  Most algorithms that rely on deep learning-based approaches to generate 3D
point sets can only produce clouds containing fixed number of points.
Furthermore, they typically require large networks parameterized by many
weights, which makes them hard to train. In this paper, we propose an
auto-encoder architecture that can both encode and decode clouds of arbitrary
size and demonstrate its effectiveness at upsampling sparse point clouds.
Interestingly, we can do so using less than half as many parameters as
state-of-the-art architectures while still delivering better performance. We
will make our code base fully available.
