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Multiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion.
A space-sweep approach to true multi-image matching
Robert T Collins · 1996
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Multiple view geometry in computer vision
Richard Hartley and Andrew Zisserman · 2003
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Multi-resolution real-time stereo on commodity graphics hardware
Ruigang Yang and Marc Pollefeys · 2003
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Variable baseline/resolution stereo
David Gallup, Jan-Michael Frahm, Philippos Mordohai, and Marc Pollefeys · 2008
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Fast cost-volume filtering for visual correspondence and beyond
Christoph Rhemann, Asmaa Hosni, Michael Bleyer, Carsten Rother, and Margrit Gelautz · 2011
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Real-time human pose recognition in parts from single depth images
Jamie Shotton, Andrew Fitzgibbon, Mat Cook, Toby Sharp, Mark Finocchio, Richard Moore, Alex Kipman, and Andrew Blake · 2011
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A quantitative evaluation of confidence measures for stereo vision
Xiaoyan Hu and Philippos Mordohai · 2012
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Indoor semantic segmentation using depth information
Camille Couprie, Clément Farabet, Laurent Najman, and Yann Lecun · 2013
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Guided image filtering
Kaiming He, Jian Sun, and Xiaoou Tang · 2013
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Segment-tree based cost aggregation for stereo matching
Xing Mei, Xun Sun, Weiming Dong, Haitao Wang, and Xiaopeng Zhang · 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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Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2014
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
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Deepstereo: Learning to predict new views from the world’s imagery
John Flynn, Ivan Neulander, James Philbin, and Noah Snavely · 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.
High-quality depth from uncalibrated small motion clip
Hyowon Ha, Sunghoon Im, Jaesik Park, Hae-Gon Jeon, and In So Kweon · 2016
Cited alongside, same era.
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.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
Cited alongside, same era.
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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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 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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Pyramid stereo matching network
Jia-Ren Chang and Yong-Sheng Chen · 2018
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2018
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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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Johannes L Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 2016
Cited alongside, same era.
Structure-from-motion revisited
Johannes Lutz Schönberger and Jan-Michael Frahm · 2016
Cited alongside, same era.
Action recognition from depth maps using deep convolutional neural networks
Pichao Wang, Wanqing Li, Zhimin Gao, Jing Zhang, Chang Tang, and Philip O Ogunbona · 2016
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
Cited alongside, same era.
Stereo matching by training a convolutional neural network to compare image patches
Jure Zbontar and Yann LeCun · 2016
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.
Surfacenet: an end-to-end 3d neural network for multiview stereopsis
Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, and Lu Fang · 2017
Cited alongside, same era.
Later among the works it cites.
Undeepvo: Monocular visual odometry through unsupervised deep learning
Ruihao Li, Sen Wang, Zhiqiang Long, and Dongbing Gu · 2018
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Learning deep correspondence through prior and posterior feature constancy
Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Linbo Qiao, Wei Chen, Li Zhou, and Jianfeng Zhang · 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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Practical deep stereo (pds): Toward applications-friendly deep stereo matching
Stepan Tulyakov, Anton Ivanov, and Francois Fleuret · 2018
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Learning depth from monocular videos using direct methods
Chaoyang Wang, Jose Miguel Buenaposada, Rui Zhu, and Simon Lucey · 2018
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Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 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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