用3D高斯点阵和视觉模型构建产线数字孪生，实时追踪与分析生产指标。
PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models
- 融合摄像头与传感器数据，用3D高斯点阵重建产线空间结构。
- 实现产线设备实时追踪，准确提取可用率、性能等关键指标。
- 开源框架+医药产线实测，适合智能制造与数字孪生研究者使用。
我们提出PerfCam，一个开源的概念验证（PoC）数字孪生框架，结合相机与传感器数据，利用3D高斯点阵和计算机视觉模型，实现工业产线的数字孪生、物体追踪及关键绩效指标（KPI）提取。通过3D重建与卷积神经网络（CNN），PerfCam提供半自动化物体追踪与空间映射能力，生成可实时反映产线可用率、性能、综合设备效率（OEE）及输送带速率等指标的数字孪生体。我们在制药行业的真实测试产线上进行了实际部署验证，并公开了一个数据集以支持该领域的进一步研究与开发。结果表明，PerfCam能通过精确的数字孪生能力提供可操作的洞察，证明其在智能制造环境中构建可用数字孪生体与提取运营分析数据方面的有效性。
原文摘要 · Abstract (English)
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.
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