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We address the problem of estimating a high quality dense depth map from a single RGB input image.
Image quality assessment: from error visibility to structural similarity
Jiheng Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli · 2004
Earlier work this paper cites.
Learning depth from single monocular images
Ashutosh Saxena, Sung H. Chung, and Andrew Y. Ng · 2005
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Active refocusing of images and videos
Francesc Moreno-Noguer, Peter N. Belhumeur, and Shree K. Nayar · 2007
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Depth-assisted real-time 3d object detection for augmented reality
Wonwoo Lee, Nohyoung Park, and Woontack Woo · 2011
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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A category-level 3d object dataset: Putting the kinect to work
Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T Barron, Mario Fritz, Kate Saenko, and Trevor Darrell · 2013
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Sun3d: A database of big spaces reconstructed using sfm and object labels
J. Xiao, A. Owens, and A. Torralba · 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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Flownet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Sun rgb-d: A rgb-d scene understanding benchmark suite
S. Song, S. P. Lichtenberg, and J. Xiao · 2015
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar B.G., Gustavo Carneiro, and Ian Reid · 2016
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Fusenet: Incorporating depth into semantic segmentation via fusion-based cnn architecture
Caner Hazirbas, Lingni Ma, Csaba Domokos, and Daniel Cremers · 2016
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Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
Earlier work this paper cites.
Learning depth from single monocular images using deep convolutional neural fields
Fayao Liu, Chunhua Shen, Guosheng Lin, and I. Reid · 2016
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A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. Guibas · 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.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Semi-supervised deep learning for monocular depth map prediction
Yevhen Kuznietsov, Jörg Stückler, and Bastian Leibe · 2017
Cited alongside, same era.
Super-convergence: Very fast training of residual networks using large learning rates
Leslie N. Smith and Nicholay Topin · 2017
Cited alongside, same era.
Demon: Depth and motion network for learning monocular stereo
Non-local neural networks
Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He · 2018
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Structured attention guided convolutional neural fields for monocular depth estimation
Dong Xu, Wei Wang, Hao Tang, Hong W. Liu, Nicu Sebe, and Elisa Ricci · 2018
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Deeptam: Deep tracking and mapping
Huizhong Zhou, Benjamin Ummenhofer, and Thomas Brox · 2018
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Structure-aware residual pyramid network for monocular depth estimation
Xiaotian Chen, Xuejin Chen, and Zheng-Jun Zha · 2019
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Image captioning: Transforming objects into words
Simao Herdade, Armin Kappeler, Kofi Boakye, and Joao Soares · 2019
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From big to small: Multi-scale local planar guidance for monocular depth estimation
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Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox · 2017
Cited alongside, same era.
Multi-scale continuous crfs as sequential deep networks for monocular depth estimation
Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, and Nicu Sebe · 2017
Cited alongside, same era.
High quality monocular depth estimation via transfer learning
Ibraheem Alhashim and Peter Wonka · 2018
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Cited alongside, same era.
Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Nematollah Batmanghelich, and Dacheng Tao · 2018
Cited alongside, same era.
Monocular depth estimation with affinity, vertical pooling, and label enhancement
Yukang Gan, Xiangyu Xu, Wenxiu Sun, and Liang Lin · 2018
Cited alongside, same era.
Detail preserving depth estimation from a single image using attention guided networks
Zhixiang Hao, Yu Li, Shaodi You, and Feng Lu · 2018
Cited alongside, same era.
Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh · 2019
Later among the works it cites.
Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Sharpnet: Fast and accurate recovery of occluding contours in monocular depth estimation
Michael Ramamonjisoa and Vincent Lepetit · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
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Enforcing geometric constraints of virtual normal for depth prediction
Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan · 2019
Later among the works it cites.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Meshed-memory transformer for image captioning
Marcella Cornia, Matteo Stefanini, Lorenzo Baraldi, and Rita Cucchiara · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
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Depthlab: Real-time 3d interaction with depth maps for mobile augmented reality
Ruofei Du, Eric Lee Turner, Maksym Dzitsiuk, Luca Prasso, Ivo Duarte, Jason Dourgarian, Joao Afonso, Jose Pascoal, Josh Gladstone, Nuno Moura e Silva Cruces, Shahram Izadi, Adarsh Kowdle, Konstantine Nicholas John Tsotsos, and David Kim · 2020
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Guiding monocular depth estimation using depth-attention volume
Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, and Janne Heikkila · 2020
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