Corpus ID: 219401636

Learning Neural Light Transport

@article{Sanzenbacher2020LearningNL,
  title={Learning Neural Light Transport},
  author={Paul Sanzenbacher and Lars M. Mescheder and Andreas Geiger},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.03427}
}
In recent years, deep generative models have gained significance due to their ability to synthesize natural-looking images with applications ranging from virtual reality to data augmentation for training computer vision models. While existing models are able to faithfully learn the image distribution of the training set, they often lack controllability as they operate in 2D pixel space and do not model the physical image formation process. In this work, we investigate the importance of 3D… Expand
Neural scene graph rendering
We present a neural scene graph---a modular and controllable representation of scenes with elements that are learned from data. We focus on the forward rendering problem, where the scene graph isExpand

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