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.
@article{arxiv.2401.06437,
title = {3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?},
author = {Zeqing Yuan and Haoxuan Lan and Qiang Zou and Junbo Zhao},
journal= {arXiv preprint arXiv:2401.06437},
year = {2024}
}