CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation

Anna C. Doris; Md Ferdous Alam; Amin Heyrani Nobari; Faez Ahmed

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arXiv · 2505.14646

CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation

Anna C. Doris·Md Ferdous Alam·Amin Heyrani Nobari·Faez Ahmed

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Abstract

Efficient creation of accurate and editable 3D CAD models is critical in engineering design, significantly impacting cost and time-to-market in product innovation. Current manual workflows remain highly time-consuming and demand extensive user expertise. While recent developments in AI-driven CAD generation show promise, existing models are limited by incomplete representations of CAD operations, inability to generalize to real-world images, and low output accuracy. This paper introduces CAD-Coder, an open-source Vision-Language Model (VLM) explicitly fine-tuned to generate editable CAD code (CadQuery Python) directly from visual input. Leveraging a novel dataset that we created--GenCAD-Code, consisting of over 163k CAD-model image and code pairs--CAD-Coder outperforms state-of-the-art VLM baselines such as GPT-4.5 and Qwen2.5-VL-72B, achieving a 100% valid syntax rate and the highest accuracy in 3D solid similarity. Notably, our VLM demonstrates some signs of generalizability, successfully generating CAD code from real-world images and executing CAD operations unseen during fine-tuning. The performance and adaptability of CAD-Coder highlights the potential of VLMs fine-tuned on code to streamline CAD workflows for engineers and designers. CAD-Coder is publicly available at: https://github.com/anniedoris/CAD-Coder.

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Anna C. Doris, Md Ferdous Alam, Amin Heyrani Nobari, Faez Ahmed. 2025-05-20. CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation. https://arxiv.org/abs/2505.14646

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