NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS
  Modeling
Authors: name: Muhammad Usama, name: Mohammad Sadil Khan, name: Didier Stricker, name: Muhammad Zeshan Afzal
Organization: Augmented Vision

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.