  Point clouds are rich geometric data structures, where their three
dimensional structure offers an excellent domain for understanding the
representation learning and generative modeling in 3D space. In this work, we
aim to improve the performance of point cloud latent-space generative models by
experimenting with transformer encoders, latent-space flow models, and
autoregressive decoders. We analyze and compare both generation and
reconstruction performance of these models on various object types.
