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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.22304 |
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| _version_ | 1866916994198011904 |
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| author | Chen, Jiali Hei, Xusen Liu, HongFei Wei, Yuancheng Deng, Zikun Xie, Jiayuan Cai, Yi Qing, Li |
| author_facet | Chen, Jiali Hei, Xusen Liu, HongFei Wei, Yuancheng Deng, Zikun Xie, Jiayuan Cai, Yi Qing, Li |
| contents | Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions (i.e., CAD programs). In practical design workflows, designers often engage in time-consuming reviews and refinements of these prototypes by comparing them with reference images. To bridge this gap, we introduce the CAD review task to automatically detect and correct potential errors, ensuring consistency between the constructed 3D objects and reference images. However, recent advanced multimodal large language models (MLLMs) struggle to recognize multiple geometric components and perform spatial geometric operations within the CAD program, leading to inaccurate reviews. In this paper, we propose the CAD program repairer (ReCAD) framework to effectively detect program errors and provide helpful feedback on error correction. Additionally, we create a dataset, CADReview, consisting of over 20K program-image pairs, with diverse errors for the CAD review task. Extensive experiments demonstrate that our ReCAD significantly outperforms existing MLLMs, which shows great potential in design applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22304 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction Chen, Jiali Hei, Xusen Liu, HongFei Wei, Yuancheng Deng, Zikun Xie, Jiayuan Cai, Yi Qing, Li Computer Vision and Pattern Recognition Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions (i.e., CAD programs). In practical design workflows, designers often engage in time-consuming reviews and refinements of these prototypes by comparing them with reference images. To bridge this gap, we introduce the CAD review task to automatically detect and correct potential errors, ensuring consistency between the constructed 3D objects and reference images. However, recent advanced multimodal large language models (MLLMs) struggle to recognize multiple geometric components and perform spatial geometric operations within the CAD program, leading to inaccurate reviews. In this paper, we propose the CAD program repairer (ReCAD) framework to effectively detect program errors and provide helpful feedback on error correction. Additionally, we create a dataset, CADReview, consisting of over 20K program-image pairs, with diverse errors for the CAD review task. Extensive experiments demonstrate that our ReCAD significantly outperforms existing MLLMs, which shows great potential in design applications. |
| title | CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.22304 |