发表机构
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对文本到CAD设计中结构一致性和参数确定问题，提出HierCAD框架，通过分解CAD构造树进行渐进推理，并引入SAPG学习策略，在CAD序列生成和模型评估上优于现有方法。
AI 中文摘要
近期文本到CAD的方法利用大语言模型取得了有前景的成果，但在复杂设计中保持结构一致性和准确确定几何参数方面存在困难。为解决这些问题，我们提出了HierCAD，一个分层文本到CAD框架，可改善结构推理和参数预测。HierCAD将CAD生成重新表述为渐进推理，通过将CAD构造树分解为对象级过程推理和部件级拓扑推理轨迹。为进一步提高生成保真度，引入统一的结构对齐和参数接地（SAPG）学习策略。实验表明HierCAD在CAD序列生成和重建CAD模型评估上优于现有最先进方法。
英文摘要
Recent text-to-CAD approaches have shown promising results by leveraging large language models, but they often struggle with maintaining structural consistency in complex designs and accurately grounding geometric parameters. To address these issues, we propose HierCAD, a hierarchical text-to-CAD framework that improves both structural reasoning and parameter prediction. HierCAD reformulates CAD generation as progressive reasoning by decomposing CAD construction trees into object-level procedural reasoning and part-level topology reasoning trajectories. To further improve generation fidelity, we introduce a unified Structure Alignment and Parameter Grounding (SAPG) learning strategy. Structure alignment aligns topology reasoning trajectories with their corresponding parametric CAD spans, while parameter grounding mitigates shortcut learning through structure-preserving parameter perturbations and ranking-based supervision. Experiments demonstrate that HierCAD outperforms prior state-of-the-art methods on both CAD sequence generation and reconstructed CAD model evaluation. Our code is available at https://github.com/Collab-Gen/HierCAD.
