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We present a neural architecture that takes as input a 2D or 3D shape andoutputs a program that generates the shape. The instructions in our program arebased on constructive solid geometry principles, i.e., a set of booleanoperations on shape primitives defined recursively. Bottom-up techniques forthis shape parsing task rely on primitive detection and are inherently slowsince the search space over possible primitive combinations is large. Incontrast, our model uses a recurrent neural network that parses the input shapein a top-down manner, which is significantly faster and yields a compact andeasy-to-interpret sequence of modeling instructions. Our model is also moreeffective as a shape detector compared to existing state-of-the-art detectiontechniques. We finally demonstrate that our network can be trained on noveldatasets without ground-truth program annotations through policy gradienttechniques.

CSGNet: Neural Shape Parser for Constructive Solid Geometry