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Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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High speed obstacle avoidance using monocular vision and reinforcement learning
Jeff Michels, Ashutosh Saxena, and Andrew Y Ng · 2005
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li jia Li, Kai Li, and Li Fei-fei · 2009
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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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Displets: Resolving stereo ambiguities using object knowledge
Fatma Güney and Andreas Geiger · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
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Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
Bo Li, Chunhua Shen, Yuchao Dai, Anton Van, and Mingyi He · 2015
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Exploiting semantic information and deep matching for optical flow
Min Bai, Wenjie Luo, Kaustav Kundu, and Raquel Urtasun · 2016
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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2016
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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
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar BG, Gustavo Carneiro, and Ian Reid · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 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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Demon: Depth and motion network for learning monocular stereo
Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox · 2017
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Unsupervised learning of geometry with edge-aware depth-normal consistency
Zhenheng Yang, Peng Wang, Wei Xu, Liang Zhao, and Ramakant Nevatia · 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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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Zhichao Yin and Jianping Shi · 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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Df-net: Unsupervised joint learning of depth and flow using cross-task consistency
Yuliang Zou, Zelun Luo, and Jia-Bin Huang · 2018
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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 · 2019
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Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos
Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova · 2019
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Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow · 2018
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Learning monocular depth by distilling cross-domain stereo networks
Xiaoyang Guo, Hongsheng Li, Shuai Yi, Jimmy Ren, and Xiaogang Wang · 2018
Cited alongside, same era.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Supervising the new with the old: Learning sfm from sfm
Maria Klodt and Andrea Vedaldi · 2018
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Single-image depth estimation based on fourier domain analysis
Jae-Han Lee, Minhyeok Heo, Kyung-Rae Kim, and Chang-Su Kim · 2018
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Learning to fuse things and stuff
Jie Li, Allan Raventos, Arjun Bhargava, Takaaki Tagawa, and Adrien Gaidon · 2018
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Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
Reza Mahjourian, Martin Wicke, and Anelia Angelova · 2018
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Towards scene understanding: Unsupervised monocular depth estimation with semantic-aware representation
Po-Yi Chen, Alexander H. Liu, Yen-Cheng Liu, and Yu-Chiang Frank Wang · 2019
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Packnet-sfm: 3d packing for self-supervised monocular depth estimation
Vitor Guizilini, Sudeep Pillai, Rares Ambrus, and Adrien Gaidon · 2019
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Panoptic feature pyramid networks
Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Dollár · 2019
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Spigan: Privileged adversarial learning from simulation
Kuan-Hui Lee, German Ros, Jie Li, and Adrien Gaidon · 2019
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Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape
Fabian Manhardt, Wadim Kehl, and Adrien Gaidon · 2019
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Sdnet: Semantically guided depth estimation network
Matthias Ochs, Adrian Kretz, and Rudolf Mester · 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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Seamless scene segmentation
Lorenzo Porzi, Samuel Rota Bulo, Aleksander Colovic, and Peter Kontschieder · 2019
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Pixel-adaptive convolutional neural networks
Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, and Jan Kautz · 2019
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Upsnet: A unified panoptic segmentation network
Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, and Raquel Urtasun · 2019
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