Unsupervised Learning of 3D Structure from Images

@inproceedings{Rezende2016UnsupervisedLO,
  title={Unsupervised Learning of 3D Structure from Images},
  author={Danilo Jimenez Rezende and S. M. Ali Eslami and Shakir Mohamed and Peter W. Battaglia and Max Jaderberg and Nicolas Heess},
  booktitle={NIPS},
  year={2016}
}
A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end… CONTINUE READING
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ShapeNet: An Information-Rich 3D Model Repository

ArXiv • 2015
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3D ShapeNets: A deep representation for volumetric shapes

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2015
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