SolidGen: An Autoregressive Model for Direct B-rep Synthesis
- URL: http://arxiv.org/abs/2203.13944v1
- Date: Sat, 26 Mar 2022 00:00:45 GMT
- Title: SolidGen: An Autoregressive Model for Direct B-rep Synthesis
- Authors: Pradeep Kumar Jayaraman, Joseph G. Lambourne, Nishkrit Desai, Karl
D.D. Willis, Aditya Sanghi, Nigel J.W. Morris
- Abstract summary: Boundary representation (B-rep) format is de-facto shape representation in computer-aided design (CAD)
Recent approaches to generating CAD models have focused on learning sketch-and-extrude modeling sequences.
We present a new approach that enables learning from and synthesizing B-reps without the need for supervision.
- Score: 15.599363091502365
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Boundary representation (B-rep) format is the de-facto shape
representation in computer-aided design (CAD) to model watertight solid
objects. Recent approaches to generating CAD models have focused on learning
sketch-and-extrude modeling sequences that are executed by a solid modeling
kernel in postprocess to recover a B-rep. In this paper we present a new
approach that enables learning from and synthesizing B-reps without the need
for supervision through CAD modeling sequence data. Our method SolidGen, is an
autoregressive neural network that models the B-rep directly by predicting the
vertices, edges and faces using Transformer-based and pointer neural networks.
Key to achieving this is our Indexed Boundary Representation that references
B-rep vertices, edges and faces in a well-defined hierarchy to capture the
geometric and topological relations suitable for use with machine learning.
SolidGen can be easily conditioned on contexts e.g., class labels thanks to its
probabilistic modeling of the B-rep distribution. We demonstrate qualitatively,
quantitatively and through perceptual evaluation by human subjects that
SolidGen can produce high quality, realistic looking CAD models.
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