SketchGen: Generating Constrained CAD Sketches
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Abstract (arXiv)
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.
Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). Builds on 33 of its 42 references that are in the map. First posted on arXiv 4 Jun 2021.
Builds on
33 of 42 references.
References · 33
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21 more
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- 2021Engineering Sketch Generation for Computer-Aided Designcited by 49
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Citing papers per year: 2021: 2; 2022: 12; 2023: 10; 2024: 17; 2025: 12; 2026: 15.
68 papers.
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- 2023Hierarchical Neural Coding for Controllable CAD Model Generationcited by 84
- 2022SolidGen: An Autoregressive Model for Direct B-rep Synthesiscited by 78
- 2021Vitruvion: A Generative Model of Parametric CAD Sketchescited by 66
- 2024A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languagescited by 65
- 2021Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinderscited by 59
- 2022Reconstructing editable prismatic CAD from rounded voxel modelscited by 41
- 2023D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropoutscited by 39
- 2022ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsingcited by 38
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- 2026AIMold: An Autonomous AI-based Pipeline for Complex Mold Design1 Aug 2026
- 2026CADENA: Stepwise CAD Reverse Engineering1 Aug 2026
- 2026Autoregressive B-Rep Shape Generation with Parametric Surfaces19 Jul 2026
- 2026HierCAD: Hierarchical Text-to-CAD Design via Structure Alignment and Parameter Grounding13 Jul 2026
- 2026ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation7 Jul 2026
- 2026GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding5 Jun 2026
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