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Learning-based multi-view stereo (MVS) methods have demonstrated promising results.
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.
A quantitative evaluation of confidence measures for stereo vision
Xiaoyan Hu and Philippos Mordohai · 2012
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
Earlier work this paper cites.
Large scale multi-view stereopsis evaluation
Rasmus Jensen, Anders Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Patchmatch based joint view selection and depthmap estimation
Enliang Zheng, Enrique Dunn, Vladimir Jojic, and Jan-Michael Frahm · 2014
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.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Learning from scratch a confidence measure
Matteo Poggi and Stefano Mattoccia · 2016
Earlier work this paper cites.
Pixelwise view selection for unstructured multi-view stereo
Johannes L Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 2016
Earlier work this paper cites.
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
Cited alongside, same era.
Learning a multi-view stereo machine
Abhishek Kar, Christian Häne, and Jitendra Malik · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 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 scene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 2017
Point-based multi-view stereo network
Rui Chen, Songfang Han, Jing Xu, and Hao Su · 2019
Later among the works it cites.
Group-wise correlation stereo network
Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li · 2019
Later among the works it cites.
Laf-net: Locally adaptive fusion networks for stereo confidence estimation
Sunok Kim, Seungryong Kim, Dongbo Min, and Kwanghoon Sohn · 2019
Later among the works it cites.
Multi-scale geometric consistency guided multi-view stereo
Qingshan Xu and Wenbing Tao · 2019
Later among the works it cites.
Mvscrf: Learning multi-view stereo with conditional random fields
Youze Xue, Jiansheng Chen, Weitao Wan, Yiqing Huang, Cheng Yu, Tianpeng Li, and Jiayu Bao · 2019
Later among the works it cites.
Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan · 2019
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Cited alongside, same era.
Deepmvs: Learning multi-view stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang · 2018
Cited alongside, same era.
Unified confidence estimation networks for robust stereo matching
Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn · 2018
Cited alongside, same era.
Raynet: Learning volumetric 3d reconstruction with ray potentials
Despoina Paschalidou, Osman Ulusoy, Carolin Schmitt, Luc Van Gool, and Andreas Geiger · 2018
Cited alongside, same era.
Beyond local reasoning for stereo confidence estimation with deep learning
Fabio Tosi, Matteo Poggi, Antonio Benincasa, and Stefano Mattoccia · 2018
Cited alongside, same era.
Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
Cited alongside, same era.
Later among the works it cites.
Deep stereo using adaptive thin volume representation with uncertainty awareness
Shuo Cheng, Zexiang Xu, Shilin Zhu, Zhuwen Li, Li Erran Li, Ravi Ramamoorthi, and Hao Su · 2020
Closest in time.
Cascade cost volume for high-resolution multi-view stereo and stereo matching
Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan · 2020
Closest in time.
Cost volume pyramid based depth inference for multi-view stereo
Jiayu Yang, Wei Mao, Jose M Alvarez, and Miaomiao Liu · 2020
Closest in time.
Blendedmvs: A large-scale dataset for generalized multi-view stereo networks
Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan · 2020
Closest in time.
Learning stereo matchability in disparity regression networks
Jingyang Zhang, Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan · 2020
Closest in time.