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Eddy Ilg
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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
3785*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
27092017
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
20882016
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
6422017
Lucid data dreaming for object tracking
A Khoreva, R Benenson, E Ilg, T Brox, B Schiele
The DAVIS challenge on video object segmentation, 2017
267*2017
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
2092020
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
1862018
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
1622018
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
1612018
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
1252019
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
692020
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
502017
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
132014
Domain adaptation of learned featuresfor visual localization
S Baik, HJ Kim, T Shen, E Ilg, KM Lee, C Sweeney
BMVC, 2020
5*2020
Fusionnet and augmentedflownet: Selective proxy ground truth for training on unlabeled images
O Makansi, E Ilg, T Brox
arXiv preprint arXiv:1808.06389, 2018
52018
Recurrent Video Restoration Transformer with Guided Deformable Attention
J Liang, Y Fan, X Xiang, R Ranjan, E Ilg, S Green, J Cao, K Zhang, ...
arXiv preprint arXiv:2206.02146, 2022
42022
Mitigating Reverse Engineering Attacks on Local Feature Descriptors
D Dangwal, VT Lee, HJ Kim, T Shen, M Cowan, R Shah, C Trippel, ...
3*2021
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
T Ng, HJ Kim, VT Lee, D DeTone, TY Yang, T Shen, E Ilg, V Balntas, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern†…, 2022
12022
Mixture distribution estimation for future prediction
T Brox, O Makansi, ÷ Cicek, ILG Eddy
US Patent App. 17/616,179, 2022
2022
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
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