  In this paper we propose a novel point cloud generator that is able to
reconstruct and generate 3D point clouds composed of semantic parts. Given a
latent representation of the target 3D model, the generation starts from a
single point and gets expanded recursively to produce the high-resolution point
cloud via a sequence of point expansion stages. During the recursive procedure
of generation, we not only obtain the coarse-to-fine point clouds for the
target 3D model from every expansion stage, but also unsupervisedly discover
the semantic segmentation of the target model according to the
hierarchical/parent-child relation between the points across expansion stages.
Moreover, the expansion modules and other elements used in our recursive
generator are mostly sharing weights thus making the overall framework light
and efficient. Extensive experiments are conducted to demonstrate that our
proposed point cloud generator has comparable or even superior performance on
both generation and reconstruction tasks in comparison to various baselines, as
well as provides the consistent co-segmentation among 3D instances of the same
object class.
