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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…
Multi-view 3D Models from Single Images with a Convolutional Network
Maxim Tatarchenko, Alexey Dosovitskiy, Thomas Brox
We present a convolutional network capable of inferring a 3D representation of a previously unseen object given a single image of this object. Concretely, the network can predict a…
Inverting Visual Representations with Convolutional Networks
Alexey Dosovitskiy, Thomas Brox
Feature representations, both hand-designed and learned ones, are often hard to analyze and interpret, even when they are extracted from visual data. We propose a new approach to s…
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…
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
Margret Keuper, Evgeny Levinkov, Nicolas Bonneel +3
Formulations of the Image Decomposition Problem as a Multicut Problem (MP) w.r.t. a superpixel graph have received considerable attention. In contrast, instances of the MP w.r.t. a…