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arXiv 提交日期: 2026-06-11
📄 Abstract - IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing

Computer-Aided Design is pivotal in modern manufacturing, yet existing automated methods predominantly rely on open-loop, one-shot generation, creating a mismatch with iterative real-world practices. In this paper, we present IterCAD, a unified multimodal agent framework for closed-loop, interactive CAD generation and editing. We formulate the task as a multi-turn interaction between a multimodal agent and an executable CAD sandbox, covering three tasks: Drawing-to-Code, Text-to-Code, and Interactive Editing. To support this, we develop a data synthesis pipeline incorporating advanced industrial manufacturing features to generate standard-compliant multi-view engineering drawings, complex code-editing tasks, and high-fidelity interaction trajectories. We optimize the agent via progressive SFT followed by geometry-aware reinforcement learning with viable-prefix masking to enhance code executability and geometric fidelity. Finally, we introduce the IterCAD-Bench evaluation suite and propose the Chamfer Distance Tolerance-Recall (CD-TR) curve alongside its AUC-TR metric, establishing a survivor-bias-free standard that unifies code validity and geometric precision. Extensive experiments demonstrate that IterCAD achieves highly competitive performance across multiple benchmarks, significantly outperforming existing approaches in both code executability and geometric precision, while exhibiting superior capabilities in closed-loop iterative refinement.

顶级标签: agents multi-modal model training
详细标签: cad generation reinforcement learning benchmark interactive editing geometric precision 或 搜索:

IterCAD:一种用于视觉引导的CAD生成与编辑的迭代式多模态智能体 / IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing


1️⃣ 一句话总结

本文提出了一种名为IterCAD的智能体框架,它能够像人类设计师一样,通过多轮交互和视觉反馈,逐步生成和修改三维CAD模型,从而解决现有方法一次性生成、缺乏迭代优化能力的问题,并显著提升了代码执行正确性和几何精度。

源自 arXiv: 2606.13368