用45万真实模型训练大模型，让AI生成更复杂真实的CAD设计。
CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models
- 基于FeatureScript构建可执行代码表示，支持15种建模操作。
- 在文本和图像条件生成上达到当前最优，设计更准确多样。
- 适合想提升生成式CAD复杂度的研究者与工业用户。
我们提出CADFS，一个以数据为中心的框架，使大型视觉语言模型能够生成复杂的CAD设计历史。现有生成式CAD系统受限于简化的表示方式和小规模数据集，仅支持草图-拉伸等基础操作。为此，我们引入基于FeatureScript的表示方法，并构建了包含45万真实世界CAD模型的数据集，涵盖15种建模操作。通过新设计的管道重建干净、可执行的FeatureScript程序，并提供多模态标注。在该表示上微调视觉语言模型，在文本条件下的CAD生成和基于图像的重建任务中均达到当前最优表现，生成的设计更准确、更丰富、更具特征。消融实验表明，FeatureScript表示、扩展的操作集以及对齐的文本描述均显著提升性能。本框架大幅提升了生成式CAD的复杂度与真实性。CADFS框架及数据集已公开：https://voyleg.github.io/cadfs/
原文摘要 · Abstract (English)
We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD systems are restricted to sketch-extrude operations due to simplified representations and limited datasets. We address this by introducing a FeatureScript-based representation and constructing a dataset of 450k real-world CAD models spanning 15 modeling operations. We obtain the dataset via a new pipeline that reconstructs clean, executable FeatureScript programs and provides multimodal annotations. Fine-tuning a VLM on this representation yields state-of-the-art results in text-conditioned CAD generation and image-based reconstruction, producing more accurate, diverse, and feature-rich designs than prior frameworks. Ablations show that each individual component of our framework, i.e., the FeatureScript representation, the extended operation set, and representation-aligned textual descriptions, significantly improves performance. Our framework substantially broadens the complexity and realism achievable in generative CAD. The CADFS framework and the new dataset are available at https://voyleg.github.io/cadfs/.
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