2.3k citations · 3.4k across the 5 of their papers we have counts for
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What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Nikolaus Mayer, Eddy Ilg, Philipp Fischer +4
The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations. As a…
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Nikolaus Mayer, Eddy Ilg, Philip Häusser +4
Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-ca…
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, Thomas Brox
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that r…
FlowNet: Learning Optical Flow with Convolutional Networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg +6
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation ha…
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
Philipp Fischer, Alexey Dosovitskiy, Thomas Brox
Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that th…