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Multi-view stereo (MVS) is a crucial task for precise 3D reconstruction.
Scale-space theory: A basic tool for analyzing structures at different scales
Tony Lindeberg · 1994
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Pvsnet: Pixelwise visibility-aware multi-view stereo network
Qingshan Xu and Wenbing Tao · 2007
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Patchmatch: A randomized correspondence algorithm for structural image editing
Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman · 2009
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Multi-view stereo: A tutorial
Yasutaka Furukawa and Carlos Hernández · 2015
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Massively parallel multiview stereopsis by surface normal diffusion
Silvano Galliani, Katrin Lasinger, and Konrad Schindler · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Large-scale data for multiple-view stereopsis
Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl · 2016
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Differential geometry of curves and surfaces: revised and updated second edition
Manfredo P Do Carmo · 2016
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Depth map super-resolution by deep multi-scale guidance
Tak-Wai Hui, Chen Change Loy, and Xiaoou Tang · 2016
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Dynamic filter networks
Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
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Pixelwise view selection for unstructured multi-view stereo
Johannes L Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 2016
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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 · 2017
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Surfacenet: An end-to-end 3d neural network for multiview stereopsis
Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, and Lu Fang · 2017
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Learning a multi-view stereo machine
Abhishek Kar, Christian Häne, and Jitendra Malik · 2017
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Tanks and temples: Benchmarking large-scale scene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Multi-scale geometric consistency guided multi-view stereo
Qingshan Xu and Wenbing Tao · 2019
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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
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Deformable convnets v2: More deformable, better results
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai · 2019
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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
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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
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Attention-aware multi-view stereo
Keyang Luo, Tao Guan, Lili Ju, Yuesong Wang, Zhuo Chen, and Yawei Luo · 2020
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Scale-adaptive convolutions for scene parsing
Rui Zhang, Sheng Tang, Yongdong Zhang, Jintao Li, and Shuicheng Yan · 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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Face recognition with contrastive convolution
Chunrui Han, Shiguang Shan, Meina Kan, Shuzhe Wu, and Xilin Chen · 2018
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Dynamic filtering with large sampling field for convnets
Jialin Wu, Dai Li, Yu Yang, Chandrajit Bajaj, and Xiangyang Ji · 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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Point-based multi-view stereo network
Rui Chen, Songfang Han, Jing Xu, and Hao Su · 2019
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Bp-mvsnet: Belief-propagation-layers for multi-view-stereo
Christian Sormann, Patrick Knöbelreiter, Andreas Kuhn, Mattia Rossi, Thomas Pock, and Friedrich Fraundorfer · 2020
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Marmvs: Matching ambiguity reduced multiple view stereo for efficient large scale scene reconstruction
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, and Yunan Zheng · 2020
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Cost volume pyramid based depth inference for multi-view stereo
Jiayu Yang, Wei Mao, Jose M Alvarez, and Miaomiao Liu · 2020
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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
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Pyramid multi-view stereo net with self-adaptive view aggregation
Hongwei Yi, Zizhuang Wei, Mingyu Ding, Runze Zhang, Yisong Chen, Guoping Wang, and Yu-Wing Tai · 2020
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Fast-mvsnet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement
Zehao Yu and Shenghua Gao · 2020
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Visibility-aware multi-view stereo network
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, and Tian Fang · 2020
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Patchmatchnet: Learned multi-view patchmatch stereo
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, and Marc Pollefeys · 2021
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