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arXiv · 1712.08290

CSGNet: Neural Shape Parser for Constructive Solid Geometry

Gopal Sharma·Rishabh Goyal·Difan Liu·Evangelos Kalogerakis·Subhransu Maji
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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. 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.

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Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji. 2017-12-22. CSGNet: Neural Shape Parser for Constructive Solid Geometry. https://arxiv.org/abs/1712.08290

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