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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2504.18165 |
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| _version_ | 1866909614196391936 |
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| author | Khan, Michel Gokan Guarese, Renan Johnson, Fabian Wang, Xi Vincent Bergman, Anders Edvinsson, Benjamin Romero, Mario Vachier, Jérémy Kronqvist, Jan |
| author_facet | Khan, Michel Gokan Guarese, Renan Johnson, Fabian Wang, Xi Vincent Bergman, Anders Edvinsson, Benjamin Romero, Mario Vachier, Jérémy Kronqvist, Jan |
| contents | We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. By utilizing 3D reconstruction and Convolutional Neural Networks (CNNs), PerfCam offers a semi-automated approach to object tracking and spatial mapping, enabling digital twins that capture real-time KPIs such as availability, performance, Overall Equipment Effectiveness (OEE), and rate of conveyor belts in the production line. We validate the effectiveness of PerfCam through a practical deployment within realistic test production lines in the pharmaceutical industry and contribute an openly published dataset to support further research and development in the field. The results demonstrate PerfCam's ability to deliver actionable insights through its precise digital twin capabilities, underscoring its value as an effective tool for developing usable digital twins in smart manufacturing environments and extracting operational analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models Khan, Michel Gokan Guarese, Renan Johnson, Fabian Wang, Xi Vincent Bergman, Anders Edvinsson, Benjamin Romero, Mario Vachier, Jérémy Kronqvist, Jan Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. By utilizing 3D reconstruction and Convolutional Neural Networks (CNNs), PerfCam offers a semi-automated approach to object tracking and spatial mapping, enabling digital twins that capture real-time KPIs such as availability, performance, Overall Equipment Effectiveness (OEE), and rate of conveyor belts in the production line. We validate the effectiveness of PerfCam through a practical deployment within realistic test production lines in the pharmaceutical industry and contribute an openly published dataset to support further research and development in the field. The results demonstrate PerfCam's ability to deliver actionable insights through its precise digital twin capabilities, underscoring its value as an effective tool for developing usable digital twins in smart manufacturing environments and extracting operational analytics. |
| title | PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.18165 |