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What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?


Mar 22, 2018
Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox

* added references (UCL dataset); added IJCV copyright information 

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A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation


Dec 07, 2015
Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox

* Includes supplementary material 

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Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT


Jun 24, 2015
Philipp Fischer, Alexey Dosovitskiy, Thomas Brox

* This paper has been merged with arXiv:1406.6909 

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Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks


Jun 19, 2015
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox

* PAMI submission. Includes matching experiments as in arXiv:1405.5769v1. Also includes new network architectures, experiments on Caltech-256, experiment on combining Exemplar-CNN with clustering 

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U-Net: Convolutional Networks for Biomedical Image Segmentation


May 18, 2015
Olaf Ronneberger, Philipp Fischer, Thomas Brox

* conditionally accepted at MICCAI 2015 

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FlowNet: Learning Optical Flow with Convolutional Networks


May 04, 2015
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox

* Added supplementary material 

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