ExpConCAD：基于形状描述与隐式空间约束的经验引导式文本到CAD生成
ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints
- College of Computer Science, Sichuan University（四川大学计算机学院）
- Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ExpConCAD是一个经验增强框架，针对文本到CAD生成中描述遗漏空间约束的问题，通过恢复构建结构、检索相关经验补全约束，生成可执行CadQuery程序，实验验证了其有效性。
AI中文摘要：
文本到CAD旨在从自然语言描述生成可执行的CAD程序。然而，现实世界的描述往往不够明确，会遗漏CAD有效构建所需的关键空间约束，这一挑战在现有方法中基本被忽视。本文认为，缺失的空间约束应基于底层构建结构进行推断，并借助可复用的设计经验。基于此见解，我们提出ExpConCAD，这是一个用于隐式空间约束补全的经验增强框架。ExpConCAD首先恢复预期的构建结构与约束范围，然后为相似范围检索相关的约束补全经验以完成缺失的空间约束，最后生成可执行的CadQuery程序。大量实验证明了ExpConCAD的有效性，并为构建结构理解与经验记忆在空间约束补全中的作用提供了见解。我们的代码可在此URL获取。
英文摘要：
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.
