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.
Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation
School of Computer Science, Wuhan University, Wuhan 430000, Hubei, China（武汉大学计算机科学学院）；Spatial Design Intelligence Lab, BitInf Ltd., Shanghai 200003, China（BitInf Ltd.空间设计智能实验室）；College of Computer and Information Engineering, Nanjing Tech University, Nanjing 211800, Jiangsu, China（南京理工大学计算机与信息工程学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China（中国科学院自动化研究所多模态人工智能系统国家重点实验室）
MamTiff-CAD: Multi-Scale Latent Diffusion with Mamba+ for Complex Parametric Sequence
Northwestern Polytechnical University（西北工业大学）；National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）；Nanchang University（南昌大学）
Arko-T: A Foundation Model for Text-to-Structured 3D Generation
Spatial Design Intelligence Lab, BitInf Ltd.（BitInf有限公司空间设计智能实验室）；School of Computer Science, Wuhan University（武汉大学计算机学院）；College of Computer and Information Engineering, Nanjing Tech University（南京工业大学计算机与信息工程学院）
