CADEvolve: Creating Realistic CAD via Program Evolution
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908839551434752 |
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| author | Elistratov, Maksim Barannikov, Marina Ivanov, Gregory Khrulkov, Valentin Konushin, Anton Kuznetsov, Andrey Zhemchuzhnikov, Dmitrii |
| author_facet | Elistratov, Maksim Barannikov, Marina Ivanov, Gregory Khrulkov, Valentin Konushin, Anton Kuznetsov, Andrey Zhemchuzhnikov, Dmitrii |
| contents | Computer-Aided Design (CAD) delivers rapid, editable modeling for engineering and manufacturing. Recent AI progress now makes full automation feasible for various CAD tasks. However, progress is bottlenecked by data: public corpora mostly contain sketch-extrude sequences, lack complex operations, multi-operation composition and design intent, and thus hinder effective fine-tuning. Attempts to bypass this with frozen VLMs often yield simple or invalid programs due to limited 3D grounding in current foundation models. We present CADEvolve, an evolution-based pipeline and dataset that starts from simple primitives and, via VLM-guided edits and validations, incrementally grows CAD programs toward industrial-grade complexity. The result is 8k complex parts expressed as executable CadQuery parametric generators. After multi-stage post-processing and augmentation, we obtain a unified dataset of 1.3m scripts paired with rendered geometry and exercising the full CadQuery operation set. A VLM fine-tuned on CADEvolve achieves state-of-the-art results on the Image2CAD task across the DeepCAD, Fusion 360, and MCB benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16317 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CADEvolve: Creating Realistic CAD via Program Evolution Elistratov, Maksim Barannikov, Marina Ivanov, Gregory Khrulkov, Valentin Konushin, Anton Kuznetsov, Andrey Zhemchuzhnikov, Dmitrii Graphics Computer-Aided Design (CAD) delivers rapid, editable modeling for engineering and manufacturing. Recent AI progress now makes full automation feasible for various CAD tasks. However, progress is bottlenecked by data: public corpora mostly contain sketch-extrude sequences, lack complex operations, multi-operation composition and design intent, and thus hinder effective fine-tuning. Attempts to bypass this with frozen VLMs often yield simple or invalid programs due to limited 3D grounding in current foundation models. We present CADEvolve, an evolution-based pipeline and dataset that starts from simple primitives and, via VLM-guided edits and validations, incrementally grows CAD programs toward industrial-grade complexity. The result is 8k complex parts expressed as executable CadQuery parametric generators. After multi-stage post-processing and augmentation, we obtain a unified dataset of 1.3m scripts paired with rendered geometry and exercising the full CadQuery operation set. A VLM fine-tuned on CADEvolve achieves state-of-the-art results on the Image2CAD task across the DeepCAD, Fusion 360, and MCB benchmarks. |
| title | CADEvolve: Creating Realistic CAD via Program Evolution |
| topic | Graphics |
| url | https://arxiv.org/abs/2602.16317 |