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Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of vision-based robots.
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
M. A. Fischler and R. C. Bolles · 1981
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Posenet: A convolutional network for real-time 6-dof camera relocalization
A. Kendall, M. Grimes, and R. Cipolla · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Earlier work this paper cites.
Multi-scale continuous crfs as sequential deep networks for monocular depth estimation
D. Xu, E. Ricci, W. Ouyang, X. Wang, and N. Sebe · 2017
Earlier work this paper cites.
Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks
S. Wang, R. Clark, H. Wen, and A. Trigoni · 2017
Earlier work this paper cites.
Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe · 2017
Earlier work this paper cites.
Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Semi-supervised deep learning for monocular depth map prediction
Y. Kuznietsov, J. Stuckler, and B. Leibe · 2017
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ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras
R. Mur-Artal and J. D. Tardós · 2017
Cited alongside, same era.
Structured attention guided convolutional neural fields for monocular depth estimation
D. Xu, W. Wang, H. Tang, H. Liu, N. Sebe, and E. Ricci · 2018
Cited alongside, same era.
Deep Ordinal Regression Network for Monocular Depth Estimation
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao · 2018
Cited alongside, same era.
Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
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Digging into self-supervised monocular depth estimation
C. Godard, O. Mac Aodha, M. Firman, and G. Brostow · 2019
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Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos
V. Casser, S. Pirk, R. Mahjourian, and A. Angelova · 2019
Later among the works it cites.
Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras
A. Gordon, H. Li, R. Jonschkowski, and A. Angelova · 2019
Later among the works it cites.
Beyond tracking: Selecting memory and refining poses for deep visual odometry
F. Xue, X. Wang, S. Li, Q. Wang, J. Wang, and H. Zha · 2019
Later among the works it cites.
Learning residual flow as dynamic motion from stereo videos
S. Lee, S. Im, S. Lin, and I. S. Kweon · 2019
Later among the works it cites.
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Look deeper into depth: Monocular depth estimation with semantic booster and attention-driven loss
J. Jiao, Y. Cao, Y. Song, and R. Lau · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
Cited alongside, same era.
Df-net: Unsupervised joint learning of depth and flow using cross-task consistency
Y. Zou, Z. Luo, and J.-B. Huang · 2018
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Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
H. Zhan, R. Garg, C. Saroj Weerasekera, K. Li, H. Agarwal, and I. Reid · 2018
Cited alongside, same era.
PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz · 2018
Cited alongside, same era.
Magicvo: An end-to-end hybrid cnn and bi-lstm method for monocular visual odometry
J. Jiao, J. Jiao, Y. Mo, W. Liu, and Z. Deng · 2019
Cited alongside, same era.
Unsupervised scale-consistent depth and ego-motion learning from monocular video
J.-W. Bian, Z. Li, N. Wang, H. Zhan, C. Shen, M.-M. Cheng, and I. Reid · 2019
Later among the works it cites.
Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
A. Ranjan, V. Jampani, L. Balles, K. Kim, D. Sun, J. Wulff, and M. J. Black · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, and etc · 2019
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Self-supervised deep visual odometry with online adaptation
S. Li, X. Wang, Y. Cao, F. Xue, Z. Yan, and H. Zha · 2020
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Towards better generalization: Joint depth-pose learning without posenet
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Visual odometry revisited: What should be learnt?
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