NURBGen：基于大语言模型驱动的NURBS建模实现高保真文本到CAD生成

NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modeling

November 9, 2025

作者: Muhammad Usama, Mohammad Sadil Khan, Didier Stricker, Muhammad Zeshan Afzal

cs.AI

摘要

从自然语言生成可编辑的3D CAD模型仍具挑战性，因为现有文本到CAD系统要么生成网格模型，要么依赖稀缺的设计历史数据。我们提出NURBGen——首个通过非均匀有理B样条（NURBS）直接从文本生成高保真3D CAD模型的框架。为实现这一目标，我们微调大型语言模型（LLM），将自由格式文本转换为包含NURBS曲面参数（即控制点、节点向量、阶数和有理权重）的JSON表示，这些参数可通过Python直接转换为边界表示（BRep）格式。我们进一步提出混合表示法，将未裁剪的NURBS与解析图元相结合，以更稳健地处理裁剪曲面和退化区域，同时降低标记复杂度。此外，我们推出partABC数据集——这是ABC数据集的精选子集，包含独立CAD组件，并通过自动化标注流程添加了详细描述。专家评估证实，NURBGen在多样化提示词上表现优异，在几何保真度和尺寸精度方面均超越现有方法。代码与数据集将公开发布。

English

Generating editable 3D CAD models from natural language remains challenging, as existing text-to-CAD systems either produce meshes or rely on scarce design-history data. We present NURBGen, the first framework to generate high-fidelity 3D CAD models directly from text using Non-Uniform Rational B-Splines (NURBS). To achieve this, we fine-tune a large language model (LLM) to translate free-form texts into JSON representations containing NURBS surface parameters (i.e, control points, knot vectors, degrees, and rational weights) which can be directly converted into BRep format using Python. We further propose a hybrid representation that combines untrimmed NURBS with analytic primitives to handle trimmed surfaces and degenerate regions more robustly, while reducing token complexity. Additionally, we introduce partABC, a curated subset of the ABC dataset consisting of individual CAD components, annotated with detailed captions using an automated annotation pipeline. NURBGen demonstrates strong performance on diverse prompts, surpassing prior methods in geometric fidelity and dimensional accuracy, as confirmed by expert evaluations. Code and dataset will be released publicly.