  Autoencoders allow to reconstruct a given input from a small set of
parameters. However, the input size is often limited due to computational
costs. We therefore propose a clustering and reassembling method for volumetric
point clouds, in order to allow high resolution data as input. We furthermore
present an autoencoder based on the well-known FoldingNet for volumetric point
clouds and discuss how our approach can be utilized for blending between high
resolution point clouds as well as for transferring a volumetric design/style
onto a pointcloud while maintaining its shape.
