Fetching the paper…
Reading the bibliography…
We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT.
Computer matching of areas in stereo images
Marsha J Hannah · 1974
Earlier work this paper cites.
Non-parametric local transforms for computing visual correspondence
Ramin Zabih and John Woodfill · 1994
Earlier work this paper cites.
Convergent tree-reweighted message passing for energy minimization
Vladimir Kolmogorov · 2006
Earlier work this paper cites.
Stereo processing by semiglobal matching and mutual information
Heiko Hirschmuller · 2007
Earlier work this paper cites.
Stereo processing by semiglobal matching and mutual information
Heiko Hirschmuller · 2007
Earlier work this paper cites.
Patchmatch: A randomized correspondence algorithm for structural image editing
Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman · 2009
Earlier work this paper cites.
Robust stereo matching using adaptive normalized cross-correlation
Yong Seok Heo, Kyong Mu Lee, and Sang Uk Lee · 2010
Earlier work this paper cites.
A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
Earlier work this paper cites.
Improved census transforms for resource-optimized stereo vision
Wade S Fife and James K Archibald · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Joint 3d estimation of vehicles and scene flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2015
Earlier work this paper cites.
Computing the stereo matching cost with a convolutional neural network
Jure Zbontar and Yann LeCun · 2015
Earlier work this paper cites.
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
Earlier work this paper cites.
Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
Earlier work this paper cites.
Sparse stereo disparity map densification using hierarchical image segmentation
Sébastien Drouyer, Serge Beucher, Michel Bilodeau, Maxime Moreaud, and Loïc Sorbier · 2017
Earlier work this paper cites.
Sparse stereo disparity map densification using hierarchical image segmentation
Sébastien Drouyer, Serge Beucher, Michel Bilodeau, Maxime Moreaud, and Loïc Sorbier · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Hierarchical discrete distribution decomposition for match density estimation
Zhichao Yin, Trevor Darrell, and Fisher Yu · 2019
Later among the works it cites.
Ga-net: Guided aggregation net for end-to-end stereo matching
Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr · 2019
Later among the works it cites.
Domain-invariant stereo matching networks
Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, and Philip Torr · 2019
Later among the works it cites.
Bi3d: Stereo depth estimation via binary classifications
Abhishek Badki, Alejandro Troccoli, Kihwan Kim, Jan Kautz, Pradeep Sen, and Orazio Gallo · 2020
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A multi-view stereo benchmark with high-resolution images and multi-camera videos
Thomas Schöps, Johannes L. Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger · 2017
Cited alongside, same era.
Continuous 3d label stereo matching using local expansion moves
Tatsunori Taniai, Yasuyuki Matsushita, Yoichi Sato, and Takeshi Naemura · 2017
Cited alongside, same era.
Pyramid stereo matching network
Jia-Ren Chang and Yong-Sheng Chen · 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.
Super-convergence: Very fast training of neural networks using large learning rates, 2018
Leslie N. Smith and Nicholay Topin · 2018
Cited alongside, same era.
Falling things: A synthetic dataset for 3d object detection and pose estimation
Jonathan Tremblay, Thang To, and Stan Birchfield · 2018
Cited alongside, same era.
Mvsnet: Depth inference for unstructured multi-view stereo, 2018
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
Cited alongside, same era.
Suw-learn: Joint supervised, unsupervised, weakly supervised deep learning for monocular depth estimation
Haoyu Ren, Aman Raj, Mostafa El-Khamy, and Jungwon Lee · 2020
Later among the works it cites.
Adastereo: A simple and efficient approach for adaptive stereo matching, 2020
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, and Jianping Shi · 2020
Later among the works it cites.
Edgestereo: An effective multi-task learning network for stereo matching and edge detection
Xiao Song, Xu Zhao, Liangji Fang, Hanwen Hu, and Yizhou Yu · 2020
Later among the works it cites.
Hitnet: Hierarchical iterative tile refinement network for real-time stereo matching
Vladimir Tankovich, Christian Häne, Sean Fanello, Yinda Zhang, Shahram Izadi, and Sofien Bouaziz · 2020
Later among the works it cites.
Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
Later among the works it cites.
Tartanair: A dataset to push the limits of visual slam
Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer · 2020
Later among the works it cites.
Learning stereo from single images
Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov, Gabriel J Brostow, and Michael Firman · 2020
Later among the works it cites.
Crosspatch-based rolling label expansion for dense stereo matching
Huaiyuan Xu, Xiaodong Chen, Haitao Liang, Siyu Ren, Yi Wang, and Huaiyu Cai · 2020
Later among the works it cites.
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
Later among the works it cites.
Superpixel alpha-expansion and normal adjustment for stereo matching
Penglei Ji, Jie Li, Hanchao Li, and Xinguo Liu · 2021
Closest in time.
Pvstereo: Pyramid voting module for end-to-end self-supervised stereo matching, 2021
Hengli Wang, Rui Fan, Peide Cai, and Ming Liu · 2021
Closest in time.