| 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 | 3303* | 2015 |
| Flownet 2.0: Evolution of optical flow estimation with deep networks E Ilg, N Mayer, T Saikia, M Keuper, A Dosovitskiy, T Brox Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 2325 | 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 | 1786 | 2016 |
| Demon: Depth and motion network for learning monocular stereo B Ummenhofer, H Zhou, J Uhrig, N Mayer, E Ilg, A Dosovitskiy, T Brox Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 573 | 2017 |
| Lucid data dreaming for object tracking A Khoreva, R Benenson, E Ilg, T Brox, B Schiele The DAVIS challenge on video object segmentation, 2017 | 236* | 2017 |
| 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 126 (9), 942-960, 2018 | 163 | 2018 |
| Deep local shapes: Learning local sdf priors for detailed 3d reconstruction R Chabra, JE Lenssen, E Ilg, T Schmidt, J Straub, S Lovegrove, ... European Conference on Computer Vision, 608-625, 2020 | 139 | 2020 |
| 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 Proceedings of the European Conference on Computer Vision (ECCV), 614-630, 2018 | 134 | 2018 |
| Uncertainty estimates and multi-hypotheses networks for optical flow E Ilg, O Cicek, S Galesso, A Klein, O Makansi, F Hutter, T Brox Proceedings of the European Conference on Computer Vision (ECCV), 652-667, 2018 | 132* | 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/CVF Conference on Computer Vision and Pattern …, 2019 | 104 | 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 | 46 | 2017 |
| Tlio: Tight learned inertial odometry W Liu, D Caruso, E Ilg, J Dong, AI Mourikis, K Daniilidis, V Kumar, J Engel IEEE Robotics and Automation Letters 5 (4), 5653-5660, 2020 | 36 | 2020 |
| Reconstruction of rigid body models from motion distorted laser range data using optical flow E Ilg, R Ku, W Burgard, T Brox 2014 IEEE International Conference on Robotics and Automation (ICRA), 4627-4632, 2014 | 13 | 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 | 5 | 2018 |
| Mitigating Reverse Engineering Attacks on Local Feature Descriptors D Dangwal, VT Lee, HJ Kim, T Shen, M Cowan, R Shah, C Trippel, ... | 3* | 2021 |
| Domain adaptation of learned featuresfor visual localization S Baik, HJ Kim, T Shen, E Ilg, KM Lee, C Sweeney BMVC, 2020 | 3* | 2020 |
| ERF: Explicit Radiance Field Reconstruction From Scratch S Aroudj, S Lovegrove, E Ilg, T Schmidt, M Goesele, R Newcombe arXiv preprint arXiv:2203.00051, 2022 | | 2022 |
| NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning T Ng, HJ Kim, V Lee, D Detone, TY Yang, T Shen, E Ilg, V Balntas, ... arXiv preprint arXiv:2112.12785, 2021 | | 2021 |
| Estimating optical flow with convolutional neural networks E Ilg Dissertation, Universität Freiburg, 2019, 2020 | | 2020 |
