research-article

Fusion 360 gallery: a dataset and environment for programmatic CAD construction from human design sequences

Authors:

Karl D. D. Willis,

Joseph G. Lambourne,

Armando Solar-Lezama,

Wojciech MatusikAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 40, Issue 4

Article No.: 54, Pages 1 - 24

https://doi.org/10.1145/3450626.3459818

Published: 19 July 2021 Publication History

Abstract

Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software. Despite the pervasiveness of parametric CAD and a growing interest from the research community, currently there does not exist a dataset of realistic CAD models in a concise programmatic form. In this paper we present the Fusion 360 Gallery, consisting of a simple language with just the sketch and extrude modeling operations, and a dataset of 8,625 human design sequences expressed in this language. We also present an interactive environment called the Fusion 360 Gym, which exposes the sequential construction of a CAD program as a Markov decision process, making it amendable to machine learning approaches. As a use case for our dataset and environment, we define the CAD reconstruction task of recovering a CAD program from a target geometry. We report results of applying state-of-the-art methods of program synthesis with neurally guided search on this task.

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Index Terms

Fusion 360 gallery: a dataset and environment for programmatic CAD construction from human design sequences
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Parametric curve and surface models

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 40, Issue 4

August 2021

2170 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3450626

Editor:
Sylvain Paris
Adobe Inc.

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Publication History

Published: 19 July 2021

Published in TOG Volume 40, Issue 4

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Zhang CPolette APinquié RCarasi GDe Charnace HPernot J(2025)eCAD-Net: Editable Parametric CAD Models Reconstruction from Dumb B-Rep Models Using Deep Neural NetworksComputer-Aided Design10.1016/j.cad.2024.103806178(103806)Online publication date: Jan-2025
https://doi.org/10.1016/j.cad.2024.103806
Mustapha K(2025)A survey of emerging applications of large language models for problems in mechanics, product design, and manufacturingAdvanced Engineering Informatics10.1016/j.aei.2024.10306664(103066)Online publication date: Mar-2025
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