Digital twins are ment to bridge the gap between real-world physical systems and virtual representations. Both standalone and descriptive digital twins incorporate 3D models, which are the physical representations of objects building the digital replica. Digital twin applications are required to rapidly update internal parameters with the evolution of their physical assets. Due to an essential need for high-quality models for accurate physical representations, this causes the storage and bandwidth requirements for storing 3D model information to quickly exceed storage and bandwidth capacity.
In this work, we demonstrate a novel approach to geometric change detection in the context of a digital twin. We address the issue through a combined solution of Dynamic Mode Decomposition (DMD) for motion detection, YOLOv5 for object detection, and 3D machine learning for pose estimation. DMD is applied for background subtraction, enabling detection of moving foreground objects in real-time. The video frames containing detected motion are extracted and used as input to the change detection network. The object detection algorithm YOLOv5 is applied to extract the bounding boxes of detected objects in the video frames. Furthermore, the rotational pose of each object is estimated in a 3D pose estimation network. A series of convolutional neural networks (CNNs) conducts feature extraction for images and 3D model shapes. Then, the network outputs the estimated Euler angles of the camera orientation with respect to the object in the input image. By only storing data associated with a detected change in pose, we minimize necessary storage and bandwidth requirements while still being able to recreate the 3D scene on demand. To the best of our knowledge, a similar solution has not previously been attempted in a digital twin context.