IRIS is a multimodal dataset that provides dense station-based LiDAR point clouds, panoramic images, a CAD model, a piping and instrumentation diagram (P&ID), annotated bounding boxes, and segmentation masks for several object categories. The scene represents a large and complex industrial room covering 530 m². It contains objects of various shapes, colors, and sizes such as pipes, valves, pumps and gauges. This dataset has been used for 3D point visibility estimation and scene-functional alignment, but it is also suitable for many other computer vision applications.
If you use this work in your research, please use the following BibTeX entries.
@article{armangeon2026irisv2,
title={An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment},
author={Flavien Armangeon and Thibaud Ehret and Enric Meinhardt-Llopis and Rafael Grompone von Gioi and Guillaume Thibault and Marc Petit and Gabriele Facciolo},
journal={arXiv:2602.15584},
year={2026}
}
@INPROCEEDINGS{10943543,
author={Armangeon, Flavien and Ehret, Thibaud and Meinhardt-Llopis, Enric and Von Gioi, Rafael Grompone and Thibault, Guillaume and Petit, Marc and Facciolo, Gabriele},
booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
title={IRIS-VIS: A New Dataset for Visibility Estimation in an Industrial Environment},
year={2025},
volume={},
number={},
pages={7235-7243},
keywords={Point cloud compression;Measurement;Computer vision;Surface reconstruction;Solid modeling;Three-dimensional displays;Laser radar;Statistical analysis;Shape;Estimation;dataset;point cloud;visibility estimation;cad model;industrial;indoor},
doi={10.1109/WACV61041.2025.00703}
}
@dataset{armangeon_2025_13777859,
author = {Armangeon, Flavien and
Ehret, Thibaud and
Meinhardt-Llopis, Enric and
Grompone Von Gioi, Rafael and
Thibault, Guillaume and
Petit, Marc and
Facciolo, Gabriele},
title = {IRIS: Industrial Room In Saclay},
month = feb,
year = 2025,
publisher = {EDF (Électricité de France)},
version = {1.0},
doi = {10.5281/zenodo.13777859},
url = {https://doi.org/10.5281/zenodo.13777859},
}