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.
Sharma et al. REF present CSG-Net, which is a neural architecture to parse a 2D or 3D input into a collection of modeling primitives with operations.
CSGNet: Neural Shape Parser for Constructive Solid Geometry
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science