CSGNet: Neural Shape Parser for Constructive Solid Geometry
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CSGNet: Neural Shape Parser for Constructive Solid Geometry
We present a neural architecture that takes as input a 2D or 3D shape and induces a program to generate it. The in structions 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 task that rely on primitive detection are inherently slow since the search space over possible primitive combi nations is large. In contrast, our model uses a recurrent neural network conditioned on the input shape to produce a sequence of instructions in a topdown manner and is sig nificantly faster. It is also more effective as a shape detec tor than existing stateoftheart detection techniques. We also demonstrate that our network can be trained on novel datasets without groundtruth program annotations through policy gradient techniques.
CSGNet: Neural Shape Parser for Constructive Solid Geometry
by Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji
https://arxiv.org/pdf/1712.08290v1.pdf
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