ED2CAD: Parametric CAD Reconstruction from Dimension-Annotated Engineering Drawings

Reconstructing editable 3D parametric models from dimension-annotated engineering drawings remains a challenging problem. We propose ED2CAD, a dimension-aware framework for reconstructing parametric Computer-Aided Design (CAD) models from multimodal engineering drawings. To support this task, we develop an automated data pipeline that generates paired raster and vector drawings from B-rep geometry, including orthographic views with visible lines, hidden lines, and dimensional annotations. A raster-vector Transformer jointly encodes and fuses multimodal drawing features to autoregressively predict CAD commands and parameters. To improve geometric fidelity, we further introduce a point cloud geometry proxy that provides 3D supervision based on Chamfer distance. The predicted command sequences are instantiated through a CAD kernel to obtain executable 3D models for geometric evaluation. Extensive experiments demonstrate the effectiveness of dimensional annotations, multimodal inputs, geometric supervision, and the proposed architecture.