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Daniel Cremers
Researcher at Technische Universität München
Publications - 702
Citations - 55592
Daniel Cremers is an academic researcher from Technische Universität München. The author has contributed to research in topics: Image segmentation & Computer science. The author has an hindex of 99, co-authored 655 publications receiving 44957 citations. Previous affiliations of Daniel Cremers include Siemens & University of Mannheim.
Papers
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Proceedings ArticleDOI
FlowNet: Learning Optical Flow with Convolutional Networks
Alexey Dosovitskiy,Philipp Fischery,Eddy Ilg,Philip Häusser,Caner Hazirbas,Vladimir Golkov,Patrick van der Smagt,Daniel Cremers,Thomas Brox +8 more
TL;DR: In this paper, the authors propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations, and show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI.
Book ChapterDOI
LSD-SLAM: Large-Scale Direct Monocular SLAM
TL;DR: A novel direct tracking method which operates on \(\mathfrak{sim}(3)\), thereby explicitly detecting scale-drift, and an elegant probabilistic solution to include the effect of noisy depth values into tracking are introduced.
Proceedings ArticleDOI
A benchmark for the evaluation of RGB-D SLAM systems
TL;DR: A large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system is recorded for the evaluation of RGB-D SLAM systems.
Journal ArticleDOI
Direct Sparse Odometry
TL;DR: Direct Sparse Odometry (DSO) as mentioned in this paper combines a fully direct probabilistic model with consistent, joint optimization of all model parameters, including geometry represented as inverse depth in a reference frame and camera motion.
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.