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Depth estimation is solved as a regression or classification problem in existing learning-based multi-view stereo methods.
Object-centered surface reconstruction: Combining multi-image stereo and shading
Pascal Fua and Yvan G Leclerc · 1995
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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
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A quasi-dense approach to surface reconstruction from uncalibrated images
Maxime Lhuillier and Long Quan · 2005
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A comparison and evaluation of multi-view stereo reconstruction algorithms
Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski · 2006
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Real-time visibility-based fusion of depth maps
Paul Merrell, Amir Akbarzadeh, Liang Wang, Philippos Mordohai, Jan-Michael Frahm, Ruigang Yang, David Nistér, and Marc Pollefeys · 2007
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Using multiple hypotheses to improve depth-maps for multi-view stereo
Neill DF Campbell, George Vogiatzis, Carlos Hernández, and Roberto Cipolla · 2008
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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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Accurate, dense, and robust multiview stereopsis
Yasutaka Furukawa and Jean Ponce · 2009
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Kinectfusion: Real-time dense surface mapping and tracking
Richard A Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J Davison, Pushmeet Kohi, Jamie Shotton, Steve Hodges, and Andrew Fitzgibbon · 2011
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Massively parallel multiview stereopsis by surface normal diffusion
Silvano Galliani, Katrin Lasinger, and Konrad Schindler · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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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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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
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang · 2020
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Planar prior assisted patchmatch multi-view stereo
Qingshan Xu and Wenbing Tao · 2020
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Dense hybrid recurrent multi-view stereo net with dynamic consistency checking
Jianfeng Yan, Zizhuang Wei, Hongwei Yi, Mingyu Ding, Runze Zhang, Yisong Chen, Guoping Wang, and Yu-Wing Tai · 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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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.
Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
Cited alongside, same era.
Point-based multi-view stereo network
Rui Chen, Songfang Han, Jing Xu, and Hao Su · 2019
Cited alongside, same era.
P-mvsnet: Learning patch-wise matching confidence aggregation for multi-view stereo
Keyang Luo, Tao Guan, Lili Ju, Haipeng Huang, and Yawei Luo · 2019
Cited alongside, same era.
Multi-scale geometric consistency guided multi-view stereo
Qingshan Xu and Wenbing Tao · 2019
Cited alongside, same era.
Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan · 2019
Cited alongside, same era.
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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Adaptive unimodal cost volume filtering for deep stereo matching
Youmin Zhang, Yimin Chen, Xiao Bai, Suihanjin Yu, Kun Yu, Zhiwei Li, and Kuiyuan Yang · 2020
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Epp-mvsnet: Epipolar-assembling based depth prediction for multi-view stereo
Xinjun Ma, Yue Gong, Qirui Wang, Jingwei Huang, Lei Chen, and Fan Yu · 2021
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Smd-nets: Stereo mixture density networks
Fabio Tosi, Yiyi Liao, Carolin Schmitt, and Andreas Geiger · 2021
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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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Aa-rmvsnet: Adaptive aggregation recurrent multi-view stereo network
Zizhuang Wei, Qingtian Zhu, Chen Min, Yisong Chen, and Guoping Wang · 2021
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Varifocalnet: An iou-aware dense object detector
Haoyang Zhang, Ying Wang, Feras Dayoub, and Niko Sunderhauf · 2021
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