| Flownet: Learning optical flow with convolutional networks A Dosovitskiy, P Fischer, E Ilg, P Hausser, C Hazirbas, V Golkov, ... Proceedings of the IEEE International Conference on Computer Vision, 2758-2766, 2015 | 2142* | 2015 |
| Flownet 2.0: Evolution of optical flow estimation with deep networks E Ilg, N Mayer, T Saikia, M Keuper, A Dosovitskiy, T Brox IEEE conference on computer vision and pattern recognition (CVPR) 2, 6, 2017 | 1433 | 2017 |
| A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation N Mayer, E Ilg, P Hausser, P Fischer, D Cremers, A Dosovitskiy, T Brox Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 1064 | 2016 |
| Demon: Depth and motion network for learning monocular stereo B Ummenhofer, H Zhou, J Uhrig, N Mayer, E Ilg, A Dosovitskiy, T Brox IEEE Conference on computer vision and pattern recognition (CVPR) 5, 6, 2017 | 394 | 2017 |
| Lucid Data Dreaming for Multiple Object Tracking A Khoreva, R Benenson, E Ilg, T Brox, B Schiele arXiv preprint arXiv:1703.09554, 2017 | 151* | 2017 |
| Occlusions, motion and depth boundaries with a generic network for disparity, optical flow or scene flow estimation E Ilg, T Saikia, M Keuper, T Brox European Conference on Computer Vision (ECCV), 2018 | 91 | 2018 |
| What makes good synthetic training data for learning disparity and optical flow estimation? N Mayer, E Ilg, P Fischer, C Hazirbas, D Cremers, A Dosovitskiy, T Brox International Journal of Computer Vision, 1-19, 2018 | 91 | 2018 |
| Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow E Ilg, O Ciçek, S Galesso, A Klein, O Makansi, F Hutter, T Brox European Conference on Computer Vision (ECCV), 2018 | 66* | 2018 |
| Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction O Makansi, E Ilg, O Cicek, T Brox Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 42 | 2019 |
| End-to-end learning of video super-resolution with motion compensation O Makansi, E Ilg, T Brox German Conference on Pattern Recognition, 203-214, 2017 | 39 | 2017 |
| Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction R Chabra, JE Lenssen, E Ilg, T Schmidt, J Straub, S Lovegrove, ... arXiv preprint arXiv:2003.10983, 2020 | 13 | 2020 |
| Reconstruction of rigid body models from motion distorted laser range data using optical flow E Ilg, R Ku, W Burgard, T Brox Robotics and Automation (ICRA), 2014 IEEE International Conference on, 4627-4632, 2014 | 11 | 2014 |
| FusionNet and AugmentedFlowNet: Selective Proxy Ground Truth for Training on Unlabeled Images O Makansi, E Ilg, T Brox arXiv preprint arXiv:1808.06389, 2018 | 3 | 2018 |
| TLIO: Tight Learned Inertial Odometry W Liu, D Caruso, E Ilg, J Dong, A Mourikis, K Daniilidis, V Kumar, J Engel, ... IEEE Robotics and Automation Letters, 2020 | 2 | 2020 |
| Domain Adaptation of Learned Features for Visual Localization S Baik, HJ Kim, T Shen, E Ilg, KM Lee, C Sweeney arXiv preprint arXiv:2008.09310, 2020 | | 2020 |
