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Stereo matching is a core task for many computer vision and robotics applications.
Multiway cut for stereo and motion with slanted surfaces
Stan Birchfield and Carlo Tomasi · 1999
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
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Comparison of graph cuts with belief propagation for stereo, using identical mrf parameters
Tappen · 2003
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Stereo processing by semiglobal matching and mutual information
Heiko Hirschmuller · 2007
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Dense stereo matching with application to augmented reality
Nadia Zenati and Noureddine Zerhouni · 2007
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A comparative study of energy minimization methods for markov random fields with smoothness-based priors
Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, and Carsten Rother · 2008
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Patchmatch stereo-stereo matching with slanted support windows
Michael Bleyer, Christoph Rhemann, and Carsten Rother · 2011
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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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
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A deep visual correspondence embedding model for stereo matching costs
Zhuoyuan Chen, Xun Sun, Liang Wang, Yinan Yu, and Chang Huang · 2015
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Matchnet: Unifying feature and metric learning for patch-based matching
Xufeng Han, Thomas Leung, Yangqing Jia, Rahul Sukthankar, and Alexander C Berg · 2015
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Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
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Discrete optimization for optical flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2015
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Learning to compare image patches via convolutional neural networks
Sergey Zagoruyko and Nikos Komodakis · 2015
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Meshstereo: A global stereo model with mesh alignment regularization for view interpolation
Chi Zhang, Zhiwei Li, Yanhua Cheng, Rui Cai, Hongyang Chao, and Yong Rui · 2015
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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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Efficient deep learning for stereo matching
Wenjie Luo, Alexander G Schwing, and Raquel Urtasun · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
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Patch based confidence prediction for dense disparity map
Akihito Seki and Marc Pollefeys · 2016
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Deep stereo matching with dense crf priors
Ron Slossberg, Aaron Wetzler, and Ron Kimmel · 2016
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Stereo matching by training a convolutional neural network to compare image patches
Jure Zbontar, Yann LeCun, et al · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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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
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End-to-end training of hybrid cnn-crf models for stereo
Patrick Knobelreiter, Christian Reinbacher, Alexander Shekhovtsov, and Thomas Pock · 2017
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A multi-view stereo benchmark with high-resolution images and multi-camera videos
Thomas Schops, Johannes L Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger · 2017
Cited alongside, same era.
Sgm-nets: Semi-global matching with neural networks
Akihito Seki and Marc Pollefeys · 2017
Cited alongside, same era.
Pyramid stereo matching network
Belief propagation reloaded: Learning bp-layers for labeling problems
Patrick Knobelreiter, Christian Sormann, Alexander Shekhovtsov, Friedrich Fraundorfer, and Thomas Pock · 2020
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A survey on deep learning techniques for stereo-based depth estimation
Hamid Laga, Laurent Valentin Jospin, Farid Boussaid, and Mohammed Bennamoun · 2020
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Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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Aanet: Adaptive aggregation network for efficient stereo matching
Haofei Xu and Juyong Zhang · 2020
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Domain-invariant stereo matching networks
Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, and Philip Torr · 2020
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Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers
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Jia-Ren Chang and Yong-Sheng Chen · 2018
Cited alongside, same era.
Stereonet: Guided hierarchical refinement for real-time edge-aware depth prediction
Sameh Khamis, Sean Fanello, Christoph Rhemann, Adarsh Kowdle, Julien Valentin, and Shahram Izadi · 2018
Cited alongside, same era.
Learning for disparity estimation through feature constancy
Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang · 2018
Cited alongside, same era.
Continuous 3D Label Stereo Matching using Local Expansion Moves
Tatsunori Taniai, Yasuyuki Matsushita, Yoichi Sato, and Takeshi Naemura · 2018
Cited alongside, same era.
Practical deep stereo (pds): Toward applications-friendly deep stereo matching
Stepan Tulyakov, Anton Ivanov, and Francois Fleuret · 2018
Cited alongside, same era.
Stereodrnet: Dilated residual stereonet
Rohan Chabra, Julian Straub, Christopher Sweeney, Richard Newcombe, and Henry Fuchs · 2019
Cited alongside, same era.
On the over-smoothing problem of cnn based disparity estimation
Chuangrong Chen, Xiaozhi Chen, and Hui Cheng · 2019
Cited alongside, same era.
Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X Creighton, Russell H Taylor, and Mathias Unberath · 2021
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Raft-stereo: Multilevel recurrent field transforms for stereo matching
Lahav Lipson, Zachary Teed, and Jia Deng · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Cfnet: Cascade and fused cost volume for robust stereo matching
Zhelun Shen, Yuchao Dai, and Zhibo Rao · 2021
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Loftr: Detector-free local feature matching with transformers
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou · 2021
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Hitnet: Hierarchical iterative tile refinement network for real-time stereo matching
Vladimir Tankovich, Christian Hane, Yinda Zhang, Adarsh Kowdle, Sean Fanello, and Sofien Bouaziz · 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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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Cswin transformer: A general vision transformer backbone with cross-shaped windows
Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, and Baining Guo · 2022
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Practical stereo matching via cascaded recurrent network with adaptive correlation
Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, and Shuaicheng Liu · 2022
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Pcw-net: Pyramid combination and warping cost volume for stereo matching
Zhelun Shen, Yuchao Dai, Xibin Song, Zhibo Rao, Dingfu Zhou, and Liangjun Zhang · 2022
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Large-scale outdoor scene reconstruction and correction with vision
Michael Tanner, Pedro Pinies, Lina Maria Paz, Ştefan Săftescu, Alex Bewley, Emil Jonasson, and Paul Newman · 2022
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Attention concatenation volume for accurate and efficient stereo matching
Gangwei Xu, Junda Cheng, Peng Guo, and Xin Yang · 2022
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Uncertainty guided adaptive warping for robust and efficient stereo matching
Junpeng Jing, Jiankun Li, Pengfei Xiong, Jiangyu Liu, Shuaicheng Liu, Yichen Guo, Xin Deng, Mai Xu, Lai Jiang, and Leonid Sigal · 2023
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Iterative geometry encoding volume for stereo matching
Gangwei Xu, Xianqi Wang, Xiaohuan Ding, and Xin Yang · 2023
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