PartCoder: Reconstructing Multi-File CAD Code Assemblies from Point Clouds

Recent LLM-based CAD reverse-engineering methods recover editable CAD code from point clouds but represent entire objects as monolithic programs. This creates generation and representation bottlenecks because the model must reconstruct the complete geometry within a single script, while the output lacks the explicit part structure and part-level controllability of industrial CAD assemblies. We introduce PartCoder, the first method for reconstructing multi-file CAD code assemblies directly from point clouds. PartCoder hierarchically decomposes a dense input point cloud into geometrically coherent parts, completes newly created interfaces, reconstructs each part using a generative CAD code backbone, and combines the components through a central assembly file. It retains only decompositions that improve the overall reconstruction, adaptively selecting the number of parts and enabling existing backbones to handle more complex objects without retraining. We introduce PrIMe3D, a benchmark of 1,000 mechanical objects generated through a prompt-to-image-to-mesh pipeline for evaluating the emerging task of converting AI-generated 3D assets into editable CAD models. Averaged across three backbones, PartCoder improves mean IoU over the corresponding monolithic baselines by 1.1, 7.7, and 11.1 percentage points on Fusion360, OmniCAD, and PrIMe3D, respectively, with larger gains on more complex datasets while producing modular CAD assemblies with independently editable parts.