mlsanityData to 7 Oct 2026

Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences

arXiv 2010.02392 ↗First posted 5 Oct 2020cs.LG cs.CV cs.GRcited by 36

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Abstract (arXiv)

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.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2020.

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Citing papers per year: 2021: 3; 2022: 12; 2023: 7; 2024: 5; 2025: 6; 2026: 3.

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  1. 2022SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebookscited by 121
  2. 2021Deep Generative Models in Engineering Design: A Reviewcited by 103
  3. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 101
  4. 2023Hierarchical Neural Coding for Controllable CAD Model Generationcited by 84
  5. 2022SolidGen: An Autoregressive Model for Direct B-rep Synthesiscited by 78
  6. 2021JoinABLe: Learning Bottom-up Assembly of Parametric CAD Jointscited by 77
  7. 2021SketchGen: Generating Constrained CAD Sketchescited by 68
  8. 2023SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operationscited by 46
  9. 2022Reconstructing editable prismatic CAD from rounded voxel modelscited by 41
  10. 20223DALL-E: Integrating Text-to-Image AI in 3D Design Workflowscited by 41
  11. 2024Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorizationcited by 34
  12. 2022Learning to Generate 3D Shapes from a Single Examplecited by 31
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  1. 2022CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representationscited by 30
  2. 2024Make-A-Shape: a Ten-Million-scale 3D Shape Modelcited by 25
  3. 2025DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometrycited by 20
  4. 2023SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselinescited by 19
  5. 2022Neural Face Identification in a 2D Wireframe Projection of a Manifold Objectcited by 18
  6. 2022ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generationcited by 17
  7. 2025Large Language Models for Computer-Aided Design: A Surveycited by 16
  8. 2025AutoBrep: Autoregressive B-Rep Generation with Unified Topology and Geometrycited by 16
  9. 2022Translating a Visual LEGO Manual to a Machine-Executable Plancited by 15
  10. 2024NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvaturecited by 12
  11. 2023PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programscited by 8
  12. 2024DiffCSG: Differentiable CSG via Rasterizationcited by 6
  13. 2025ArcPro: Architectural Programs for Structured 3D Abstraction of Sparse Pointscited by 6
  14. 2025ShapeLib: Designing a library of programmatic 3D shape abstractions with Large Language Modelscited by 5
  15. 2023Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaulcited by 4
  16. 2022Material Prediction for Design Automation Using Graph Representation Learningcited by 3
  17. 2023A Survey of Methods for Converting Unstructured Data to CSG Modelscited by 3
  18. 2026Deep Sketch-Based 3D Modeling: A Surveycited by 3
  19. 2022Mates2Motion: Learning How Mechanical CAD Assemblies Workcited by 2
  20. 2025Target-Guided Bayesian Flow Networks for Quantitatively Constrained CAD Generationcited by 2
  21. 2026DualBrep: A Dual-Field Continuous Representation for B-rep Modellingcited by 2
  22. 2022AutoPoly: Predicting a Polygonal Mesh Construction Sequence from a Silhouette Imagecited by 0
  23. 2023A Meta-Generation framework for Industrial System Generationcited by 0
  24. 2026Agent-Aided Design for Dynamic CAD Modelscited by 0