📄 论文总结
NURBGen：通过大语言模型驱动的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
1️⃣ 一句话总结
这篇论文提出了首个直接从文本生成高精度可编辑3D CAD模型的框架，它通过微调大语言模型将自然语言转换为NURBS曲面参数，并结合混合表示方法显著提升了模型的几何保真度和鲁棒性。
🤖 系统
Abstract - NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modeling
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 (\textit{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.