Lars Mescheder
Lars Mescheder
Max Planck Institute for Intelligent System, Tübingen
Verified email at tue.mpg.de - Homepage
Title
Cited by
Cited by
Year
Which Training Methods for GANs do actually Converge?
L Mescheder, A Geiger, S Nowozin
International Conference on Machine Learning, 3478-3487, 2018
6032018
Occupancy networks: Learning 3d reconstruction in function space
L Mescheder, M Oechsle, M Niemeyer, S Nowozin, A Geiger
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
4912019
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
L Mescheder, S Nowozin, A Geiger
International Conference on Machine Learning, 2017
3972017
The Numerics of GANs
L Mescheder, S Nowozin, A Geiger
Advances in Neural Information Processing Systems, 1825-1835, 2017
3142017
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
1902017
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
1262020
Convolutional occupancy networks
S Peng, M Niemeyer, L Mescheder, M Pollefeys, A Geiger
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
912020
Texture fields: Learning texture representations in function space
M Oechsle, L Mescheder, M Niemeyer, T Strauss, A Geiger
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
752019
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
612017
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
M Niemeyer, L Mescheder, M Oechsle, A Geiger
602019
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
232020
Learning implicit surface light fields
M Oechsle, M Niemeyer, C Reiser, L Mescheder, T Strauss, A Geiger
2020 International Conference on 3D Vision (3DV), 452-462, 2020
162020
Learning Neural Light Transport
P Sanzenbacher, L Mescheder, A Geiger
arXiv preprint arXiv:2006.03427, 2020
22020
An Extended Perona–Malik Model Based on Probabilistic Models
LM Mescheder, DA Lorenz
Journal of Mathematical Imaging and Vision 60 (1), 128-144, 2018
22018
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
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Articles 1–18