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2019

Conditional Invertible Flow for Point Cloud Generation

Stypułkowski, Michał, Zamorski, Maciej, Zięba, Maciej et al.

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This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.

The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a cloud-specific neural network.
To capture the similarity between point clouds we rely on parameter sharing among networks, with each cloud having only a small embedding vector that defines it.
We use invertible flows networks to generate the individual point clouds, and to regularize the embedding vectors.

Conditional Invertible Flow for Point Cloud Generation · Around

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