3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?

TL;DR

This paper introduces 3D-PreMise, a framework leveraging Large Language Models to generate 3D shapes with sharp features and parametric control, addressing key challenges in industrial design and manufacturing.

Contribution

It presents a novel pipeline that uses LLMs for text-driven 3D shape generation and introduces a new dataset for 3D parametric modeling of industrial shapes.

Findings

Effective generation strategies for 3D shapes with sharp features.

Demonstrated self-correction capabilities of LLMs in 3D modeling.

Identified potential and limitations of LLMs in industrial 3D design.

Abstract

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.