  Digital twins are meant to bridge the gap between real-world physical systems
and virtual representations. Both stand-alone and descriptive digital twins
incorporate 3D geometric models, which are the physical representations of
objects in the digital replica. Digital twin applications are required to
rapidly update internal parameters with the evolution of their physical
counterpart. Due to an essential need for having high-quality geometric models
for accurate physical representations, the storage and bandwidth requirements
for storing 3D model information can quickly exceed the available 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 conducts feature extraction from 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.
