Engineering design intent is often communicated through rasterized orthographic drawings. However, downstream workflows inherently require editable and parametrically defined 3D computer-aided design (CAD) models. To bridge this gap, we introduce Ortho2CAD, a vision-language model (VLM) specifically designed to translate rasterized orthographic drawings directly into editable CadQuery code, which can then be seamlessly converted into 3D CAD models. To train the model effectively, we utilize supervised fine-tuning (SFT) for instances where explicit CadQuery code labels already exist, and we apply geometry-grounded reinforcement learning (RL) to optimize the model in scenarios where ground-truth labels are absent. To enable learning at scale, we create a pythonOCC-based drawing generator that renders first-angle orthographic projections from STEP models, complete with dashed hidden lines and key dimensions. On existing datasets encompassing settings both with and without CadQuery supervision, we generate orthographic drawings and show that our model produces 100% syntactically valid code. Moreover, it achieves a 3D CAD intersection-over-union (IoU) accuracy that surpasses all baselines, with an average relative improvement of over 7% compared directly against the next best performing model. We show that leveraging VLMs with SFT and RL techniques can effectively pave the way forward for orthographic drawing to 3D CAD reconstruction. Our implementation is available at https://github.com/AdityaJoglekar/Ortho2CAD.

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