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Single image surface normal estimation and depth estimation are closely related problems as the former can be calculated from the latter.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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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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Data-driven 3d primitives for single image understanding
David F Fouhey, Abhinav Gupta, and Martial Hebert · 2013
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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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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High-resolution stereo datasets with subpixel-accurate ground truth
Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nešić, Xi Wang, and Porter Westling · 2014
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Deep convolutional neural fields for depth estimation from a single image
Fayao Liu, Chunhua Shen, and Guosheng Lin · 2015
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Designing deep networks for surface normal estimation
Xiaolong Wang, David Fouhey, and Abhinav Gupta · 2015
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Marr revisited: 2d-3d alignment via surface normal prediction
Aayush Bansal, Bryan Russell, and Abhinav Gupta · 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
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Estimating depth from monocular images as classification using deep fully convolutional residual networks
Yuanzhouhan Cao, Zifeng Wu, and Chunhua Shen · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 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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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Evaluation of cnn-based single-image depth estimation methods
Tobias Koch, Lukas Liebel, Friedrich Fraundorfer, and Marco Korner · 2018
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Geonet: Geometric neural network for joint depth and surface normal estimation
Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, and Jiaya Jia · 2018
Cited alongside, same era.
Super-convergence: Very fast training of residual networks using large learning rates
Leslie N Smith and Nicholay Topin · 2018
Cited alongside, same era.
Enforcing geometric constraints of virtual normal for depth prediction
Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan · 2019
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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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Surface normal estimation of tilted images via spatial rectifier
Tien Do, Khiem Vuong, Stergios I Roumeliotis, and Hyun Soo Park · 2020
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Guiding monocular depth estimation using depth-attention volume
Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, and Janne Heikkilä · 2020
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One shot 3d photography
Johannes Kopf, Kevin Matzen, Suhib Alsisan, Ocean Quigley, Francis Ge, Yangming Chong, Josh Patterson, Jan-Michael Frahm, Shu Wu, Matthew Yu, et al · 2020
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Geonet++: Iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation
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Jingwei Huang, Yichao Zhou, Thomas Funkhouser, and Leonidas J Guibas · 2019
Cited alongside, same era.
From big to small: Multi-scale local planar guidance for monocular depth estimation
Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh · 2019
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Spherical regression: Learning viewpoints, surface normals and 3d rotations on n-spheres
Shuai Liao, Efstratios Gavves, and Cees GM Snoek · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank 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, et al · 2019
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Sharpnet: Fast and accurate recovery of occluding contours in monocular depth estimation
Michael Ramamonjisoa and Vincent Lepetit · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Xiaojuan Qi, Zhengzhe Liu, Renjie Liao, Philip HS Torr, Raquel Urtasun, and Jiaya Jia · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Estimating and exploiting the aleatoric uncertainty in surface normal estimation
Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla · 2021
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Adabins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
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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, et al · 2021
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Adaptive surface normal constraint for depth estimation
Xiaoxiao Long, Cheng Lin, Lingjie Liu, Wei Li, Christian Theobalt, Ruigang Yang, and Wenping Wang · 2021
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Transformers solve the limited receptive field for monocular depth prediction
Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, and Elisa Ricci · 2021
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