Abstract
Computer-aided design (CAD) is the most widely used modeling approach for technical design. The typical starting point in these designs is 2D sketches which can later be extruded and combined to obtain complex three-dimensional assemblies. Such sketches are typically composed of parametric primitives, such as points, lines, and circular arcs, augmented with geometric constraints linking the primitives, such as coincidence, parallelism, or orthogonality. Sketches can be represented as graphs, with the primitives as nodes and the constraints as edges. Training a model to automatically generate CAD sketches can enable several novel workflows, but is challenging due to the complexity of the graphs and the heterogeneity of the primitives and constraints. In particular, each type of primitive and constraint may require a record of different size and parameter types. We propose SketchGen as a generative model based on a transformer architecture to address the heterogeneity problem by carefully designing a sequential language for the primitives and constraints that allows distinguishing between different primitive or constraint types and their parameters, while encouraging our model to re-use information across related parameters, encoding shared structure. A particular highlight of our work is the ability to produce primitives linked via constraints that enables the final output to be further regularized via a constraint solver. We evaluate our model by demonstrating constraint prediction for given sets of primitives and full sketch generation from scratch, showing that our approach significantly out performs the state-of-the-art in CAD sketch generation.
Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021 |
Editors | Marc'Aurelio Ranzato, Alina Beygelzimer, Yann Dauphin, Percy S. Liang, Jenn Wortman Vaughan |
Publisher | Neural information processing systems foundation |
Pages | 5077-5088 |
Number of pages | 12 |
ISBN (Electronic) | 9781713845393 |
State | Published - 2021 |
Event | 35th Conference on Neural Information Processing Systems, NeurIPS 2021 - Virtual, Online Duration: Dec 6 2021 → Dec 14 2021 |
Publication series
Name | Advances in Neural Information Processing Systems |
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Volume | 7 |
ISSN (Print) | 1049-5258 |
Conference
Conference | 35th Conference on Neural Information Processing Systems, NeurIPS 2021 |
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City | Virtual, Online |
Period | 12/6/21 → 12/14/21 |
Bibliographical note
Funding Information:This work was suppported in part by ARL grant W911NF2120104, and a Vannevar Bush Faculty Fellowship. We would like to acknowledge gifts from Adobe, Autodesk and the UCL AI Centre. We thank the KAUST Supercomputing Lab (KSL) for providing compute infrastructure. Finally, we thank the anonymous reviewers for their constructive comments.
Publisher Copyright:
© 2021 Neural information processing systems foundation. All rights reserved.
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing