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Stereo matching aims to estimate the disparity between matching pixels in a stereo image pair, which is important to robotics, autonomous driving, and other computer vision tasks.
A pixel dissimilarity measure that is insensitive to image sampling
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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Surface patch similarity for near-duplicate 3d model retrieval
Kai Zhang and Janusz Kosecka · 2005
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Stereo processing by semiglobal matching and mutual information
Heiko Hirschmuller · 2007
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Adaptive support-weight approach for correspondence search with outlier rejection
Qingxiong Yang, Liang Wang, Rui Gan, Minglun Gong, and Yunde Jia · 2010
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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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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Efficient dense stereo matching using adaptive window and census transform
Rui Li and Xiaolin Wu · 2013
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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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Very deep convolutional networks for large-scale image recognition
Karen Simonyan · 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
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Efficient graph-based segmentation for stereo matching
Peter Pinggera, Thomas Pock, and Horst Bischof · 2015
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Building a 3-d line-based map using stereo slam
Guoxuan Zhang, Jin Han Lee, Jongwoo Lim, and Il Hong Suh · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Stereo matching by training a convolutional neural network to compare image patches
Jure Zbontar, Yann LeCun, et al · 2016
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 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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Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras
Raúl Mur-Artal and Juan D. Tardós · 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
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Pyramid stereo matching network
Jia-Ren Chang and Yong-Sheng Chen · 2018
Cited alongside, same era.
Occlusions, motion and depth boundaries with a generic network for disparity, optical flow or scene flow estimation
Eddy Ilg, Tonmoy Saikia, Margret Keuper, and Thomas Brox · 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.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Segstereo: Exploiting semantic information for disparity estimation
Guorun Yang, Hengshuang Zhao, Jianping Shi, Zhidong Deng, and Jiaya Jia · 2018
Cited alongside, same era.
Openpose: Realtime multi-person 2d pose estimation using part affinity fields
Security and privacy of smart home systems based on the internet of things and stereo matching algorithms
Aimin Yang, Chunying Zhang, Yongjie Chen, Yunxi Zhuansun, and Huixiang Liu · 2020
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Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
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Correlate-and-excite: Real-time stereo matching via guided cost volume excitation
Antyanta Bangunharcana, Jae Won Cho, Seokju Lee, In So Kweon, Kyung-Soo Kim, and Soohyun Kim · 2021
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Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers
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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Z. Cao, G. Hidalgo Martinez, T. Simon, S. Wei, and Y. A. Sheikh · 2019
Cited alongside, same era.
MMDetection: Open mmlab detection toolbox and benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin · 2019
Cited alongside, same era.
Deeppruner: Learning efficient stereo matching via differentiable patchmatch
Shivam Duggal, Shenlong Wang, Wei-Chiu Ma, Rui Hu, and Raquel Urtasun · 2019
Cited alongside, same era.
Group-wise correlation stereo network
Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li · 2019
Cited alongside, same era.
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, et al · 2019
Cited alongside, same era.
Guided stereo matching
Matteo Poggi, Davide Pallotti, Fabio Tosi, and Stefano Mattoccia · 2019
Cited alongside, same era.
Autodispnet: Improving disparity estimation with automl
Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, and Thomas Brox · 2019
Cited alongside, same era.
Zhelun Shen, Yuchao Dai, and Zhibo Rao · 2021
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Efficientnetv2: Smaller models and faster training
Mingxing Tan and Quoc Le · 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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Fadnet++: Real-time and accurate disparity estimation with configurable networks
Qiang Wang, Shaohuai Shi, Shizhen Zheng, Kaiyong Zhao, and Xiaowen Chu · 2021
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Mpvit: Multi-path vision transformer for dense prediction
Youngwan Lee, Jonghee Kim, Jeffrey Willette, and Sung Ju Hwang · 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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Mobilestereonet: Towards lightweight deep networks for stereo matching
Faranak Shamsafar, Samuel Woerz, Rafia Rahim, and Andreas Zell · 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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Opengait: Revisiting gait recognition toward better practicality
Chao Fan, Junhao Liang, Chuanfu Shen, Saihui Hou, Yongzhen Huang, and Shiqi Yu · 2023
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CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy, Yohann Cabon, Vaibhav Arora, Romain Brégier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and Jérôme Revaud · 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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Mocha-stereo: Motif channel attention network for stereo matching
Ziyang Chen, Wei Long, He Yao, Yongjun Zhang, Bingshu Wang, Yongbin Qin, and Jia Wu · 2024
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Neural markov random field for stereo matching
Tongfan Guan, Chen Wang, and Yun-Hui Liu · 2024
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Lightstereo: Channel boost is all your need for efficient 2d cost aggregation
Xianda Guo, Chenming Zhang, Dujun Nie, Wenzhao Zheng, Youmin Zhang, and Long Chen · 2024
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Iinet: Implicit intra-inter information fusion for real-time stereo matching
Ximeng Li, Chen Zhang, Wanjuan Su, and Wenbing Tao · 2024
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Adaptive multi-modal cross-entropy loss for stereo matching
Peng Xu, Zhiyu Xiang, Chengyu Qiao, Jingyun Fu, and Tianyu Pu · 2024
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