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The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity.
Very large-scale global sfm by distributed motion averaging
Siyu Zhu, Runze Zhang, Lei Zhou, Tianwei Shen, Tian Fang, Ping Tan, and Long Quan · 1912
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Photorealistic scene reconstruction by voxel coloring
Steven M Seitz and Charles R Dyer · 1999
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A theory of shape by space carving
Kiriakos N Kutulakos and Steven M Seitz · 2000
Earlier work this paper cites.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
Earlier work this paper cites.
Stereo matching using belief propagation
Jian Sun, Nan-Ning Zheng, and Heung-Yeung Shum · 2003
Earlier work this paper cites.
Accurate and efficient stereo processing by semi-global matching and mutual information
Heiko Hirschmuller · 2005
Earlier work this paper cites.
A quasi-dense approach to surface reconstruction from uncalibrated images
Maxime Lhuillier and Long Quan · 2005
Earlier work this paper cites.
Segment-based stereo matching using belief propagation and a self-ddapting dissimilarity measure
Andreas Klaus, Mario Sormann, and Konrad Karner · 2006
Earlier work this paper cites.
Using multiple hypotheses to improve depth-maps for multi-view stereo
Neill DF Campbell, George Vogiatzis, Carlos Hernández, and Roberto Cipolla · 2008
Earlier work this paper cites.
Accurate, dense, and robust multiview stereopsis
Yasutaka Furukawa and Jean Ponce · 2009
Earlier work this paper cites.
Cross-based local stereo matching using orthogonal integral images
Ke Zhang, Jiangbo Lu, and Gauthier Lafruit · 2009
Earlier work this paper cites.
Efficient large-scale multi-view stereo for ultra high-resolution image sets
Engin Tola, Christoph Strecha, and Pascal Fua · 2012
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A non-local cost aggregation method for stereo matching
Qingxiong Yang · 2012
Earlier work this paper cites.
Segment-tree based cost aggregation for stereo matching
Xing Mei, Xun Sun, Weiming Dong, Haitao Wang, and Xiaopeng Zhang · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Multi-view stereo: A tutorial
Yasutaka Furukawa, Carlos Hernández, et al · 2015
Earlier work this paper cites.
Massively parallel multiview stereopsis by surface normal diffusion
Silvano Galliani, Katrin Lasinger, and Konrad Schindler · 2015
Earlier work this paper cites.
Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
Earlier work this paper cites.
Computing the stereo matching cost with a convolutional neural network
Jure Zbontar and Yann LeCun · 2015
Earlier work this paper cites.
Large-scale data for multiple-view stereopsis
Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl · 2016
Earlier work this paper cites.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow sstimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
Cited alongside, same era.
Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
Cited alongside, same era.
Pixelwise view selection for unstructured multi-view stereo
Johannes L Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 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.
Learned multi-patch similarity
Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc Van Gool, and Konrad Schindler · 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
Multi-view stereo with asymmetric checkerboard propagation and multi-hypothesis joint view selection
Qingshan Xu and Wenbing Tao · 2018
Later among the works it cites.
Segstereo: Exploiting semantic information for disparity estimation
Guorun Yang, Hengshuang Zhao, Jianping Shi, Zhidong Deng, and Jiaya Jia · 2018
Later among the works it cites.
Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
Later among the works it cites.
Point-based multi-view stereo network
Rui Chen, Songfang Han, Jing Xu, and Hao Su · 2019
Closest in time.
Deeppruner: Learning efficient stereo matching via differentiable patchmatch
Duggal et al · 2019
Closest in time.
Multi-view stereo by temporal nonparametric fusion
Hou et al · 2019
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Cited alongside, same era.
Learning a multi-view stereo machine
Abhishek Kar, Christian Häne, and Jitendra Malik · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Tanks and temples: Benchmarking large-scale ccene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Cited alongside, same era.
Cascade residual learning: A two-stage convolutional neural network for stereo matching
Jiahao Pang, Wenxiu Sun, Jimmy SJ Ren, Chengxi Yang, and Qiong Yan · 2017
Cited alongside, same era.
Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
Cited alongside, same era.
Closest in time.
Tapa-mvs: Textureless-aware patchmatch multi-view stereo
Romanoni et al · 2019
Closest in time.
Real-time self-adaptive deep stereo
Tonioni et al · 2019
Closest in time.
Anytime stereo image depth estimation on mobile devices
Wang et al · 2019
Closest in time.
Mvscrf: Learning multi-view stereo with conditional random fields
Xue et al · 2019
Closest in time.
Hierarchical discrete distribution decomposition for match density estimation
Yin et al · 2019
Closest in time.
Group-wise correlation stereo network
Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li · 2019
Closest in time.
Dpsnet: end-to-end deep plane sweep stereo
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, and In So Kweon · 2019
Closest in time.
P-mvsnet: Learning patch-wise matching confidence aggregation for multi-view stereo
Keyang Luo, Tao Guan, Lili Ju, Haipeng Huang, and Yawei Luo · 2019
Closest in time.
Multi-level context ultra-aggregation for stereo matching
Guang-Yu Nie, Ming-Ming Cheng, Yun Liu, Zhengfa Liang, Deng-Ping Fan, Yue Liu, and Yongtian Wang · 2019
Closest in time.
Semantic stereo matching with pyramid cost volumes
Zhenyao Wu, Xinyi Wu, Xiaoping Zhang, Song Wang, and Lili Ju · 2019
Closest in time.
Hierarchical deep stereo matching on high-resolution images
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan · 2019
Closest in time.
Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan · 2019
Closest in time.
Ga-net: Guided aggregation net for end-to-end stereo matching
Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr · 2019
Closest in time.