An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910024697118720 |
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| author | Armangeon, Flavien Ehret, Thibaud Meinhardt-Llopis, Enric von Gioi, Rafael Grompone Thibault, Guillaume Petit, Marc Facciolo, Gabriele |
| author_facet | Armangeon, Flavien Ehret, Thibaud Meinhardt-Llopis, Enric von Gioi, Rafael Grompone Thibault, Guillaume Petit, Marc Facciolo, Gabriele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15584 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment Armangeon, Flavien Ehret, Thibaud Meinhardt-Llopis, Enric von Gioi, Rafael Grompone Thibault, Guillaume Petit, Marc Facciolo, Gabriele Computer Vision and Pattern Recognition 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. |
| title | An Industrial Dataset for Scene Acquisitions and Functional Schematics Alignment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.15584 |