Rene Ranftl
Rene Ranftl
Intel Labs
Bestätigte E-Mail-Adresse bei intel.com
TitelZitiert vonJahr
Image guided depth upsampling using anisotropic total generalized variation
D Ferstl, C Reinbacher, R Ranftl, M Rüther, H Bischof
Proceedings of the IEEE International Conference on Computer Vision, 993-1000, 2013
3492013
Pushing the limits of stereo using variational stereo estimation
R Ranftl, S Gehrig, T Pock, H Bischof
2012 IEEE Intelligent Vehicles Symposium, 401-407, 2012
1212012
Non-local total generalized variation for optical flow estimation
R Ranftl, K Bredies, T Pock
European Conference on Computer Vision, 439-454, 2014
1072014
Dense Monocular Depth Estimation in Complex Dynamic Scenes
R Ranftl, V Vineet, Q Chen, V Koltun
CVPR, 2016
862016
Insights into analysis operator learning: From patch-based sparse models to higher order MRFs
Y Chen, R Ranftl, T Pock
IEEE Transactions on Image Processing 23 (3), 1060-1072, 2014
832014
Accurate optical flow via direct cost volume processing
J Xu, R Ranftl, V Koltun
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
822017
Revisiting loss-specific training of filter-based MRFs for image restoration
Y Chen, T Pock, R Ranftl, H Bischof
German Conference on Pattern Recognition, 271-281, 2013
572013
Variational shape from light field
S Heber, R Ranftl, T Pock
International Workshop on Energy Minimization Methods in Computer Vision and …, 2013
412013
Bilevel optimization with nonsmooth lower level problems
P Ochs, R Ranftl, T Brox, T Pock
International Conference on Scale Space and Variational Methods in Computer …, 2015
342015
Deep drone racing: Learning agile flight in dynamic environments
E Kaufmann, A Loquercio, R Ranftl, A Dosovitskiy, V Koltun, ...
arXiv preprint arXiv:1806.08548, 2018
302018
Deep fundamental matrix estimation
R Ranftl, V Koltun
Proceedings of the European Conference on Computer Vision (ECCV), 284-299, 2018
292018
A bi-level view of inpainting-based image compression
Y Chen, R Ranftl, T Pock
arXiv preprint arXiv:1401.4112, 2014
292014
A Deep Variational Model for Image Segmentation
R Ranftl, T Pock
282014
Minimizing TGV-based variational models with non-convex data terms
R Ranftl, T Pock, H Bischof
International Conference on Scale Space and Variational Methods in Computer …, 2013
262013
A higher-order MRF based variational model for multiplicative noise reduction
Y Chen, W Feng, R Ranftl, H Qiao, T Pock
IEEE signal processing letters 21 (11), 1370-1374, 2014
212014
Techniques for gradient-based bilevel optimization with non-smooth lower level problems
P Ochs, R Ranftl, T Brox, T Pock
Journal of Mathematical Imaging and Vision 56 (2), 175-194, 2016
152016
Beauty and the beast: Optimal methods meet learning for drone racing
E Kaufmann, M Gehrig, P Foehn, R Ranftl, A Dosovitskiy, V Koltun, ...
2019 International Conference on Robotics and Automation (ICRA), 690-696, 2019
142019
What Do Single-view 3D Reconstruction Networks Learn?
M Tatarchenko, SR Richter, R Ranftl, Z Li, V Koltun, T Brox
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
142019
Events-to-video: Bringing modern computer vision to event cameras
H Rebecq, R Ranftl, V Koltun, D Scaramuzza
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
122019
Trajectory optimization with implicit hard contacts
J Carius, R Ranftl, V Koltun, M Hutter
IEEE Robotics and Automation Letters 3 (4), 3316-3323, 2018
92018
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