用自然语言生成可编辑带标注的CAD代码，提升设计个性化效率
CAD-Coder:Text-Guided CAD Files Code Generation
- 将自然语言转为Python可执行的CAD脚本，支持交互式修改
- 构建包含29,130个带标注可编辑DXF文件的数据集
- 适合需要快速生成可定制工业设计稿的工程师和开发者
计算机辅助设计（CAD）用于数字创建现实产品的2D图纸和3D模型。传统CAD依赖专家手绘或现有库文件修改，难以实现快速个性化。随着生成式人工智能兴起，个性化CAD生成成为可能。然而，现有方法生成的结果缺乏交互编辑性和几何标注，限制了在制造中的应用。为此，我们提出CAD-Coder框架，将自然语言指令转化为可在Python环境中执行的CAD脚本，生成人类可编辑的CAD文件（.DXF）。为支持可编辑且带标注的草图生成，我们构建了一个涵盖29,130个带有对应脚本代码的DXF文件的综合数据集，每个草图均保留可编辑性与几何标注信息。我们在多种2D/3D CAD生成任务上对CAD-Coder进行评估，结果表明其具备更优的交互能力，并唯一实现了带几何标注的可编辑草图。
原文摘要 · Abstract (English)
Computer-aided design (CAD) is a way to digitally create 2D drawings and 3D models of real-world products. Traditional CAD typically relies on hand-drawing by experts or modifications of existing library files, which doesn't allow for rapid personalization. With the emergence of generative artificial intelligence, convenient and efficient personalized CAD generation has become possible. However, existing generative methods typically produce outputs that lack interactive editability and geometric annotations, limiting their practical applications in manufacturing. To enable interactive generative CAD, we propose CAD-Coder, a framework that transforms natural language instructions into CAD script codes, which can be executed in Python environments to generate human-editable CAD files (.Dxf). To facilitate the generation of editable CAD sketches with annotation information, we construct a comprehensive dataset comprising 29,130 Dxf files with their corresponding script codes, where each sketch preserves both editability and geometric annotations. We evaluate CAD-Coder on various 2D/3D CAD generation tasks against existing methods, demonstrating superior interactive capabilities while uniquely providing editable sketches with geometric annotations.
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