将位图二维图纸转为可编辑的参数化CAD代码
Drawing-Recode: Annotation Grounding for Parametric CAD Code Generation from Raster 2D CAD Drawings
- 用图像编码器提取几何特征，独立识别标注文本
- 通过交叉注意力和标注接地损失显式关联尺寸与图形
- 适配工业扫描图，助力零件复现与制造自动化
从数字化转型前积累的位图格式2D计算机辅助设计（CAD）图纸中恢复参数化CAD序列，对零件复现和制造流程自动化至关重要。然而，现有方法要么仅处理矢量图，要么局限于特定领域，且未能显式关联尺寸标注与几何信息，限制了其对3D参数化CAD序列恢复中尺寸信息的利用。本文提出Drawing-Recode框架，从位图2D CAD图纸生成参数化CAD序列。该框架通过图像编码器提取几何特征，通过独立文本识别模块识别标注，再利用交叉注意力和提出的标注接地损失（AGL）显式将标注与几何信息对齐。对齐后的特征输入大语言模型（LLM），生成结构化参数化CAD代码（SPCC）格式的CAD代码。实验表明，Drawing-Recode优于现有基线，在类似工业条件的扫描图上仍具鲁棒性。我们期望该方法推动工业场景下位图2D CAD图纸的数字化，助力零件复现与制造自动化。
原文摘要 · Abstract (English)
Recovering Parametric CAD sequences from raster-format 2D Computer-Aided Design (CAD) drawings accumulated prior to digital transformation is important for part reproduction and manufacturing process automation. However, existing studies either process only vector drawings or are limited to specific domains, and fail to explicitly connect dimensional annotations to geometric information, limiting their use of dimensional information for 3D Parametric CAD sequences recovery. We propose Drawing-Recode, a framework that generates Parametric CAD sequences as CAD code from raster 2D CAD drawings. Drawing-Recode extracts geometric features via an image encoder and recognizes annotations through a separate text recognition module, then explicitly grounds annotations to geometric information using cross-attention and our proposed Annotation Grounding Loss (AGL). The resulting features are fed into a Large Language Model (LLM) to generate CAD code in the Structured Parametric CAD Code (SPCC) format. Experiments show that Drawing-Recode outperforms existing baselines and remains robust on scanned drawings resembling industrial conditions. We expect Drawing-Recode contributes to digitizing raster 2D CAD drawings in industrial settings and to part reproduction and manufacturing automation.
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