  As details are missing in most representations of structures, the lack of
controllability to more information is one of the major weaknesses in
structure-based controllable point cloud generation. It is observable that
definitions of details and structures are subjective. Details can be treated as
structures on small scales. To represent structures in different scales at the
same time, we present a graph-based representation of structures called the
Multiscale Structure Graph (MSG). Given structures in multiple scales, similar
patterns of local structures can be found at different scales, positions, and
angles. The knowledge learned from a regional structure pattern shall be
transferred to other similar patterns. An encoding and generation mechanism,
namely the Multiscale Structure-based Point Cloud Generator (MSPCG) is
proposed, which can simultaneously learn point cloud generation from local
patterns with miscellaneous spatial properties. The proposed method supports
multiscale editions on point clouds by editing the MSG. By generating point
clouds from local structures and learning simultaneously in multiple scales,
our MSPCG has better generalization ability and scalability. Trained on the
ShapeNet, our MSPCG can generate point clouds from a given structure for unseen
categories and indoor scenes. The experimental results show that our method
significantly outperforms baseline methods.
