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3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?
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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.
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Cited by 2 Pith papers
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Geometric consensus selection, which returns the most representative compiled CAD model in a sampled pool, outperforms a VLM verifier on geometry metrics and matches it on topology.
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Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models
Quantitative geometry scores across four input types show semantic richness improves LLM-generated CAD fidelity, with code-based prompts reaching perfect scores only after human code edits.
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