CME-CAD: Heterogeneous Collaborative Multi-Expert Reinforcement Learning for CAD Code Generation
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866918265315393536 |
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| author | Niu, Ke Yu, Haiyang Chen, Zhuofan Yao, Zhengtao Jia, Weitao Ge, Xiaodong Tang, Jingqun Cui, Benlei Li, Bin Xue, Xiangyang |
| author_facet | Niu, Ke Yu, Haiyang Chen, Zhuofan Yao, Zhengtao Jia, Weitao Ge, Xiaodong Tang, Jingqun Cui, Benlei Li, Bin Xue, Xiangyang |
| contents | Computer-Aided Design (CAD) is essential in industrial design, but the complexity of traditional CAD modeling and workflows presents significant challenges for automating the generation of high-precision, editable CAD models. Existing methods that reconstruct 3D models from sketches often produce non-editable and approximate models that fall short of meeting the stringent requirements for precision and editability in industrial design. Moreover, the reliance on text or image-based inputs often requires significant manual annotation, limiting their scalability and applicability in industrial settings. To overcome these challenges, we propose the Heterogeneous Collaborative Multi-Expert Reinforcement Learning (CME-CAD) paradigm, a novel training paradigm for CAD code generation. Our approach integrates the complementary strengths of these models, facilitating collaborative learning and improving the model's ability to generate accurate, constraint-compatible, and fully editable CAD models. We introduce a two-stage training process: Multi-Expert Fine-Tuning (MEFT), and Multi-Expert Reinforcement Learning (MERL). Additionally, we present CADExpert, an open-source benchmark consisting of 17,299 instances, including orthographic projections with precise dimension annotations, expert-generated Chain-of-Thought (CoT) processes, executable CADQuery code, and rendered 3D models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23333 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CME-CAD: Heterogeneous Collaborative Multi-Expert Reinforcement Learning for CAD Code Generation Niu, Ke Yu, Haiyang Chen, Zhuofan Yao, Zhengtao Jia, Weitao Ge, Xiaodong Tang, Jingqun Cui, Benlei Li, Bin Xue, Xiangyang Computer Vision and Pattern Recognition Computer-Aided Design (CAD) is essential in industrial design, but the complexity of traditional CAD modeling and workflows presents significant challenges for automating the generation of high-precision, editable CAD models. Existing methods that reconstruct 3D models from sketches often produce non-editable and approximate models that fall short of meeting the stringent requirements for precision and editability in industrial design. Moreover, the reliance on text or image-based inputs often requires significant manual annotation, limiting their scalability and applicability in industrial settings. To overcome these challenges, we propose the Heterogeneous Collaborative Multi-Expert Reinforcement Learning (CME-CAD) paradigm, a novel training paradigm for CAD code generation. Our approach integrates the complementary strengths of these models, facilitating collaborative learning and improving the model's ability to generate accurate, constraint-compatible, and fully editable CAD models. We introduce a two-stage training process: Multi-Expert Fine-Tuning (MEFT), and Multi-Expert Reinforcement Learning (MERL). Additionally, we present CADExpert, an open-source benchmark consisting of 17,299 instances, including orthographic projections with precise dimension annotations, expert-generated Chain-of-Thought (CoT) processes, executable CADQuery code, and rendered 3D models. |
| title | CME-CAD: Heterogeneous Collaborative Multi-Expert Reinforcement Learning for CAD Code Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.23333 |