  Recent advancements in implicit 3D representations and generative models have
markedly propelled the field of 3D object generation forward. However, it
remains a significant challenge to accurately model geometries with defined
sharp features under parametric controls, which is crucial in fields like
industrial design and manufacturing. To bridge this gap, we introduce a
framework that employs Large Language Models (LLMs) to generate text-driven 3D
shapes, manipulating 3D software via program synthesis. We present 3D-PreMise,
a dataset specifically tailored for 3D parametric modeling of industrial
shapes, designed to explore state-of-the-art LLMs within our proposed pipeline.
Our work reveals effective generation strategies and delves into the
self-correction capabilities of LLMs using a visual interface. Our work
highlights both the potential and limitations of LLMs in 3D parametric modeling
for industrial applications.
