An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment

TL;DR

This paper introduces IRIS-v2, a comprehensive industrial dataset with multimodal data to facilitate the alignment of schematics and scene acquisitions, addressing a key challenge in digital twin construction for old industrial sites.

Contribution

The paper presents IRIS-v2, a new dataset with diverse multimodal data and a case study demonstrating improved schematic-scene alignment methods.

Findings

IRIS-v2 enables research in schematic and scene alignment.

Combining segmentation and graph matching reduces alignment time.

The dataset addresses a gap in public industrial datasets.

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.