用自然语言生成可编辑的3D零件模型，新手也能快速设计。
Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts
- 用Transformer模型从文本直接生成参数化3D模型
- 在17万模型数据集上实现高精度几何还原和参数匹配
- 适合工业设计、教育和自动化建模场景
现代CAD软件中创建复杂设计模型耗时费力，主要因为缺乏能快速生成中间简化部件的智能系统。本文提出Text2CAD，首个基于自然语言指令生成参数化CAD模型的AI框架，支持从初学者到专家的各类设计需求。我们构建了基于Mistral和LLaVA-NeXT的数据标注流程，为DeepCAD数据集生成约66万条文本注释，涵盖从抽象描述（如“生成两个同心圆柱”）到详细规格（如“以坐标(x,y)为中心画两个半径分别为r₁、r₂的圆，并沿法线方向拉伸长度d”）的全范围指令。在该框架中，我们设计了一个端到端的自回归Transformer网络，从输入文本生成参数化3D模型。通过视觉质量、参数精度和几何准确性等多维度评估，结果表明该框架在人工智能辅助设计中具有巨大潜力。开源代码与标注数据将公开发布。
原文摘要 · Abstract (English)
Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using designer-friendly instructions for all skill levels. Furthermore, we introduce a data annotation pipeline for generating text prompts based on natural language instructions for the DeepCAD dataset using Mistral and LLaVA-NeXT. The dataset contains $\sim170$K models and $\sim660$K text annotations, from abstract CAD descriptions (e.g., generate two concentric cylinders) to detailed specifications (e.g., draw two circles with center $(x,y)$ and radius $r_{1}$, $r_{2}$, and extrude along the normal by $d$...). Within the Text2CAD framework, we propose an end-to-end transformer-based auto-regressive network to generate parametric CAD models from input texts. We evaluate the performance of our model through a mixture of metrics, including visual quality, parametric precision, and geometrical accuracy. Our proposed framework shows great potential in AI-aided design applications. Our source code and annotations will be publicly available.
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