  Automatic synthesis of high quality 3D shapes is an ongoing and challenging
area of research. While several data-driven methods have been proposed that
make use of neural networks to generate 3D shapes, none of them reach the level
of quality that deep learning synthesis approaches for images provide. In this
work we present a method for a convolutional point cloud decoder/generator that
makes use of recent advances in the domain of image synthesis. Namely, we use
Adaptive Instance Normalization and offer an intuition on why it can improve
training. Furthermore, we propose extensions to the minimization of the
commonly used Chamfer distance for auto-encoding point clouds. In addition, we
show that careful sampling is important both for the input geometry and in our
point cloud generation process to improve results. The results are evaluated in
an auto-encoding setup to offer both qualitative and quantitative analysis. The
proposed decoder is validated by an extensive ablation study and is able to
outperform current state of the art results in a number of experiments. We show
the applicability of our method in the fields of point cloud upsampling, single
view reconstruction, and shape synthesis.
