  3D shape abstraction has drawn great interest over the years. Apart from
low-level representations such as meshes and voxels, researchers also seek to
semantically abstract complex objects with basic geometric primitives. Recent
deep learning methods rely heavily on datasets, with limited generality to
unseen categories. Furthermore, abstracting an object accurately yet with a
small number of primitives still remains a challenge. In this paper, we propose
a novel non-parametric Bayesian statistical method to infer an abstraction,
consisting of an unknown number of geometric primitives, from a point cloud. We
model the generation of points as observations sampled from an infinite mixture
of Gaussian Superquadric Taper Models (GSTM). Our approach formulates the
abstraction as a clustering problem, in which: 1) each point is assigned to a
cluster via the Chinese Restaurant Process (CRP); 2) a primitive representation
is optimized for each cluster, and 3) a merging post-process is incorporated to
provide a concise representation. We conduct extensive experiments on two
datasets. The results indicate that our method outperforms the state-of-the-art
in terms of accuracy and is generalizable to various types of objects.
