SketchGen: Generating Constrained CAD Sketches
Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero, Tom Kelly, Niloy J. Mitra, Leonidas J. Guibas, Peter Wonka
摘要
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. Preprint. Under review.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper27
- Text2CAD: Generating Sequential CAD Designs from Beginner-to-Expert Level Text PromptsMohammad Sadil Khan, Sankalp Sinha, Talha Uddin Sheikh, Didier Stricker 等NeurIPS 2024 · 被引用 148 次
- ComplexGen: CAD reconstruction by B-rep chain complex generationHaoxiang Guo, Shilin Liu, Hao Pan, Yang Liu 等SIGGRAPH 2022 · 被引用 106 次
- Free2CAD: parsing freehand drawings into CAD commandsChangjian Li, Hao Pan, Adrien Bousseau, Niloy J. MitraSIGGRAPH 2022 · 被引用 100 次
- Vitruvion: A Generative Model of Parametric CAD SketchesAri Seff, Wenda Zhou, Nick Richardson, Ryan P. AdamsICLR 2022 · 被引用 86 次
- D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and DropoutsFenggen Yu, Qimin Chen, Maham Tanveer, Ali Mahdavi-Amiri 等NeurIPS 2023 · 被引用 61 次
它引用的顶会 Paper13
- PolyGen: An Autoregressive Generative Model of 3D MeshesCharlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. BattagliaICML 2020 · 被引用 339 次
- DeepCAD: A Deep Generative Network for Computer-Aided Design ModelsRundi Wu, Chang Xiao, Changxi ZhengICCV 2021 · 被引用 290 次
- DeepSVG: A Hierarchical Generative Network for Vector Graphics AnimationAlexandre Carlier, Martin Danelljan, Alexandre Alahi, Radu TimofteNeurIPS 2020 · 被引用 247 次
- Fusion 360 gallery: a dataset and environment for programmatic CAD construction from human design sequencesKarl D. D. Willis, Yewen Pu, Jieliang Luo, Hang Chu 等SIGGRAPH 2021 · 被引用 197 次
- A Learned Representation for Scalable Vector GraphicsRaphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon ShlensICCV 2019 · 被引用 153 次
相关 Paper
- UniSketch: A Unified Framework for Parametric Sketch Generation and Constraint PredictionJing Lin, Fazhi He, Rubin FanAAAI 2026
- SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled CodebooksXiang Xu, Karl D. D. Willis, Joseph G. Lambourne, Chin-Yi Cheng 等ICML 2022 · 被引用 126 次
- Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector DrawingsFeiwei Qin, Shichao Lu, Junhao Hou, Changmiao Wang 等ACM MM 2025 · 被引用 4 次
- Aligning Constraint Generation with Design Intent in Parametric CADEvan Casey, Tianyu Zhang, Shu Ishida, John Roger Thompson 等ICCV 2025 · 被引用 4 次
- Computer-Aided Design as LanguageYaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller 等NeurIPS 2021 · 被引用 129 次
