Ilg, E.
* 2015: FlowNet: Learning Optical Flow with Convolutional Networks
* 2016: Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation, A
* 2017: DeMoN: Depth and Motion Network for Learning Monocular Stereo
* 2017: End-to-End Learning of Video Super-Resolution with Motion Compensation
* 2017: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
* 2018: Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
* 2018: Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow
* 2018: What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
* 2019: Lucid Data Dreaming for Video Object Segmentation
* 2019: Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
* 2020: Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
* 2022: NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
* 2023: SimNP: Learning Self-Similarity Priors Between Neural Points
Includes: Ilg, E. Ilg, E.[Eddy]
13 for Ilg, E.
Ilg, M.
* 1986: Consistent Operations on a Spatial Data Structure
* 1990: Gripping Information for a Robot from Silhouettes
* 1990: Knowledge-Based Understanding of Road Maps and Other Line Images
* 1991: Gripping Information for a Robot from Silhouettes
* 1992: application of Voronoi skeletons to perceptual grouping in line images, The
* 1992: Voronoi Skeletons: Theory and Applications
Ilg, W.[Winfried]
* 2002: Modeling of Movement Sequences Based on Hierarchical Spatial-Temporal Correspondence of Movement Primitives
* 2003: Estimation of Skill Levels in Sports Based on Hierarchical Spatio-Temporal Correspondences