构建工业场景与功能图谱对齐数据集，助力老旧工厂数字孪生
An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment
- 提出IRIS-v2数据集，融合图像、点云、CAD与管道图等多源信息
- 通过分割与图匹配结合，实验表明可显著缩短对齐时间
- 适合研究工业数字孪生、场景理解与跨模态对齐的学者使用
将功能图谱与二维、三维场景数据对齐，是构建老旧工业设施数字孪生的关键。现有基于图像和激光雷达的人工对齐方式因流程繁琐、场景复杂而难以扩展。加之图谱与现实不一致、公开工业数据集稀缺，该问题既具挑战性又研究不足。本文提出IRIS-v2数据集，涵盖图像、点云、2D标注框与分割掩码、CAD模型、3D管道布局信息及P&ID（管道与仪表图）。在实际案例中，采用分割与图匹配相结合的方法进行对齐实验，有效降低任务耗时。
原文摘要 · Abstract (English)
Aligning functional schematics with 2D and 3D scene acquisitions is crucial for building digital twins, especially for old industrial facilities that lack native digital models. Current manual alignment using images and LiDAR data does not scale due to tediousness and complexity of industrial sites. Inconsistencies between schematics and reality, and the scarcity of public industrial datasets, make the problem both challenging and underexplored. This paper introduces IRIS-v2, a comprehensive dataset to support further research. It includes images, point clouds, 2D annotated boxes and segmentation masks, a CAD model, 3D pipe routing information, and the P&ID (Piping and Instrumentation Diagram). The alignment is experimented on a practical case study, aiming at reducing the time required for this task by combining segmentation and graph matching.
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