  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.
