首个开放的多领域工程图谱数据集，助力AI解析复杂图纸。
Enginuity: Building an Open Multi-Domain Dataset of Complex Engineering Diagrams
- 构建跨领域的工程图结构化数据集，标注组件关系与连接信息。
- 支持多模态大模型进行图纸解析、跨模态检索等任务。
- 适合从事AI辅助设计、科学发现与视觉推理的研究者使用。
我们提出Enginuity——首个公开的大型多领域工程图谱数据集，包含全面的结构标注，专为自动化图纸解析而设计。通过捕捉不同工程领域中组件的层级关系、连接方式及语义元素，该数据集可推动多模态大语言模型完成结构化图纸解析、跨模态信息检索以及AI辅助工程仿真等关键下游任务。Enginuity将推动AI在科学发现中的应用，使人工智能系统能够理解并操作嵌入在工程图纸中的视觉-结构知识，突破当前AI在需图纸解读、技术绘图分析与视觉推理的科研流程中无法深度参与的根本障碍。
原文摘要 · Abstract (English)
We propose Enginuity - the first open, large-scale, multi-domain engineering diagram dataset with comprehensive structural annotations designed for automated diagram parsing. By capturing hierarchical component relationships, connections, and semantic elements across diverse engineering domains, our proposed dataset would enable multimodal large language models to address critical downstream tasks including structured diagram parsing, cross-modal information retrieval, and AI-assisted engineering simulation. Enginuity would be transformative for AI for Scientific Discovery by enabling artificial intelligence systems to comprehend and manipulate the visual-structural knowledge embedded in engineering diagrams, breaking down a fundamental barrier that currently prevents AI from fully participating in scientific workflows where diagram interpretation, technical drawing analysis, and visual reasoning are essential for hypothesis generation, experimental design, and discovery.
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