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With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains.
Accurate and efficient stereo processing by semi-global matching and mutual information
Heiko Hirschmuller · 2005
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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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
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Deep learning, dark knowledge, and dark matter
Peter Sadowski, Julian Collado, Daniel Whiteson, and Pierre Baldi · 2015
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Computing the stereo matching cost with a convolutional neural network
Jure Zbontar and Yann LeCun · 2015
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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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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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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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Antti Tarvainen and Harri Valpola · 2017
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There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2018
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Pyramid stereo matching network
Jia-Ren Chang and Yong-Sheng Chen · 2018
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Born again neural networks
Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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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
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Learning depth with convolutional spatial propagation network
Xinjing Cheng, Peng Wang, and Ruigang Yang · 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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Stereo matching using multi-level cost volume and multi-scale feature constancy
Zhengfa Liang, Yulan Guo, Yiliu Feng, Wei Chen, Linbo Qiao, Li Zhou, Jianfeng Zhang, and Hengzhu Liu · 2019
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Real-time self-adaptive deep stereo
Alessio Tonioni, Fabio Tosi, Matteo Poggi, Stefano Mattoccia, and Luigi Di Stefano · 2019
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Hierarchical discrete distribution decomposition for match density estimation
Zhichao Yin, Trevor Darrell, and Fisher Yu · 2019
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Ga-net: Guided aggregation net for end-to-end stereo matching
Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr · 2019
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Matching-space stereo networks for cross-domain generalization
Changjiang Cai, Matteo Poggi, Stefano Mattoccia, and Philippos Mordohai · 2020
Itsa: An information-theoretic approach to automatic shortcut avoidance and domain generalization in stereo matching networks
WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, Alireza Bab-Hadiashar, and David Suter · 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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Open challenges in deep stereo: the booster dataset
Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoccia, and Luigi Di Stefano · 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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Improved cross-view completion pre-training for stereo matching
Philippe Weinzaepfel, Vaibhav Arora, Yohann Cabon, Thomas Lucas, Romain Brégier, Vincent Leroy, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, and Jérôme Revaud · 2022
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Cited alongside, same era.
Hierarchical neural architecture search for deep stereo matching
Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, and Zongyuan Ge · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Aanet: Adaptive aggregation network for efficient stereo matching
Haofei Xu and Juyong Zhang · 2020
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Revisiting knowledge distillation via label smoothing regularization
Li Yuan, Francis EH Tay, Guilin Li, Tao Wang, and Jiashi Feng · 2020
Cited alongside, same era.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Cited alongside, same era.
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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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Revisiting domain generalized stereo matching networks from a feature consistency perspective
Jiawei Zhang, Xiang Wang, Xiao Bai, Chen Wang, Lei Huang, Yimin Chen, Lin Gu, Jun Zhou, Tatsuya Harada, and Edwin R Hancock · 2022
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Decoupled knowledge distillation
Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, and Jiajun Liang · 2022
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Domain generalized stereo matching via hierarchical visual transformation
Tianyu Chang, Xun Yang, Tianzhu Zhang, and Meng Wang · 2023
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Learning depth estimation for transparent and mirror surfaces
Alex Costanzino, Pierluigi Zama Ramirez, Matteo Poggi, Fabio Tosi, Stefano Mattoccia, and Luigi Di Stefano · 2023
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Masked representation learning for domain generalized stereo matching
Zhibo Rao, Bangshu Xiong, Mingyi He, Yuchao Dai, Renjie He, Zhelun Shen, and Xing Li · 2023
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Digging into uncertainty-based pseudo-label for robust stereo matching
Zhelun Shen, Xibin Song, Yuchao Dai, Dingfu Zhou, Zhibo Rao, and Liangjun Zhang · 2023
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Nerf-supervised deep stereo
Fabio Tosi, Alessio Tonioni, Daniele De Gregorio, and Matteo Poggi · 2023
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Multi-label knowledge distillation
Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, and Sheng-Jun Huang · 2023
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Parameterized cost volume for stereo matching
Jiaxi Zeng, Chengtang Yao, Lidong Yu, Yuwei Wu, and Yunde Jia · 2023
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High-frequency stereo matching network
Haoliang Zhao, Huizhou Zhou, Yongjun Zhang, Jie Chen, Yitong Yang, and Yong Zhao · 2023
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