用大模型+交互对话，让新手也能生成高质量概念级3D设计。
CADDesigner: Conceptual CAD Model Generation with a General-Purpose Agent
- 基于文本或草图输入，通过对话明确需求并生成代码。
- 在迭代视觉反馈中优化设计，生成结果优于现有方法。
- 支持知识积累，适合设计初学者和快速原型开发。
计算机辅助设计（CAD）广泛应用于概念设计与参数化三维建模，但通常需要设计者具备较高专业水平。为降低入门门槛并促进早期阶段的CAD建模，我们提出CADDesigner，一个基于大语言模型的通用智能体，用于概念级CAD设计。该智能体接受文本描述或草图作为输入，通过与用户进行交互式对话，实现对设计需求的全面分析与细化。基于创新的显式上下文强制范式（Explicit Context Imperative Paradigm, ECIP），智能体生成高质量的CAD建模代码。在生成过程中，通过迭代视觉反馈持续提升模型质量。生成的设计案例可存储于结构化知识库中，实现持续的知识积累与代码生成能力的迭代优化。实验结果表明，CADDesigner在概念级CAD模型生成任务上表现优异，性能超越多个代表性基线方法。
原文摘要 · Abstract (English)
Computer-Aided Design (CAD) is widely used for conceptual design and parametric 3D modeling, but typically requires a high level of expertise from designers. To lower the entry barrier and facilitate early-stage CAD modeling, we present CADDesigner, an LLM-powered agent for conceptual CAD design. The agent accepts both textual descriptions and sketches as input, engaging in interactive dialogue with users to refine and clarify design requirements through comprehensive requirement analysis. Built upon a novel Explicit Context Imperative Paradigm (ECIP), the agent generates high-quality CAD modeling code. During the generation process, the agent incorporates iterative visual feedback to improve model quality. Generated design cases can be stored in a structured knowledge base, providing a mechanism for continual knowledge accumulation and future improvement of code generation. Experimental results show that CADDesigner achieves competitive performance and outperforms representative baselines on conceptual CAD model generation tasks.
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