Rene Ranftl
Rene Ranftl
Intel Labs
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Cited by
Cited by
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
Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
R Ranftl, K Lasinger, D Hafner, K Schindler, V Koltun
arXiv preprint arXiv:1907.01341, 2019
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
Dense Monocular Depth Estimation in Complex Dynamic Scenes
R Ranftl, V Vineet, Q Chen, V Koltun
CVPR, 2016
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
Non-local total generalized variation for optical flow estimation
R Ranftl, K Bredies, T Pock
European Conference on Computer Vision, 439-454, 2014
What do single-view 3d reconstruction networks learn?
M Tatarchenko, SR Richter, R Ranftl, Z Li, V Koltun, T Brox
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
Events-to-video: Bringing modern computer vision to event cameras
H Rebecq, R Ranftl, V Koltun, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
High speed and high dynamic range video with an event camera
H Rebecq, R Ranftl, V Koltun, D Scaramuzza
IEEE transactions on pattern analysis and machine intelligence, 2019
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
Deep fundamental matrix estimation
R Ranftl, V Koltun
Proceedings of the European conference on computer vision (ECCV), 284-299, 2018
Deep drone racing: Learning agile flight in dynamic environments
E Kaufmann, A Loquercio, R Ranftl, A Dosovitskiy, V Koltun, ...
Conference on Robot Learning, 133-145, 2018
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
Deep drone racing: From simulation to reality with domain randomization
A Loquercio, E Kaufmann, R Ranftl, A Dosovitskiy, V Koltun, ...
IEEE Transactions on Robotics 36 (1), 1-14, 2019
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
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
Variational shape from light field
S Heber, R Ranftl, T Pock
International Workshop on Energy Minimization Methods in Computer Vision and …, 2013
Where should i walk? predicting terrain properties from images via self-supervised learning
L Wellhausen, A Dosovitskiy, R Ranftl, K Walas, C Cadena, M Hutter
IEEE Robotics and Automation Letters 4 (2), 1509-1516, 2019
Feedback mpc for torque-controlled legged robots
R Grandia, F Farshidian, R Ranftl, M Hutter
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
A bi-level view of inpainting-based image compression
Y Chen, R Ranftl, T Pock
arXiv preprint arXiv:1401.4112, 2014
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