CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward

Yandong Guan; Xilin Wang; Ximing Xing; Jing Zhang; Dong Xu; Qian Yu

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arXiv · 2505.19713

CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward

Yandong Guan·Xilin Wang·Ximing Xing·Jing Zhang·Dong Xu·Qian Yu

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Abstract

In this work, we introduce CAD-Coder, a novel framework that reformulates text-to-CAD as the generation of CadQuery scripts - a Python-based, parametric CAD language. This representation enables direct geometric validation, a richer modeling vocabulary, and seamless integration with existing LLMs. To further enhance code validity and geometric fidelity, we propose a two-stage learning pipeline: (1) supervised fine-tuning on paired text-CadQuery data, and (2) reinforcement learning with Group Reward Policy Optimization (GRPO), guided by a CAD-specific reward comprising both a geometric reward (Chamfer Distance) and a format reward. We also introduce a chain-of-thought (CoT) planning process to improve model reasoning, and construct a large-scale, high-quality dataset of 110K text-CadQuery-3D model triplets and 1.5K CoT samples via an automated pipeline. Extensive experiments demonstrate that CAD-Coder enables LLMs to generate diverse, valid, and complex CAD models directly from natural language, advancing the state of the art of text-to-CAD generation and geometric reasoning.

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BibTeX RIS

Yandong Guan, Xilin Wang, Ximing Xing, Jing Zhang, Dong Xu, Qian Yu. 2025-05-26. CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward. https://arxiv.org/abs/2505.19713

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