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Self-supervised monocular depth and ego-motion estimation is a promising approach to replace or supplement expensive depth sensors such as LiDAR for robotics applications like autonomous driving.
Advances in computational stereo
Myron Z Brown, Darius Burschka, and Gregory D Hager · 2003
Earlier work this paper cites.
Multiple view geometry in computer vision
Richard Hartley and Andrew Zisserman · 2003
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Depth estimation using monocular and stereo cues
Ashutosh Saxena, Jamie Schulte, Andrew Y Ng, et al · 2007
Earlier work this paper cites.
Make3d: Learning 3d scene structure from a single still image
Ashutosh Saxena, Min Sun, and Andrew Y Ng · 2008
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
Earlier work this paper cites.
Posenet: A convolutional network for real-time 6-dof camera relocalization
Alex Kendall, Matthew Grimes, and Roberto Cipolla · 2015
Earlier work this paper cites.
Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar Bg, Gustavo Carneiro, and Ian Reid · 2016
Earlier work this paper cites.
Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
Earlier work this paper cites.
Stereo matching by training a convolutional neural network to compare image patches
Jure Zbontar, Yann LeCun, et al · 2016
Earlier work this paper cites.
Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
Earlier work this paper cites.
Learning a multi-view stereo machine
Abhishek Kar, Christian Häne, and Jitendra Malik · 2017
Earlier work this paper cites.
Geometric loss functions for camera pose regression with deep learning
Alex Kendall and Roberto Cipolla · 2017
Earlier work this paper cites.
End-to-end learning of geometry and context for deep stereo regression
Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry · 2017
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Semi-supervised deep learning for monocular depth map prediction
Yevhen Kuznietsov, Jorg Stuckler, and Bastian Leibe · 2017
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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
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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
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Deepmvs: Learning multi-view stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang · 2018
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Supervising the new with the old: learning sfm from sfm
Maria Klodt and Andrea Vedaldi · 2018
Learning unsupervised multi-view stereopsis via robust photometric consistency
Tejas Khot, Shubham Agrawal, Shubham Tulsiani, Christoph Mertz, Simon Lucey, and Martial Hebert · 2019
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Superdepth: Self-supervised, super-resolved monocular depth estimation
Sudeep Pillai, Rares Ambrus, and Adrien Gaidon · 2019
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Unsupervised learning of depth and ego-motion from cylindrical panoramic video
Alisha Sharma and Jonathan Ventura · 2019
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Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Q Weinberger · 2019
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Unos: Unified unsupervised optical-flow and stereo-depth estimation by watching videos
Yang Wang, Peng Wang, Zhenheng Yang, Chenxu Luo, Yi Yang, and Wei Xu · 2019
Later among the works it cites.
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Undeepvo: Monocular visual odometry through unsupervised deep learning
Ruihao Li, Sen Wang, Zhiqiang Long, and Dongbing Gu · 2018
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Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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.
Group normalization
Yuxin Wu and Kaiming He · 2018
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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
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Lipu Zhou, Jiamin Ye, Montiel Abello, Shengze Wang, and Michael Kaess · 2018
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Omnimvs: End-to-end learning for omnidirectional stereo matching
Changhee Won, Jongbin Ryu, and Jongwoo Lim · 2019
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Mvscrf: Learning multi-view stereo with conditional random fields
Youze Xue, Jiansheng Chen, Weitao Wan, Yiqing Huang, Cheng Yu, Tianpeng Li, and Jiayu Bao · 2019
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Matryodshka: Real-time 6dof video view synthesis using multi-sphere images
Benjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, and James Tompkin · 2020
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Omniphotos: casual 360° vr photography
Tobias Bertel, Mingze Yuan, Reuben Lindroos, and Christian Richardt · 2020
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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3d packing for self-supervised monocular depth estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
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3d packing for self-supervised monocular depth estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
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Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance
Marvin Klingner, Jan-Aike Termöhlen, Jonas Mikolajczyk, and Tim Fingscheidt · 2020
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Attention-aware multi-view stereo
Keyang Luo, Tao Guan, Lili Ju, Yuesong Wang, Zhuo Chen, and Yawei Luo · 2020
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Neural ray surfaces for self-supervised learning of depth and ego-motion
Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Wolfram Burgard, Greg Shakhnarovich, and Adrien Gaidon · 2020
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Bifuse: Monocular 360 depth estimation via bi-projection fusion
Fu-En Wang, Yu-Hsuan Yeh, Min Sun, Wei-Chen Chiu, and Yi-Hsuan Tsai · 2020
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360sd-net: 360° stereo depth estimation with learnable cost volume
Ning-Hsu Wang, Bolivar Solarte, Yi-Hsuan Tsai, Wei-Chen Chiu, and Min Sun · 2020
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Distortion-aware monocular depth estimation for omnidirectional images
Hong-Xiang Chen, Kunhong Li, Zhiheng Fu, Mengyi Liu, Zonghao Chen, and Yulan Guo · 2021
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