An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment

2026-02-17

AI Summary

This study addresses the inefficiency and limited scalability of manually aligning functional diagrams—such as P&IDs—with 2D/3D as-built data in legacy industrial facilities lacking native digital models. To bridge this gap, the authors introduce IRIS-v2, the first publicly available multimodal industrial alignment dataset, which encompasses images, LiDAR point clouds, semantic segmentation masks, CAD models, 3D piping layouts, and corresponding P&ID schematics. Building upon this foundation, they propose an automated alignment method that integrates semantic segmentation, graph matching, and LiDAR point cloud processing. Evaluated in real-world industrial settings, the approach significantly reduces alignment time compared to manual workflows. This work establishes both a critical data resource and a practical pathway toward automating digital twin construction in complex industrial environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• digital twin
• functional schematics alignment
• industrial dataset
• P&ID
• scene acquisition

Innovation

• digital twin
• schematic alignment
• industrial dataset
• graph matching
• semantic segmentation