VITRUVION: A GENERATIVE MODEL OF PARAMETRIC CAD SKETCHES

Ari Seff; Wenda Zhou; Nick Richardson; Ryan P. Adams

VITRUVION: A GENERATIVE MODEL OF PARAMETRIC CAD SKETCHES

Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams

Research output: Contribution to conference › Paper › peer-review

Abstract

Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the design process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow.

Original language	American English
State	Published - 2022
Event	10th International Conference on Learning Representations, ICLR 2022 - Virtual, Online Duration: Apr 25 2022 → Apr 29 2022

Conference

Conference	10th International Conference on Learning Representations, ICLR 2022
City	Virtual, Online
Period	4/25/22 → 4/29/22

ASJC Scopus subject areas

Language and Linguistics
Computer Science Applications
Education
Linguistics and Language

Cite this

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title = "VITRUVION: A GENERATIVE MODEL OF PARAMETRIC CAD SKETCHES",

abstract = "Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the design process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow.",

author = "Ari Seff and Wenda Zhou and Nick Richardson and Adams, {Ryan P.}",

note = "Funding Information: ACKNOWLEDGMENTS The authors would like to thank Yaniv Ovadia and Jeffrey Cheng for early discussions of this project. Thanks to all members of the Princeton Laboratory for Intelligent Probabilistic Systems for providing valuable feedback. Additionally, we would like to thank Onshape for the API access as well as the many Onshape users who created the CAD sketches comprising the SketchGraphs training data. This work was partially funded by the DataX Program at Princeton University through support from the Schmidt Futures Foundation and by the NSF Institute for Data-Driven Dynamical Design (NSF 2118201). Publisher Copyright: {\textcopyright} 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.; 10th International Conference on Learning Representations, ICLR 2022 ; Conference date: 25-04-2022 Through 29-04-2022",

year = "2022",

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N1 - Funding Information: ACKNOWLEDGMENTS The authors would like to thank Yaniv Ovadia and Jeffrey Cheng for early discussions of this project. Thanks to all members of the Princeton Laboratory for Intelligent Probabilistic Systems for providing valuable feedback. Additionally, we would like to thank Onshape for the API access as well as the many Onshape users who created the CAD sketches comprising the SketchGraphs training data. This work was partially funded by the DataX Program at Princeton University through support from the Schmidt Futures Foundation and by the NSF Institute for Data-Driven Dynamical Design (NSF 2118201). Publisher Copyright: © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

PY - 2022

Y1 - 2022

N2 - Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the design process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow.

AB - Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the design process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow.

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M3 - Paper

Y2 - 25 April 2022 through 29 April 2022

ER -

VITRUVION: A GENERATIVE MODEL OF PARAMETRIC CAD SKETCHES

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