3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp
  Features and Parametric Control?
Zeqing Yuan Haoxuan Lan Qiang Zou Junbo Zhao
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.