通过分层推理与参数对齐，提升复杂设计的结构一致性和参数准确性。
HierCAD: Hierarchical Text-to-CAD Design via Structure Alignment and Parameter Grounding
- 分层分解建模：将CAD构建过程拆分为对象级与部件级的逐步推理。
- 生成精度显著提升：在序列生成和模型重建任务上超越现有最优方法。
- 适合工业设计与AI辅助建模场景，尤其关注高保真参数化建模需求。
近期基于大语言模型的文本到CAD方法虽有进展，但在复杂设计中常难以保持结构一致性并准确对齐几何参数。为此，我们提出HierCAD，一种分层式文本到CAD框架，通过将CAD构建树分解为对象级过程推理与部件级拓扑推理路径，实现渐进式建模。为进一步提升生成保真度，引入统一的结构对齐与参数接地（SAPG）学习策略：结构对齐使拓扑推理轨迹与对应的参数化CAD段对齐；参数接地通过结构保持的参数扰动与基于排序的监督，缓解捷径学习问题。实验表明，HierCAD在CAD序列生成与重构模型评估上均优于先前最先进方法。代码已公开于https://github.com/Collab-Gen/HierCAD。
原文摘要 · Abstract (English)
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.
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