| 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 | 423 | 2013 |
| 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 | 138 | 2012 |
| 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 | 125 | 2017 |
| Dense Monocular Depth Estimation in Complex Dynamic Scenes R Ranftl, V Vineet, Q Chen, V Koltun CVPR, 2016 | 125 | 2016 |
| Non-local total generalized variation for optical flow estimation R Ranftl, K Bredies, T Pock European Conference on Computer Vision, 439-454, 2014 | 119 | 2014 |
| 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 | 97 | 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 Conference on Computer Vision and Pattern …, 2019 | 77 | 2019 |
| 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 | 68 | 2018 |
| 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 | 65 | 2019 |
| Deep fundamental matrix estimation R Ranftl, V Koltun Proceedings of the European Conference on Computer Vision (ECCV), 284-299, 2018 | 63 | 2018 |
| 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 | 63 | 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, arXiv: 1907.01341, 2019 | 62* | 2019 |
| 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 | 47 | 2019 |
| Variational shape from light field S Heber, R Ranftl, T Pock International Workshop on Energy Minimization Methods in Computer Vision and …, 2013 | 45 | 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 | 43 | 2015 |
| 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 | 39 | 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 | 36 | 2019 |
| A bi-level view of inpainting-based image compression Y Chen, R Ranftl, T Pock arXiv preprint arXiv:1401.4112, 2014 | 33 | 2014 |
| A Deep Variational Model for Image Segmentation R Ranftl, T Pock | 30 | 2014 |
| 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 | 30 | 2013 |
Vladlen KoltunChief Scientist for Intelligent Systems, IntelVerified email at intel.com
