| Which Training Methods for GANs do actually Converge? L Mescheder, A Geiger, S Nowozin International Conference on Machine Learning, 3478-3487, 2018 | 424 | 2018 |
| Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks L Mescheder, S Nowozin, A Geiger International Conference on Machine Learning, 2017 | 336 | 2017 |
| Occupancy networks: Learning 3d reconstruction in function space L Mescheder, M Oechsle, M Niemeyer, S Nowozin, A Geiger Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 253 | 2019 |
| The Numerics of GANs L Mescheder, S Nowozin, A Geiger Advances in Neural Information Processing Systems, 1825-1835, 2017 | 243 | 2017 |
| Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes HA Alhaija, SK Mustikovela, L Mescheder, A Geiger, C Rother International Journal of Computer Vision, 1-12, 2017 | 140 | 2017 |
| Augmented reality meets deep learning for car instance segmentation in urban scenes HA Alhaija, SK Mustikovela, L Mescheder, A Geiger, C Rother British Machine Vision Conference 1, 2, 2017 | 53 | 2017 |
| Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision M Niemeyer, L Mescheder, M Oechsle, A Geiger Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 31 | 2020 |
| Texture fields: Learning texture representations in function space M Oechsle, L Mescheder, M Niemeyer, T Strauss, A Geiger Proceedings of the IEEE International Conference on Computer Vision, 4531-4540, 2019 | 29 | 2019 |
| Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics M Niemeyer, L Mescheder, M Oechsle, A Geiger | 16 | 2019 |
| Convolutional Occupancy Networks S Peng, M Niemeyer, L Mescheder, M Pollefeys, A Geiger arXiv preprint arXiv:2003.04618, 2020 | 10 | 2020 |
| Towards unsupervised learning of generative models for 3d controllable image synthesis Y Liao, K Schwarz, L Mescheder, A Geiger Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 8 | 2020 |
| Learning Implicit Surface Light Fields M Oechsle, M Niemeyer, L Mescheder, T Strauss, A Geiger arXiv preprint arXiv:2003.12406, 2020 | 3 | 2020 |
| An Extended Perona–Malik Model Based on Probabilistic Models LM Mescheder, DA Lorenz Journal of Mathematical Imaging and Vision 60 (1), 128-144, 2018 | 2 | 2018 |
| Learning Neural Light Transport P Sanzenbacher, L Mescheder, A Geiger arXiv preprint arXiv:2006.03427, 2020 | | 2020 |
| Stability and Expressiveness of Deep Generative Models L Mescheder http://hdl.handle.net/10900/106074, 2020 | | 2020 |
| Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring L Mescheder, S Nowozin, A Geiger arXiv preprint arXiv:1611.06684, 2016 | | 2016 |
| Supplementary Material for Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis Y Liao, K Schwarz, L Mescheder, A Geiger | | |
| Augmented Reality Meets Computer Vision HA Alhaija, SK Mustikovela, L Mescheder, A Geiger, C Rother | | |
Andreas GeigerProfessor of Computer Science, University of Tübingen and MPI-ISVerified email at tue.mpg.de
