CADEvolve generates realistic CAD code by evolving programs through iterative refinement and validation.
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.
Public CAD datasets contain mostly simple sketch-extrude sequences, failing to represent real industrial complexity.
CADEvolve uses evolutionary program synthesis to generate diverse, realistic CAD training data offline.
Propose-execute-filter pipeline with vision-language models iteratively creates valid, geometrically sound CAD programs.
Previous synthetic CAD generation methods produce only sketches and extrusions without complex geometric operations.
Frozen vision-language models saturate on extruded prisms and fail to chain heterogeneous CAD operations reliably.
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