DraftCoder:A Benchmark and Model for Dimensioned Multi-View Mechanical Drawing

Mechanical engineering drawings convey the design intent of parts through standardized views and complete dimensions, serving as a vital bridge between design and manufacturing. When 3D models are unavailable, generating engineering drawings based on part images and key dimensions still relies on manual labor. To address this, we propose DraftCoder, which uses supervised fine-tuning and reinforcement learning based on execution feedback to convert text-and-image inputs into executable programs, outputting editable multi-view DXF engineering drawings with native dimension annotations. We designed the Hierarchical Drawing Consistency Reward (HDCR) to evaluate geometric measurements and dimensional annotations separately, ensuring that the rewards for dimensions and annotations depend on both view and geometric quality. We also constructed the DimDraft dataset and evaluation benchmark, comprising 5,233 samples and nine categories of part structures, with paired text-image inputs, drawing programs, and reference engineering drawings. In our experiments, the rate of scoreable DXF generation reached 86.26%; the GRPO phase required only 96 training samples, achieving relative improvements of 10.29% and 9.12% over SFT in complete annotation matching rate and layout compliance rate, respectively. The dataset will be made publicly available in the future.