用自然语言生成可编辑的高保真3D CAD模型，直接输出NURBS参数。
NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modeling
- 用大语言模型将文本转为包含控制点、权值等参数的JSON格式NURBS数据。
- 在几何精度和尺寸准确性上超越现有方法，专家评估表现优异。
- 适合需要精准建模与可编辑性的工业设计、逆向工程场景。
从自然语言生成可编辑的3D CAD模型仍具挑战性，现有文本到CAD系统要么生成网格，要么依赖稀缺的设计历史数据。我们提出NURBGen，首个直接通过非均匀有理B样条（NURBS）从文本生成高保真3D CAD模型的框架。为此，我们微调大型语言模型（LLM），将自由文本转换为包含控制点、节点向量、次数和有理权重等参数的JSON表示，该数据可直接通过Python转为边界表示（BRep）格式。我们还提出一种混合表示：结合未修剪NURBS与解析基本体，更稳健地处理修剪表面和退化区域，同时降低令牌复杂度。此外，我们构建了partABC——一个经过筛选的ABC数据集子集，包含独立的CAD组件，并通过自动化标注流程添加详细描述。NURBGen在多样化提示下表现出色，几何保真度与尺寸准确性均优于先前方法，经专家评估验证。代码与数据集将公开发布。
原文摘要 · Abstract (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 (\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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。