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Eddy Ilg
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2020 – today
- 2024
- [j4]Raza Yunus, Jan Eric Lenssen, Michael Niemeyer, Yiyi Liao, Christian Rupprecht, Christian Theobalt, Gerard Pons-Moll, Jia-Bin Huang, Vladislav Golyanik, Eddy Ilg:
Recent Trends in 3D Reconstruction of General Non-Rigid Scenes. Comput. Graph. Forum 43(2): i-iii (2024) - [c23]Cameron Braunstein, Eddy Ilg, Vladislav Golyanik:
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids. 3DV 2024: 1340-1349 - [c22]Jonas Kälble, Sascha Wirges, Maxim Tatarchenko, Eddy Ilg:
Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory. CVPR 2024: 5281-5290 - [c21]Philipp Schröppel, Christopher Wewer, Jan Eric Lenssen, Eddy Ilg, Thomas Brox:
Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation. CVPR 2024: 8785-8794 - [c20]Devikalyan Das, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen:
Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction. CVPR 2024: 10715-10725 - [c19]Leonhard Sommer, Artur Jesslen, Eddy Ilg, Adam Kortylewski:
Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos. CVPR 2024: 22787-22796 - [c18]Tom Fischer, Yaoyao Liu
, Artur Jesslen
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning. ECCV (77) 2024: 357-374 - [c17]Christopher Wewer, Kevin Raj, Eddy Ilg
LatentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction. ECCV (87) 2024: 456-473 - [c16]Tom Fischer, Pascal Peter, Joachim Weickert, Eddy Ilg:
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields. ICML 2024 - [i29]Raza Yunus, Jan Eric Lenssen, Michael Niemeyer, Yiyi Liao, Christian Rupprecht, Christian Theobalt, Gerard Pons-Moll, Jia-Bin Huang, Vladislav Golyanik, Eddy Ilg:
Recent Trends in 3D Reconstruction of General Non-Rigid Scenes. CoRR abs/2403.15064 (2024) - [i28]Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen:
latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction. CoRR abs/2403.16292 (2024) - [i27]Jonas Kälble, Sascha Wirges, Maxim Tatarchenko, Eddy Ilg:
Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory. CoRR abs/2405.10575 (2024) - [i26]Tom Fischer, Pascal Peter, Joachim Weickert, Eddy Ilg:
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields. CoRR abs/2405.14599 (2024) - [i25]Leonhard Sommer, Artur Jesslen, Eddy Ilg, Adam Kortylewski:
Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos. CoRR abs/2407.04384 (2024) - [i24]Tom Fischer, Yaoyao Liu, Artur Jesslen, Noor Ahmed, Prakhar Kaushik, Angtian Wang, Alan L. Yuille, Adam Kortylewski, Eddy Ilg:
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning. CoRR abs/2407.09271 (2024) - [i23]Kevin Raj, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen:
Spurfies: Sparse Surface Reconstruction using Local Geometry Priors. CoRR abs/2408.16544 (2024) - 2023
- [c15]Christopher Wewer, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen:
SimNP: Learning Self-Similarity Priors Between Neural Points. ICCV 2023: 8807-8818 - [i22]Christopher Wewer, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen:
SimNP: Learning Self-Similarity Priors Between Neural Points. CoRR abs/2309.03809 (2023) - [i21]Devikalyan Das, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen:
Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction. CoRR abs/2312.01196 (2023) - [i20]Philipp Schröppel, Christopher Wewer, Jan Eric Lenssen, Eddy Ilg, Thomas Brox:
Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation. CoRR abs/2312.14124 (2023) - [i19]Cameron Braunstein, Eddy Ilg, Vladislav Golyanik:
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids. CoRR abs/2312.16118 (2023) - 2022
- [c14]Tony Ng, Hyo Jin Kim, Vincent T. Lee, Daniel DeTone, Tsun-Yi Yang, Tianwei Shen, Eddy Ilg, Vassileios Balntas, Krystian Mikolajczyk, Chris Sweeney:
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning. CVPR 2022: 12787-12797 - [c13]Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, Luc Van Gool:
Recurrent Video Restoration Transformer with Guided Deformable Attention. NeurIPS 2022 - [i18]Samir Aroudj, Steven Lovegrove, Eddy Ilg, Tanner Schmidt, Michael Goesele, Richard A. Newcombe:
ERF: Explicit Radiance Field Reconstruction From Scratch. CoRR abs/2203.00051 (2022) - [i17]Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao
Recurrent Video Restoration Transformer with Guided Deformable Attention. CoRR abs/2206.02146 (2022) - 2021
- [c12]Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim, Tianwei Shen, Meghan Cowan, Rajvi Shah, Caroline Trippel, Brandon Reagen, Timothy Sherwood, Vasileios Balntas, Armin Alaghi, Eddy Ilg:
Mitigating Reverse Engineering Attacks on Local Feature Descriptors. BMVC 2021: 106 - [i16]Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim, Tianwei Shen, Meghan Cowan, Rajvi Shah, Caroline Trippel, Brandon Reagen, Timothy Sherwood, Vasileios Balntas, Armin Alaghi, Eddy Ilg:
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors. CoRR abs/2105.03812 (2021) - [i15]Tony Ng, Hyo Jin Kim, Vincent T. Lee, Daniel DeTone, Tsun-Yi Yang, Tianwei Shen, Eddy Ilg, Vassileios Balntas, Krystian Mikolajczyk, Chris Sweeney:
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning. CoRR abs/2112.12785 (2021) - 2020
- [j3]Wenxin Liu
TLIO: Tight Learned Inertial Odometry. IEEE Robotics Autom. Lett. 5(4): 5653-5660 (2020) - [c11]Sungyong Baik, Hyo Jin Kim, Tianwei Shen, Eddy Ilg, Kyoung Mu Lee, Christopher Sweeney:
Domain Adaptation of Learned Featuresfor Visual Localization. BMVC 2020 - [c10]Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, Richard A. Newcombe:
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction. ECCV (29) 2020: 608-625 - [i14]Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, Richard A. Newcombe:
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction. CoRR abs/2003.10983 (2020) - [i13]Wenxin Liu, David Caruso, Eddy Ilg, Jing Dong, Anastasios I. Mourikis, Kostas Daniilidis, Vijay Kumar, Jakob Engel:
TLIO: Tight Learned Inertial Odometry. CoRR abs/2007.01867 (2020) - [i12]Sungyong Baik, Hyo Jin Kim, Tianwei Shen, Eddy Ilg, Kyoung Mu Lee, Chris Sweeney:
Domain Adaptation of Learned Features for Visual Localization. CoRR abs/2008.09310 (2020)
2010 – 2019
- 2019
- [b1]Eddy Ilg:
Estimating optical flow with convolutional neural networks. University of Freiburg, Freiburg im Breisgau, Germany, 2019 - [j2]Anna Khoreva
Lucid Data Dreaming for Video Object Segmentation. Int. J. Comput. Vis. 127(9): 1175-1197 (2019) - [c9]Osama Makansi, Eddy Ilg, Özgün Çiçek, Thomas Brox:
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction. CVPR 2019: 7144-7153 - [i11]Osama Makansi, Eddy Ilg, Özgün Çiçek, Thomas Brox:
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction. CoRR abs/1906.03631 (2019) - 2018
- [j1]Nikolaus Mayer
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? Int. J. Comput. Vis. 126(9): 942-960 (2018) - [c8]Eddy Ilg, Tonmoy Saikia, Margret Keuper, Thomas Brox:
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation. ECCV (12) 2018: 626-643 - [c7]Eddy Ilg, Özgün Çiçek, Silvio Galesso
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow. ECCV (7) 2018: 677-693 - [i10]Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox:
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? CoRR abs/1801.06397 (2018) - [i9]Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox:
Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks. CoRR abs/1802.07095 (2018) - [i8]Eddy Ilg, Tonmoy Saikia, Margret Keuper, Thomas Brox:
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation. CoRR abs/1808.01838 (2018) - [i7]Osama Makansi, Eddy Ilg, Thomas Brox:
FusionNet and AugmentedFlowNet: Selective Proxy Ground Truth for Training on Unlabeled Images. CoRR abs/1808.06389 (2018) - 2017
- [c6]Eddy Ilg, Nikolaus Mayer
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. CVPR 2017: 1647-1655 - [c5]Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer
DeMoN: Depth and Motion Network for Learning Monocular Stereo. CVPR 2017: 5622-5631 - [c4]Osama Makansi, Eddy Ilg, Thomas Brox:
End-to-End Learning of Video Super-Resolution with Motion Compensation. GCPR 2017: 203-214 - [i6]Anna Khoreva, Rodrigo Benenson, Eddy Ilg, Thomas Brox, Bernt Schiele:
Lucid Data Dreaming for Object Tracking. CoRR abs/1703.09554 (2017) - [i5]Osama Makansi, Eddy Ilg, Thomas Brox:
End-to-End Learning of Video Super-Resolution with Motion Compensation. CoRR abs/1707.00471 (2017) - 2016
- [c3]Nikolaus Mayer
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation. CVPR 2016: 4040-4048 - [i4]Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, Thomas Brox:
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. CoRR abs/1612.01925 (2016) - [i3]Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, Thomas Brox:
DeMoN: Depth and Motion Network for Learning Monocular Stereo. CoRR abs/1612.02401 (2016) - 2015
- [c2]Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt
FlowNet: Learning Optical Flow with Convolutional Networks. ICCV 2015: 2758-2766 - [i2]Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox:
FlowNet: Learning Optical Flow with Convolutional Networks. CoRR abs/1504.06852 (2015) - [i1]Nikolaus Mayer, Eddy Ilg, Philip Häusser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox:
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation. CoRR abs/1512.02134 (2015) - 2014
- [c1]Eddy Ilg, Rainer Kümmerle, Wolfram Burgard
Reconstruction of rigid body models from motion distorted laser range data using optical flow. ICRA 2014: 4627-4632
Coauthor Index
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last updated on 2024-12-23 20:35 CET by the dblp team
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