  Generating an interpretable and compact representation of 3D shapes from
point clouds is an important and challenging problem. This paper presents
CSG-Stump Net, an unsupervised end-to-end network for learning shapes from
point clouds and discovering the underlying constituent modeling primitives and
operations as well. At the core is a three-level structure called {\em
CSG-Stump}, consisting of a complement layer at the bottom, an intersection
layer in the middle, and a union layer at the top. CSG-Stump is proven to be
equivalent to CSG in terms of representation, therefore inheriting the
interpretable, compact and editable nature of CSG while freeing from CSG's
complex tree structures. Particularly, the CSG-Stump has a simple and regular
structure, allowing neural networks to give outputs of a constant
dimensionality, which makes itself deep-learning friendly. Due to these
characteristics of CSG-Stump, CSG-Stump Net achieves superior results compared
to previous CSG-based methods and generates much more appealing shapes, as
confirmed by extensive experiments. Project page:
https://kimren227.github.io/projects/CSGStump/

Computer Vision and Pattern Recognition;Graphics;Machine Learning