N
Nikolaus Mayer
Researcher at University of Freiburg
Publications - 12
Citations - 7461
Nikolaus Mayer is an academic researcher from University of Freiburg. The author has contributed to research in topics: Optical flow & Supervised learning. The author has an hindex of 8, co-authored 11 publications receiving 5433 citations.
Papers
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Proceedings ArticleDOI
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
TL;DR: The concept of end-to-end learning of optical flow is advanced and it work really well, and faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet are presented.
Proceedings ArticleDOI
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Nikolaus Mayer,Eddy Ilg,Philip Häusser,Philipp Fischer,Daniel Cremers,Alexey Dosovitskiy,Thomas Brox +6 more
TL;DR: In this article, a large-scale synthetic stereo video dataset is proposed to enable training and evaluation of optical flow estimation with a convolutional network and disparity estimation with CNNs.
Proceedings ArticleDOI
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Nikolaus Mayer,Eddy Ilg,Philip Häusser,Philipp Fischer,Daniel Cremers,Alexey Dosovitskiy,Thomas Brox +6 more
TL;DR: This paper proposes three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks and presents a convolutional network for real-time disparity estimation that provides state-of-the-art results.
Posted Content
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
TL;DR: FlowNet 2.0 as discussed by the authors proposes an end-to-end learning framework for optical flow estimation, which is only marginally slower than the original FlowNet but decreases the estimation error by more than 50%.
Proceedings ArticleDOI
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Benjamin Ummenhofer,Huizhong Zhou,Jonas Uhrig,Nikolaus Mayer,Eddy Ilg,Alexey Dosovitskiy,Thomas Brox +6 more
TL;DR: DeMoN as mentioned in this paper proposes an end-to-end architecture composed of multiple stacked encoder-decoder networks, the core part being an iterative network that is able to improve its own predictions.