AI 中文总结
Drawing-Recode是一种从栅格2D CAD图纸生成参数化CAD代码的框架，通过图像编码器、文本识别模块、交叉注意力与AGL实现标注定位，性能优于基线且对工业扫描图纸鲁棒，可助力制造自动化。
AI 中文摘要
从数字化转型前积累的栅格格式2D计算机辅助设计（CAD）图纸中恢复参数化CAD序列，对于零件复制和制造过程自动化至关重要。然而，现有研究要么仅处理矢量图纸，要么局限于特定领域，且未能将尺寸标注与几何信息明确关联，限制了其利用尺寸信息进行3D参数化CAD序列恢复的能力。我们提出Drawing-Recode，这一框架可从栅格2D CAD图纸中生成作为CAD代码的参数化CAD序列。Drawing-Recode通过图像编码器提取几何特征，通过独立的文本识别模块识别标注，随后利用交叉注意力机制和我们提出的标注定位损失（Annotation Grounding Loss, AGL）将标注明确定位到几何信息。所得特征被输入大型语言模型（Large Language Model, LLM），以生成结构化参数化CAD代码（Structured Parametric CAD Code, SPCC）格式的CAD代码。实验表明，Drawing-Recode的性能优于现有基线方法，且在类似工业场景的扫描图纸上仍保持鲁棒性。我们期望Drawing-Recode能助力工业场景下栅格2D CAD图纸的数字化，以及零件复制和制造自动化。
英文摘要
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.
