Hypernetwork approach to generating point clouds

• URL: http://arxiv.org/abs/2003.00802v2
• Date: Tue, 13 Oct 2020 19:18:59 GMT
• Title: Hypernetwork approach to generating point clouds
• Authors: Przemys{\l}aw Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zi\k{e}ba, Tomasz Trzci\'nski
• Abstract summary: We build a hyper network that returns weights of a particular neural network trained to map points into a 3D shape. A particular 3D shape can be generated using point-by-point sampling from the assumed prior distribution. Since the hyper network is based on an auto-encoder architecture trained to reconstruct realistic 3D shapes, the target network weights can be considered a parametrization of the surface of a 3D shape.
• Score: 18.67883065951206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose a novel method for generating 3D point clouds that leverage properties of hyper networks. Contrary to the existing methods that learn only the representation of a 3D object, our approach simultaneously finds a representation of the object and its 3D surface. The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape. As a consequence, a particular 3D shape can be generated using point-by-point sampling from the assumed prior distribution and transforming sampled points with the target network. Since the hyper network is based on an auto-encoder architecture trained to reconstruct realistic 3D shapes, the target network weights can be considered a parametrization of the surface of a 3D shape, and not a standard representation of point cloud usually returned by competitive approaches. The proposed architecture allows finding mesh-based representation of 3D objects in a generative manner while providing point clouds en pair in quality with the state-of-the-art methods.

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