| Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer R Ranftl, K Lasinger, D Hafner, K Schindler, V Koltun IEEE transactions on pattern analysis and machine intelligence 44 (3), 1623-1637, 2020 | 694* | 2020 |
| Vision transformers for dense prediction R Ranftl, A Bochkovskiy, V Koltun Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 662 | 2021 |
| 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 | 559 | 2013 |
| 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 43 (6), 1964-1980, 2019 | 308 | 2019 |
| 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 | 306 | 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 | 259 | 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 | 217 | 2017 |
| Dense Monocular Depth Estimation in Complex Dynamic Scenes R Ranftl, V Vineet, Q Chen, V Koltun CVPR, 2016 | 193 | 2016 |
| Deep fundamental matrix estimation R Ranftl, V Koltun Proceedings of the European conference on computer vision (ECCV), 284-299, 2018 | 187 | 2018 |
| 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 | 170 | 2019 |
| Non-local total generalized variation for optical flow estimation R Ranftl, K Bredies, T Pock Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 157 | 2014 |
| 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 | 148 | 2012 |
| 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 | 146 | 2018 |
| Language-driven semantic segmentation B Li, KQ Weinberger, S Belongie, V Koltun, R Ranftl arXiv preprint arXiv:2201.03546, 2022 | 133 | 2022 |
| 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 | 124 | 2019 |
| 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 | 118 | 2019 |
| Deep drone acrobatics E Kaufmann, A Loquercio, R Ranftl, M Müller, V Koltun, D Scaramuzza arXiv preprint arXiv:2006.05768, 2020 | 117 | 2020 |
| 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 | 113 | 2014 |
| 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 | 101 | 2019 |
| Learning high-speed flight in the wild A Loquercio, E Kaufmann, R Ranftl, M Müller, V Koltun, D Scaramuzza Science Robotics 6 (59), eabg5810, 2021 | 98 | 2021 |
Vladlen KoltunDistinguished Scientist, AppleVerified email at apple.com
