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. Bottom-up 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.
@article{arxiv.1712.08290,
title = {CSGNet: Neural Shape Parser for Constructive Solid Geometry},
author = {Gopal Sharma and Rishabh Goyal and Difan Liu and Evangelos Kalogerakis and Subhransu Maji},
journal= {arXiv preprint arXiv:1712.08290},
year = {2018}
}