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We address the problem of finding reliable dense correspondences between a pair of images.
Determining optical flow
B. K. Horn and B. G. Schunck · 1981
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An iterative image registration technique with an application to stereo vision
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A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry
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Local grayvalue invariants for image retrieval
C. Schmid and R. Mohr · 1997
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An affine invariant interest point detector
K. Mikolajczyk and C. Schmid · 2002
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Automated scene matching in movies
F. Schaffalitzky and A. Zisserman · 2002
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Video Google: A text retrieval approach to object matching in videos
J. Sivic and A. Zisserman · 2003
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Distinctive image features from scale-invariant keypoints
D. G. Lowe · 2004
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Stereo processing by semiglobal matching and mutual information
H. Hirschmüller · 2008
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Learned local descriptors for recognition and matching
M. Jahrer, M. Grabner, and H. Bischof · 2008
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Sift-flow: Dense correspondence across different scenes
C. Liu, J. Yuen, A. Torralba, J. Sivic, and W. T. Freeman · 2008
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Local invariant feature detectors: A survey
T. Tuytelaars and K. Mikolajczyk · 2008
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SCRAMSAC: Improving RANSAC‘s Efficiency with a Spatial Consistency Filter
T. Sattler, B. Leibe, and L. Kobbelt · 2009
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Secrets of optical flow estimation and their principles
D. Sun, S. Roth, and M. J. Black · 2010
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Building rome in a day
S. Agarwal, Y. Furukawa, N. Snavely, I. Simon, B. Curless, S. M. Seitz, and R. Szeliski · 2011
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Large displacement optical flow: descriptor matching in variational motion estimation
T. Brox and J. Malik · 2011
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The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2011
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Descriptor matching with convolutional neural networks: a comparison to SIFT
P. Fischer, A. Dosovitskiy, and T. Brox · 2014
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Do convnets learn correspondence?
J. Long, N. Zhang, and T. Darrell · 2014
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Learning local feature descriptors using convex optimisation
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
LIFT: Learned invariant feature transform
K. M. Yi, E. Trulls, V. Lepetit, and P. Fua · 2016
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GMS: Grid-based motion statistics for fast, ultra-robust feature correspondence
J. Bian, W.-Y. Lin, Y. Matsushita, S.-K. Yeung, T.-D. Nguyen, and M.-M. Cheng · 2017
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Proposal flow: Semantic correspondences from object proposals
B. Ham, M. Cho, C. Schmid, and J. Ponce · 2017
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SCNet: Learning Semantic Correspondence
K. Han, R. S. Rezende, B. Ham, K.-Y. K. Wong, M. Cho, C. Schmid, and J. Ponce · 2017
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End-to-end learning of geometry and context for deep stereo regression
A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach, and A. Bry · 2017
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Large-scale image retrieval with attentive deep local features
H. Noh, A. Araujo, J. Sim, T. Weyand, and B. Han · 2017
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Cited alongside, same era.
FlowNet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. Van Der Smagt, D. Cremers, and T. Brox · 2015
Cited alongside, same era.
MatchNet: Unifying feature and metric learning for patch-based matching
X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Discriminative learning of deep convolutional feature point descriptors
E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer · 2015
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Learning to compare image patches via convolutional neural networks
S. Zagoruyko and N. Komodakis · 2015
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PN-Net: Conjoined triple deep network for learning local image descriptors
V. Balntas, E. Johns, L. Tang, and K. Mikolajczyk · 2016
Cited alongside, same era.
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Convolutional neural network architecture for geometric matching
I. Rocco, R. Arandjelović, and J. Sivic · 2017
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Matching neural paths: transfer from recognition to correspondence search
N. Savinov, L. Ladicky, and M. Pollefeys · 2017
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Comparative evaluation of hand-crafted and learned local features
J. L. Schonberger, H. Hardmeier, T. Sattler, and M. Pollefeys · 2017
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Learning category-specific mesh reconstruction from image collections
A. Kanazawa, S. Tulsiani, A. A. Efros, and J. Malik · 2018
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End-to-end weakly-supervised semantic alignment
I. Rocco, R. Arandjelović, and J. Sivic · 2018
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Benchmarking 6dof urban visual localization in changing conditions
T. Sattler, W. Maddern, C. Toft, A. Torii, L. Hammarstrand, E. Stenborg, D. Safari, M. Okutomi, M. Pollefeys, J. Sivic, F. Kahl, and T. Pajdla · 2018
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PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz · 2018
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InLoc: Indoor visual localization with dense matching and view synthesis
H. Taira, M. Okutomi, T. Sattler, M. Cimpoi, M. Pollefeys, J. Sivic, T. Pajdla, and A. Torii · 2018
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