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Recently, learning-based multi-view stereo methods have achieved promising results.
A space-sweep approach to true multi-image matching
R. T. Collins · 1996
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Handling occlusions in dense multi-view stereo
Sing Bing Kang, R. Szeliski, and Jinxiang Chai · 2001
Earlier work this paper cites.
A comparison and evaluation of multi-view stereo reconstruction algorithms
Steven M. Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski · 2006
Earlier work this paper cites.
Multi-view stereo revisited
M. Goesele, B. Curless, and S. M. Seitz · 2006
Earlier work this paper cites.
Mve: A multi-view reconstruction environment
Simon Fuhrmann, Fabian Langguth, and Michael Goesele · 2007
Earlier work this paper cites.
Using multiple hypotheses to improve depth-maps for multi-view stereo
Neill D. F. Campbell, George Vogiatzis, Carlos Hernández, and Roberto Cipolla · 2008
Earlier work this paper cites.
Accurate, dense, and robust multiview stereopsis
Y. Furukawa and J. Ponce · 2010
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A quantitative evaluation of confidence measures for stereo vision
X. Hu and P. 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
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Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
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Patchmatch based joint view selection and depthmap estimation
E. Zheng, E. Dunn, V. Jojic, and J. M. Frahm · 2014
Earlier work this paper cites.
Multi-view stereo: A tutorial
Yasutaka Furukawa and Carlos Hernández · 2015
Earlier work this paper cites.
Massively parallel multiview stereopsis by surface normal diffusion
S. Galliani, K. Lasinger, and K. Schindler · 2015
Earlier work this paper cites.
Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and koray kavukcuoglu · 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.
Pixelwise view selection for unstructured multi-view stereo
Johannes L. Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 2016
Cited alongside, same era.
Patch based confidence prediction for dense disparity map
Akihito Seki and Marc Pollefeys · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Large-scale data for multiple-view stereopsis
Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl · 2016
Cited alongside, same era.
Tanks and temples: Benchmarking large-scale scene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 2017
Cited alongside, same era.
A multi-view stereo benchmark with high-resolution images and multi-camera videos
T. Schöps, J. L. Schönberger, S. Galliani, T. Sattler, K. Schindler, M. Pollefeys, and A. Geiger · 2017
Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan · 2019
Later among the works it cites.
Point-based multi-view stereo network
Rui Chen, Songfang Han, Jing Xu, and Hao Su · 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.
P-mvsnet: Learning patch-wise matching confidence aggregation for multi-view stereo
Keyang Luo, Tao Guan, Lili Ju, Haipeng Huang, and Yawei Luo · 2019
Later among the works it cites.
Dpsnet: End-to-end deep plane sweep stereo
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, and In So Kweon · 2019
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Group-wise correlation stereo network
Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li · 2019
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Cited alongside, same era.
Learned multi-patch similarity
W. Hartmann, S. Galliani, M. Havlena, L. V. Gool, and K. 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.
Quantitative evaluation of confidence measures in a machine learning world
M. Poggi, F. Tosi, and S. Mattoccia · 2017
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.
Deepmvs: Learning multi-view stereopsis
P. Huang, K. Matzen, J. Kopf, N. Ahuja, and J. Huang · 2018
Cited alongside, same era.
Later among the works it cites.
Unified confidence estimation networks for robust stereo matching
S. Kim, D. Min, S. Kim, and K. Sohn · 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.
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
Later among the works it cites.
Planar prior assisted patchmatch multi-view stereo
Qingshan Xu and Wenbing Tao · 2020
Closest in time.
Learning inverse depth regression for multi-view stereo with correlation cost volume
Qingshan Xu and Wenbing Tao · 2020
Closest in time.
Deep stereo using adaptive thin volume representation with uncertainty awareness, 2020
Shuo Cheng, Zexiang Xu, Shilin Zhu, Zhuwen Li, Li Erran Li, Ravi Ramamoorthi, and Hao Su · 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.
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.