让AI像工程师一样反复修改设计图,实现可执行的精准建模。
IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing

- 构建多轮交互式智能体,结合视觉与文本指令迭代生成编辑CAD
- 在多个基准上代码可执行率与几何精度均显著优于现有方法
- 专为工业设计打造,适合需要闭环优化的制造场景研究者
计算机辅助设计在现代制造中至关重要,但现有自动化方法多依赖单次生成的开环流程,与实际迭代设计过程不匹配。本文提出IterCAD,一种统一的多模态智能体框架,支持闭环、交互式的CAD生成与编辑。将任务建模为智能体与可执行CAD沙盒间的多轮交互,涵盖三类任务:绘图转代码、文本转代码、交互式编辑。为此,我们开发了数据合成管道,融入先进工业制造特征,生成符合标准的多视图工程图、复杂代码编辑任务及高保真交互轨迹。通过渐进式监督微调,再结合基于可行前缀掩码的几何感知强化学习,优化智能体以提升代码可执行性与几何保真度。最后,引入IterCAD-Bench评估套件,并提出切比雪夫距离容忍召回(CD-TR)曲线及其AUC-TR指标,建立无幸存者偏差的标准,统一衡量代码有效性与几何精度。大量实验表明,IterCAD在多个基准上表现优异,显著超越现有方法,在代码可执行性与几何精度方面均有大幅提升,且具备卓越的闭环迭代优化能力。
原文摘要 · Abstract (English)
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.
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