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Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
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Stereo processing by semiglobal matching and mutual information
Heiko Hirschmuller · 2007
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Depth camera based localization and navigation for indoor mobile robots
Joydeep Biswas and Manuela Veloso · 2011
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A new monotonic, clone-independent, reversal symmetric, and condorcet-consistent single-winner election method
Markus Schulze · 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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A quantitative evaluation of confidence measures for stereo vision
Xiaoyan Hu and Philippos Mordohai · 2012
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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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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Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
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Large-scale direct slam with stereo cameras
Jakob Engel, Jörg Stückler, and Daniel Cremers · 2015
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Fast r-cnn
Ross Girshick · 2015
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Learning depth from single monocular images using deep convolutional neural fields
Fayao Liu, Chunhua Shen, Guosheng Lin, and Ian Reid · 2015
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Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 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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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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Gated feedback refinement network for dense image labeling
Md Amirul Islam, Mrigank Rochan, Neil DB Bruce, and Yang Wang · 2017
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Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 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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Semi-supervised deep learning for monocular depth map prediction
Yevhen Kuznietsov, Jorg Stuckler, and Bastian Leibe · 2017
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Cascade residual learning: A two-stage convolutional neural network for stereo matching
Jiahao Pang, Wenxiu Sun, Jimmy SJ Ren, Chengxi Yang, and Qiong Yan · 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
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Monocular depth estimation with affinity, vertical pooling, and label enhancement
Yukang Gan, Xiangyu Xu, Wenxiu Sun, and Liang Lin · 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.
Zoom and learn: Generalizing deep stereo matching to novel domains
Jiahao Pang, Wenxiu Sun, Chengxi Yang, Jimmy Ren, Ruichao Xiao, Jin Zeng, and Liang Lin · 2018
Cited alongside, same era.
A bi-directional message passing model for salient object detection
Lu Zhang, Ju Dai, Huchuan Lu, You He, and Gang Wang · 2018
Cited alongside, same era.
Semi-supervised monocular depth estimation with left-right consistency using deep neural network
Ali Jahani Amiri, Shing Yan Loo, and Hong Zhang · 2019
Cited alongside, same era.
Learning depth with convolutional spatial propagation network
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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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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Nlca-net: a non-local context attention network for stereo matching
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He · 2020
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Msmd-net: Deep stereo matching with multi-scale and multi-dimension cost volume
Zhelun Shen, Yuchao Dai, and Zhibo Rao · 2020
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Faster self-adaptive deep stereo
Haiyang Wang, Xinchao Wang, Jie Song, Jie Lei, and Mingli Song · 2020
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Improving deep stereo network generalization with geometric priors
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Xinjing Cheng, Peng Wang, and Ruigang Yang · 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.
Digging into self-supervised monocular depth estimation
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow · 2019
Cited alongside, same era.
Pl-slam: A stereo slam system through the combination of points and line segments
Ruben Gomez-Ojeda, Francisco-Angel Moreno, David Zuñiga-Noël, Davide Scaramuzza, and Javier Gonzalez-Jimenez · 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.
From big to small: Multi-scale local planar guidance for monocular depth estimation
Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Jialiang Wang, Varun Jampani, Deqing Sun, Charles Loop, Stan Birchfield, and Jan Kautz · 2020
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Aanet: Adaptive aggregation network for efficient stereo matching
Haofei Xu and Juyong Zhang · 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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Domain-invariant stereo matching networks
Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, and Philip Torr · 2020
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Adaptive unimodal cost volume filtering for deep stereo matching
Youmin Zhang, Yimin Chen, Xiao Bai, Jun Zhou, Kun Yu, Zhiwei Li, and Kuiyuan Yang · 2020
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Suppress and balance: A simple gated network for salient object detection
Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang · 2020
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On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Matteo Poggi, Seungryong Kim, Fabio Tosi, Sunok Kim, Filippo Aleotti, Dongbo Min, Kwanghoon Sohn, and Stefano Mattoccia · 2021
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Continual adaptation for deep stereo
Matteo Poggi, Alessio Tonioni, Fabio Tosi, Stefano Mattoccia, and Luigi Di Stefano · 2021
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On the synergies between machine learning and binocular stereo for depth estimation from images: a survey
Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai, and Stefano Mattoccia · 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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Monocular depth estimation using laplacian pyramid-based depth residuals
Minsoo Song, Seokjae Lim, and Wonjun Kim · 2021
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Adastereo: a simple and efficient approach for adaptive stereo matching
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, and Jianping Shi · 2021
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Mlda-net: multi-level dual attention-based network for self-supervised monocular depth estimation
Xibin Song, Wei Li, Dingfu Zhou, Yuchao Dai, Jin Fang, Hongdong Li, and Liangjun Zhang · 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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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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Local similarity pattern and cost self-reassembling for deep stereo matching networks
Biyang Liu, Huimin Yu, and Yangqi Long · 2022
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Exploiting pseudo labels in a self-supervised learning framework for improved monocular depth estimation
Andra Petrovai and Sergiu Nedevschi · 2022
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Adastereo: An efficient domain-adaptive stereo matching approach
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Yuexin Ma, Zhe Wang, and Jianping Shi · 2022
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