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Recent work has shown that CNN-based depth and ego-motion estimators can be learned using unlabelled monocular videos.
Bundle adjustment—a modern synthesis
Bill Triggs, Philip F McLauchlan, Richard I Hartley, and Andrew W Fitzgibbon · 1999
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Multiple view geometry in computer vision
Richard Hartley and Andrew Zisserman · 2003
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Lucas-kanade 20 years on: A unifying framework
Simon Baker and Iain Matthews · 2004
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Image Quality Assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, Eero P Simoncelli, et al · 2004
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Learning depth from single monocular images
Ashutosh Saxena, Sung H Chung, and Andrew Y Ng · 2006
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Vision meets Robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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ADAM: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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ORB-SLAM: a versatile and accurate monocular slam system
Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
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Learning depth from single monocular images using deep convolutional neural fields
Fayao Liu, Chunhua Shen, Guosheng Lin, and Ian Reid · 2016
Cited alongside, same era.
Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar BG, Gustavo Carneiro, and Ian Reid · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Cited alongside, same era.
GMS: Grid-based motion statistics for fast, ultra-robust feature correspondence
Jia-Wang Bian, Wen-Yan Lin, Yasuyuki Matsushita, Sai-Kit Yeung, Tan-Dat Nguyen, and Ming-Ming Cheng · 2017
Cited alongside, same era.
Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
Cited alongside, same era.
Look deeper into depth: Monocular depth estimation with semantic booster and attention-driven loss
Jianbo Jiao, Ying Cao, Yibing Song, and Rynson Lau · 2018
Later among the works it cites.
Learning depth from monocular videos using direct methods
Chaoyang Wang, José Miguel Buenaposada, Rui Zhu, and Simon Lucey · 2018
Later among the works it cites.
Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera, Kejie Li, Harsh Agarwal, and Ian Reid · 2018
Later among the works it cites.
Self-supervised learning for dense depth estimation in monocular endoscopy
Xingtong Liu, Ayushi Sinha, Mathias Unberath, Masaru Ishii, Gregory D Hager, Russell H Taylor, and Austin Reiter · 2018
Later among the works it cites.
Unsupervised learning of geometry with edge-aware depth-normal consistency
Zhenheng Yang, Peng Wang, Wei Xu, Liang Zhao, and Ramakant Nevatia · 2018
Later among the works it cites.
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Yevhen Kuznietsov, Jorg Stuckler, and Bastian Leibe · 2017
Cited alongside, same era.
Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
Reza Mahjourian, Martin Wicke, and Anelia Angelova · 2018
Cited alongside, same era.
GeoNet: Unsupervised learning of dense depth, optical flow and camera pose
Zhichao Yin and Jianping Shi · 2018
Cited alongside, same era.
DF-Net: Unsupervised joint learning of depth and flow using cross-task consistency
Yuliang Zou, Zelun Luo, and Jia-Bin Huang · 2018
Cited alongside, same era.
An evaluation of feature matchers for fundamental matrix estimation
Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, and Ian Reid · 2019
Closest in time.
Competitive Collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
Anurag Ranjan, Varun Jampani, Kihwan Kim, Deqing Sun, Jonas Wulff, and Michael J Black · 2019
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Joint unsupervised learning of optical flow and depth by watching stereo videos
Yang Wang, Zhenheng Yang, Peng Wang, Yi Yang, Chenxu Luo, and Wei Xu · 2019
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BA-Net: Dense bundle adjustment network
Chengzhou Tang and Ping Tan · 2019
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Hierarchical discrete distribution decomposition for match density estimation
Zhichao Yin, Trevor Darrell, and Fisher Yu · 2019
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Enforcing geometric constraints of virtual normal for depth prediction
Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan · 2019
Closest in time.