  Sketch-and-extrude is a common and intuitive modeling process in computer
aided design. This paper studies the problem of learning the shape given in the
form of point clouds by inverse sketch-and-extrude. We present ExtrudeNet, an
unsupervised end-to-end network for discovering sketch and extrude from point
clouds. Behind ExtrudeNet are two new technical components: 1) an effective
representation for sketch and extrude, which can model extrusion with freeform
sketches and conventional cylinder and box primitives as well; and 2) a
numerical method for computing the signed distance field which is used in the
network learning. This is the first attempt that uses machine learning to
reverse engineer the sketch-and-extrude modeling process of a shape in an
unsupervised fashion. ExtrudeNet not only outputs a compact, editable and
interpretable representation of the shape that can be seamlessly integrated
into modern CAD software, but also aligns with the standard CAD modeling
process facilitating various editing applications, which distinguishes our work
from existing shape parsing research. Code is released at
https://github.com/kimren227/ExtrudeNet.
