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We tackle the problem of estimating flow between two images with large lighting variations.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Scale & affine invariant interest point detectors
Krystian Mikolajczyk and Cordelia Schmid · 2004
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Efficient computation of optical flow using the census transform
Fridtjof Stein · 2004
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A variational model for the joint recovery of the fundamental matrix and the optical flow
Levi Valgaerts, Andrés Bruhn, and Joachim Weickert · 2008
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Structure-and motion-adaptive regularization for high accuracy optic flow
Andreas Wedel, Daniel Cremers, Thomas Pock, and Horst Bischof · 2009
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Sift flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2010
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Building rome in a day
Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M Seitz, and Richard Szeliski · 2011
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Three things everyone should know to improve object retrieval
Relja Arandjelović and Andrew Zisserman · 2012
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Usac: a universal framework for random sample consensus
Rahul Raguram, Ondrej Chum, Marc Pollefeys, Jiri Matas, and Jan-Michael Frahm · 2012
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Robust monocular epipolar flow estimation
Koichiro Yamaguchi, David McAllester, and Raquel Urtasun · 2013
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 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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Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness
J Yu Jason, Adam W Harley, and Konstantinos G Derpanis · 2016
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Structure-from-motion revisited
Johannes Lutz Schönberger and Jan-Michael Frahm · 2016
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Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors
Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk · 2017
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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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1 year, 1000 km: The oxford robotcar dataset
Will Maddern, Geoffrey Pascoe, Chris Linegar, and Paul Newman · 2017
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Relative camera pose estimation using convolutional neural networks
Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, and Esa Rahtu · 2017
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Working hard to know your neighbor’s margins: Local descriptor learning loss
Anastasya Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas · 2017
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Large-scale image retrieval with attentive deep local features
Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, and Bohyung Han · 2017
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Optical flow estimation using a spatial pyramid network
Anurag Ranjan and Michael J Black · 2017
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Unsupervised deep learning for optical flow estimation
Zhe Ren, Junchi Yan, Bingbing Ni, Bin Liu, Xiaokang Yang, and Hongyuan Zha · 2017
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Convolutional neural network architecture for geometric matching
Ignacio Rocco, Relja Arandjelovic, and Josef Sivic · 2017
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Demon: Depth and motion network for learning monocular stereo
Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox · 2017
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Superpoint: Self-supervised interest point detection and description
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2018
Dgc-net: Dense geometric correspondence network
Iaroslav Melekhov, Aleksei Tiulpin, Torsten Sattler, Marc Pollefeys, Esa Rahtu, and Juho Kannala · 2019
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R2d2: Reliable and repeatable detector and descriptor
Jerome Revaud, Cesar De Souza, Martin Humenberger, and Philippe Weinzaepfel · 2019
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From coarse to fine: Robust hierarchical localization at large scale
Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, and Marcin Dymczyk · 2019
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Models matter, so does training: An empirical study of cnns for optical flow estimation
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2019
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Learning data-adaptive interest points through epipolar adaptation
Guandao Yang, Tomasz Malisiewicz, Serge J Belongie, Erez Farhan, Sungsoo Ha, Yuewei Lin, Xiaojing Huang, Hanfei Yan, and Wei Xu · 2019
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Unsupervised deep epipolar flow for stationary or dynamic scenes
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Liteflownet: A lightweight convolutional neural network for optical flow estimation
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy · 2018
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Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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Unflow: Unsupervised learning of optical flow with a bidirectional census loss
Simon Meister, Junhwa Hur, and Stefan Roth · 2018
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Lf-net: learning local features from images
Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi · 2018
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Online temporal calibration for monocular visual-inertial systems
Tong Qin and Shaojie Shen · 2018
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Deep fundamental matrix estimation
René Ranftl and Vladlen Koltun · 2018
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Yiran Zhong, Pan Ji, Jianyuan Wang, Yuchao Dai, and Hongdong Li · 2019
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Epipolar transformers
Yihui He, Rui Yan, Katerina Fragkiadaki, and Shoou-I Yu · 2020
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What matters in unsupervised optical flow
Rico Jonschkowski, Austin Stone, Jon Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova · 2020
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Deep homography estimation for dynamic scenes
Hoang Le, Feng Liu, Shu Zhang, and Aseem Agarwala · 2020
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Flow2stereo: Effective self-supervised learning of optical flow and stereo matching
Pengpeng Liu, Irwin King, Michael R Lyu, and Jia Xu · 2020
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Niid-net: Adapting surface normal knowledge for intrinsic image decomposition in indoor scenes
Jundan Luo, Zhaoyang Huang, Yijin Li, Xiaowei Zhou, Guofeng Zhang, and Hujun Bao · 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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Ransac-flow: generic two-stage image alignment
Xi Shen, François Darmon, Alexei A Efros, and Mathieu Aubry · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Glu-net: Global-local universal network for dense flow and correspondences
Prune Truong, Martin Danelljan, and Radu Timofte · 2020
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Gocor: Bringing globally optimized correspondence volumes into your neural network
Prune Truong, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
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Learning feature descriptors using camera pose supervision
Qianqian Wang, Xiaowei Zhou, Bharath Hariharan, and Noah Snavely · 2020
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Optical flow in dense foggy scenes using semi-supervised learning
Wending Yan, Aashish Sharma, and Robby T Tan · 2020
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Content-aware unsupervised deep homography estimation
Jirong Zhang, Chuan Wang, Shuaicheng Liu, Lanpeng Jia, Nianjin Ye, Jue Wang, Ji Zhou, and Jian Sun · 2020
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Optical flow in the dark
Yinqiang Zheng, Mingfang Zhang, and Feng Lu · 2020
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