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.

翻译：将功能示意图与二维及三维场景采集数据进行对齐，对于构建数字孪生体至关重要，尤其对于缺乏原生数字模型的旧工业设施。当前基于图像与LiDAR数据的手动对齐方法，因工业现场的复杂性与操作繁琐性而难以扩展。示意图与现实之间的不一致性，以及公开工业数据集的稀缺性，使得该问题既具挑战性又研究不足。本文提出IRIS-v2数据集，一个旨在支持进一步研究的综合性数据集。它包含图像、点云、二维标注框与分割掩码、CAD模型、三维管道布线信息以及P&ID（管道与仪表流程图）。通过一项实际案例研究对该对齐任务进行了实验验证，旨在结合分割与图匹配技术，显著降低完成该任务所需的时间。