让AI理解设计逻辑，自动补全图纸里的空间约束
ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints
- 基于设计经验与构建结构推理缺失的空间约束
- 在多个真实描述上实现90%以上的约束补全准确率
- 适合需要自动化生成可执行CAD代码的工程师
文本生成CAD旨在从自然语言描述生成可执行的CAD程序。然而，现实中的描述往往信息不足，遗漏了构建有效CAD所需的必要空间约束，这一挑战尚未被现有方法充分重视。本文提出ExpConCAD——一种融合设计经验的隐式空间约束补全框架。该方法首先恢复预期的构建结构与约束范围，再检索相似范围的相关约束补全经验以完成缺失的空间约束，最终生成可执行的CadQuery程序。大量实验验证了ExpConCAD的有效性，并揭示了构建结构理解与经验记忆在空间约束补全中的关键作用。代码已开源：https://github.com/Hotjiashell/ExpConCAD。
原文摘要 · Abstract (English)
Text-to-CAD aims to generate executable CAD programs from natural-language descriptions. However, real-world descriptions are often underspecified and omit critical spatial constraints required for valid CAD construction, a challenge that has been largely overlooked by existing methods. In this paper, we argue that missing spatial constraints should be inferred with respect to the underlying construction structure and informed by reusable design experience. Based on this insight, we propose ExpConCAD, an experience-enhanced framework for implicit spatial constraint completion. ExpConCAD first recovers the intended construction structure and constraint scopes, then retrieves relevant constraint-completion experience for similar scopes to complete the missing spatial constraints, and finally generates executable CadQuery programs. Extensive experiments demonstrate the effectiveness of ExpConCAD and provide insights into the role of construction structure understanding and experience memory in spatial constraint completion. Our code is available at: https://github.com/Hotjiashell/ExpConCAD.
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