This repository implements PointFlow, a probabilistic framework for generating 3D point clouds by modeling shapes as distributions of distributions. The model learns a two-level hierarchy: a distribution over shapes, and a distribution of points conditioned on each shape. Both levels are learned using continuous normalizing flows, enabling exact likelihood computation and training via variational inference. The implementation reproduces the ICCV 2019 paper results on unconditional point cloud generation and reconstruction.