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
5342018
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
4122019
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
L Mescheder, S Nowozin, A Geiger
International Conference on Machine Learning, 2017
3642017
The Numerics of GANs
L Mescheder, S Nowozin, A Geiger
Advances in Neural Information Processing Systems, 1825-1835, 2017
2782017
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
1822017
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
1012020
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
622019
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
572017
Convolutional occupancy networks
S Peng, M Niemeyer, L Mescheder, M Pollefeys, A Geiger
arXiv preprint arXiv:2003.04618 2, 2020
562020
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
M Niemeyer, L Mescheder, M Oechsle, A Geiger
422019
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
152020
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
152020
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
Learning Neural Light Transport
P Sanzenbacher, L Mescheder, A Geiger
arXiv preprint arXiv:2006.03427, 2020
12020
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