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Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches.
A pixel dissimilarity measure that is insensitive to image sampling
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
D. Scharstein and R. Szeliski · 2002
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Stereo matching using belief propagation
J. Sun, N.-N. Zheng, and H.-Y. Shum · 2003
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Stereo processing by semiglobal matching and mutual information
H. Hirschmuller · 2008
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M. Bleyer, C. Rother, P. Kohli, D. Scharstein, and S. Sinha · 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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Joint optimization for object class segmentation and dense stereo reconstruction
L. Ladickỳ, P. Sturgess, C. Russell, S. Sengupta, Y. Bastanlar, W. Clocksin, and P. H. Torr · 2012
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A tour of modern image filtering: New insights and methods, both practical and theoretical
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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High-resolution stereo datasets with subpixel-accurate ground truth
D. Scharstein, H. Hirschmüller, Y. Kitajima, G. Krathwohl, N. Nešić, X. Wang, and P. Westling · 2014
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Efficient joint segmentation, occlusion labeling, stereo and flow estimation
K. Yamaguchi, D. McAllester, and R. Urtasun · 2014
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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
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Displets: Resolving stereo ambiguities using object knowledge
F. Guney and A. Geiger · 2015
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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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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Efficient deep learning for stereo matching
W. Luo, A. G. Schwing, and R. Urtasun · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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Stereo matching by training a convolutional neural network to compare image patches
J. Zbontar and Y. LeCun · 2016
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Detect, replace, refine: Deep structured prediction for pixel wise labeling
S. Gidaris and N. Komodakis · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
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