Abstract
We present a neural architecture that takes as input a 2D or
3D shape and outputs a program that generates the shape.
The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottomup techniques for this shape parsing task rely on primitive
detection and are inherently slow since the search space
over possible primitive combinations is large. In contrast,
our model uses a recurrent neural network that parses the
input shape in a top-down manner, which is significantly
faster and yields a compact and easy-to-interpret sequence
of modeling instructions. Our model is also more effective
as a shape detector compared to existing state-of-the-art detection techniques. We finally demonstrate that our network
can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.