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.
View on arXiv@article{khan2025_2504.18165,
title={ PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models },
author={ Michel Gokan Khan and Renan Guarese and Fabian Johnson and Xi Vincent Wang and Anders Bergman and Benjamin Edvinsson and Mario Romero and Jérémy Vachier and Jan Kronqvist },
journal={arXiv preprint arXiv:2504.18165},
year={ 2025 }
}