| 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 | 275 | 2013 |
| Pushing the limits of stereo using variational stereo estimation R Ranftl, S Gehrig, T Pock, H Bischof Intelligent Vehicles Symposium (IV), 2012 IEEE, 401-407, 2012 | 110 | 2012 |
| Non-local total generalized variation for optical flow estimation R Ranftl, K Bredies, T Pock European Conference on Computer Vision, 439-454, 2014 | 85 | 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 | 66 | 2014 |
| 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 | 47 | 2013 |
| Accurate optical flow via direct cost volume processing J Xu, R Ranftl, V Koltun arXiv preprint arXiv:1704.07325, 2017 | 37 | 2017 |
| Dense Monocular Depth Estimation in Complex Dynamic Scenes R Ranftl, V Vineet, Q Chen, V Koltun CVPR, 2016 | 37 | 2016 |
| Variational shape from light field S Heber, R Ranftl, T Pock International Workshop on Energy Minimization Methods in Computer Vision and …, 2013 | 37 | 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 | 27 | 2015 |
| A bi-level view of inpainting-based image compression Y Chen, R Ranftl, T Pock arXiv preprint arXiv:1401.4112, 2014 | 25 | 2014 |
| A Deep Variational Model for Image Segmentation R Ranftl, T Pock | 23 | 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 | 21 | 2013 |
| 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 | 16 | 2014 |
| 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 | 9 | 2016 |
| Insights into analysis operator learning: A view from higher-order filter-based mrf model Y Chen, R Ranftl, T Pock IEEE Trans. Image Process 23 (3), 1060-1072, 2014 | 9 | 2014 |
| Multi-modality depth map fusion using primal-dual optimization D Ferstl, R Ranftl, M Rüther, H Bischof Computational Photography (ICCP), 2013 IEEE International Conference on, 1-8, 2013 | 6 | 2013 |
| Approximate Envelope Minimization for Curvature Regularity S Heber, R Ranftl, T Pock Workshop on Higher-Order Models and Global Constraints in Computer Vision …, 2012 | 6 | 2012 |
| Depth Restoration via Joint Training of a Global Regression Model and CNNs G Riegler, R Ranftl, M Rüther, T Pock, H Bischof British Machine Vision Conference, 2015 | 5 | 2015 |
| 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 | 4 | 2018 |
| Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing E Kaufmann, M Gehrig, P Foehn, R Ranftl, A Dosovitskiy, V Koltun, ... arXiv preprint arXiv:1810.06224, 2018 | 1 | 2018 |
Thomas PockProfessor of Computer Science, TU GrazBestätigte E-Mail-Adresse bei icg.tugraz.at
