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.

翻译：近年来，隐式三维表示与生成模型的进步显著推动了三维物体生成领域的发展。然而，在参数化控制下精确建模具有明确锐利特征的几何体仍是一项重大挑战——这在工业设计与制造等关键领域尤为重要。为弥合这一差距，我们提出一个利用大语言模型（LLMs）通过程序合成操控三维软件、生成文本驱动三维形状的框架。我们发布了3D-PreMise数据集——专为工业形状参数化建模定制的数据集，旨在基于我们所提出的流水线探索前沿LLMs的性能。本研究揭示了高效生成策略，并通过视觉接口深入探究LLMs的自我修正能力。我们的工作凸显了LLMs在工业三维参数化建模中蕴含的潜力与局限性。