Corpus ID: 53718111

Adversarial Autoencoders for Generating 3D Point Clouds

@article{Zamorski2018AdversarialAF,
  title={Adversarial Autoencoders for Generating 3D Point Clouds},
  author={M. Zamorski and M. Zieba and R. Nowak and Wojciech Stokowiec and T. Trzciński},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.07605}
}
  • M. Zamorski, M. Zieba, +2 authors T. Trzciński
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • Deep generative architectures provide a way to model not only images, but also complex, 3-dimensional objects, such as point clouds. [...] Key Method To achieve this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D input and create 3D output.Expand Abstract
    11 Citations
    PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows
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    NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler
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    Learning geometry-image representation for 3D point cloud generation
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    Learning Gradient Fields for Shape Generation
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    Deep Learning on Point Clouds and Its Application: A Survey
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    DualSDF: Semantic Shape Manipulation Using a Two-Level Representation
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    Generative Adversarial Networks: recent developments
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    • PDF
    Energy-Based Processes for Exchangeable Data
    • 1
    • PDF

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