  We proposed a novel graph convolutional neural network that could construct a
coarse, sparse latent point cloud from a dense, raw point cloud. With a novel
non-isotropic convolution operation defined on irregular geometries, the model
then can reconstruct the original point cloud from this latent cloud with fine
details. Furthermore, we proposed that it is even possible to perform particle
simulation using the latent cloud encoded from some simulated particle cloud
(e.g. fluids), to accelerate the particle simulation process. Our model has
been tested on ShapeNetCore dataset for Auto-Encoding with a limited latent
dimension and tested on a synthesis dataset for fluids simulation. We also
compare the model with other state-of-the-art models, and several
visualizations were done to intuitively understand the model.
