CIT-CAD：基于约束意图树的CAD代码生成与验证

中文摘要 AI 辅助

自然语言计算机辅助设计（CAD）代码生成旨在将设计意图转化为可执行且可编辑的参数化程序。大型语言模型（LLMs）使这一目标日益可行，但实用的系统必须保留渲染几何体背后的构建过程。现有基准和方法大多关注生成的CAD模型与参考几何体的匹配程度，常使用交并比（IoU）等指标。此类指标可能遗漏零件分解、构建层次、布尔运算、草图结构和几何关系中的错误。这一差距要求一种能够明确表达设计意图并让系统根据该意图检查生成代码的表示方法。我们提出CIT-CAD，一个从输入描述中推断约束意图树（CIT）以表示预期实体、层次、操作和关系的框架。该树具有双重作用：它指导CAD代码生成并定义用于验证的预期约束。该框架从生成的程序中提取实际约束，将其与预期约束进行比较，并利用不匹配来定位和修复设计违规。实验表明，该框架提升了CAD生成性能，在更复杂的多实体设计中提升更大。通过将设计意图转化为明确且可检查的对象，这项工作首次尝试将文本到CAD生成从渲染几何匹配推进到构建感知的合成、验证和修复。

英文摘要

Natural-language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large language models (LLMs) make this goal increasingly practical, but useful systems must preserve the construction process behind the rendered geometry. Existing benchmarks and methods mostly focus on how closely the generated CAD model matches the reference geometry, often using metrics such as Intersection over Union (IoU). Such metrics can miss errors in part decomposition, construction hierarchy, Boolean operations, sketch structure, and geometric relations. This gap calls for a representation that makes design intent explicit and lets a system check generated code against that intent. We propose CIT-CAD, a framework that infers a Constraint Intent Tree (CIT) from the input description to represent the intended entities, hierarchy, operations, and relations. The tree has two roles: it guides CAD code generation and defines expected constraints for verification. The framework extracts actual constraints from the generated program, compares them with the expected constraints, and uses mismatches to localize and repair design violations. Experiments show that the framework improves CAD generation performance, with larger gains on more complex multi-entity designs. By turning design intent into an explicit and checkable object, this work is the first attempt to move text-to-CAD generation beyond rendered-geometry matching toward construction-aware synthesis, verification, and repair.

发表机构

• National Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）
• School of Artificial Intelligence（人工智能学院）
• Nanjing University（南京大学）

机构由 AI 辅助整理，请以论文原文为准。