CADEvolve: Creating Realistic CAD via Program Evolution
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.
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Maksim Elistratov
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Marina Barannikov
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2026-02-20T08:37:23.959Z
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Marina Barannikov
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m1b
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Gregory Ivanov
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Valentin Khrulkov
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Anton Konushin
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Andrey Kuznetsov
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2026-02-20T08:37:26.401Z
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Andrey Kuznetsov
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Dmitrii Zhemchuzhnikov
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2026-02-19T11:48:52.062Z
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DMITRII ZHEMCHUZHNIKOV
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zhemchuzhnikov
CADEvolve: Creating Realistic CAD via Program Evolution
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.
CADEvolve presents an evolution-based approach using VLM-guided edits to generate complex CAD programs from simple primitives, creating a large dataset for improved Image2CAD performance.
CAD VLM evolution-based pipeline parametric generators CadQuery Image2CAD DeepCAD Fusion 360 MCB