN and Transformer architecture for self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 18 537–18 546
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Han et al. , “Self-supervised monocular depth estimation by direction-aware cumulative convolution network,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 8613–8623
2023
Later among the works it cites.
X. Chen et al. , “Self-supervised monocular depth estimation: Solving the edge-fattening problem,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2023, pp. 5776–5786
2023
Later among the works it cites.
Y. Sun and B. Hariharan, “Dynamo-Depth: Fixing unsupervised depth estimation for dynamical scenes,” Advances in Neural Information Processing Systems (NeurIPS) , 2023
2023
Later among the works it cites.
G. Li et al. , “SENSE: Self-evolving learning for self-supervised monocular depth estimation,” IEEE Transactions on Image Processing , 2023
2023
Later among the works it cites.
D. Shim and H. J. Kim, “Swindepth: Unsupervised depth estimation using monocular sequences via swin transformer and densely cascaded network,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 4983–4990
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
M. Oquab et al. , “DINOv2: Learning robust visual features without supervision,” Transactions on Machine Learning Research , 2024. [Online]. Available: https://openreview.net/forum?id=a68SUt6zFt
2024
Closest in time.
J. L. G. Bello et al. , “Self-supervised monocular depth estimation with positional shift depth variance and adaptive disparity quantization,” IEEE Transactions on Image Processing , 2024
2024
Closest in time.
S. Shao et al. , “MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model,” IEEE Transactions on Circuits and Systems for Video Technology , pp. 1–1, 2024
2024
Closest in time.