Linking Abstract Digital Twin Data to 3D Models: A Configurable Approach for Real-Time Adaptation

This paper presents a novel approach to linking abstract digital twin data with 3D visualization models for real-time adaptation. Traditional digital twin visualization solutions often rely on proprietary tools or platform-specific designs, limiting flexibility and scalability. In contrast, our proposed approach introduces a configurable framework that decouples digital twin data from specific development environments, enabling seamless integration across various platforms. By using a JSON-based behavior configuration, dynamic data from an abstract digital twin can be visualized in real-time, allowing 3D models to adapt automatically to live updates without extensive reconfiguration. The effectiveness of this approach is demonstrated through its implementation in the FastGate project, which focuses on autonomous airport operations. The results show that this solution is flexible, scalable, and applicable across multiple development environments, supporting a wide range of use cases beyond the scope of the FastGate scenario.