3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?
This work addresses a problem in industrial design and manufacturing by enabling parametric 3D modeling with LLMs, though it appears incremental as it builds on existing implicit representations and generative models.
The paper tackles the challenge of generating 3D shapes with sharp features and parametric control for industrial design by introducing a framework that uses Large Language Models (LLMs) to create text-driven shapes via program synthesis, resulting in the development of the 3D-PreMise dataset and insights into LLM capabilities and limitations.
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.