SAR4CAD: Structure-Aware Reinforcement Learning for Parametric CAD Reconstruction from Multi-View Drawings

Computer-Aided Design (CAD) models can be reconstructed from engineering drawings; however, existing methods are limited by the scarcity of well-annotated drawing datasets and an excessive reliance on a single sequence representation. Specifically, most sequence generators optimize only one serialization scheme, although neither sketch ordering nor the decomposition of certain features is necessarily unique. SAR4CAD is a two-stage framework that trains a multimodal large language model (MLLM) using multi-view renderings, scalable vector graphics (SVG) projection tokens, and concise textual descriptions. The SVG tokens encode visible and hidden edges, primitive types, and quantized coordinates extracted from the CAD model, thereby providing reliable geometric information for reconstruction. In the first stage, supervised fine-tuning teaches the model the required output syntax and establishes a stable generation policy. In the second stage, structure-aware group relative policy optimization (GRPO) evaluates sampled outputs after they have been parsed and executed. The associated reference-guided structural reward integrates CAD-kernel validity, structural-count similarity, sketch overlap computed via Hungarian matching, ordered extrusion-parameter similarity, and 3D Chamfer similarity. Experiments on a constructed parametric CAD dataset demonstrate that SAR4CAD consistently outperforms the evaluated baselines across all evaluation metrics. Code is available at bluehttps://anonymous.4open.science/r/SAR4CAD-F64F.