# Seeking a Definition of Digital Twins for Construction and Infrastructure Management
## 3 Digital Twins Current State of the Art
### Digital Twin Development Frameworks
This design allows for the addition, replacement, and connection of modules without disrupting the entire system, making it highly adaptable to various applications. OpenTwins also employs a container-based structure using Docker and Kubernetes, ensuring the efficient management, portability, and execution of the platform. In the context of smart cities, OpenTwins can manage and optimise urban infrastructure components, such as street lighting, traffic signals, and waste management systems. By creating DTs for these elements, cities can achieve real-time monitoring, predictive maintenance, and operational optimisation, resulting in improved service reliability and reduced energy consumption. OpenTwins' compositional approach also supports the creation of modular and flexible DTs, which can be reused and distributed across different devices. This capability is particularly beneficial for generating detailed and comprehensive analyses at both the individual and collective levels, significantly enhancing decision-making processes. PhotoScene [34] is an open-source framework designed to convert images into detailed 3D scenes utilising advanced algorithms and pipelines. Integrating segmentation and material estimation tools, such as MaskFormer and MATch, PhotoScene excels in visualisation and modelling, making it particularly adept at creating high-quality, photorealistic DTs. This framework's focus extends beyond 3D geometry and scene layout to include material properties and lighting, ensuring a comprehensive and realistic DT representation. In the context of construction and BIM, PhotoScene offers significant advantages. It facilitates the creation of detailed and realistic 3D models from images, which are crucial for visualising and planning construction projects. By capturing high-quality materials and lighting, PhotoScene enables the creation of DTs that can be employed in various applications, such as augmented reality, photorealistic rendering, and simulation training. For instance, architects and construction managers can utilise these photorealistic DTs to simulate different lighting conditions and material appearances, thereby enhancing decision-making and project presentations. The framework's ability to convert coarse 3D models and images into high-resolution, fully relightable 3D scenes is particularly beneficial for BIM. This capability allows for accurate visual representations of buildings and interiors, which can be rendered from novel viewpoints and under different lighting conditions. This is essential for planning and visualising the impact of design changes, material choices, and lighting setups on the final appearance of a construction project. PhotoScene's integration with segmentation and material estimation tools, like MaskFormer and MATch, enhances its ability to create realistic and detailed 3D scenes. MaskFormer assists in segmenting the image into distinct material regions, while MATch provides procedural material graphs that represent photorealistic and resolution-independent materials. These tools enable PhotoScene to accurately capture and replicate the material properties and lighting conditions of the input images, resulting in high-quality DTs. One notable application of PhotoScene in construction and BIM is its use in converting indoor scene photographs into detailed 3D models. The framework can process input images of a scene along with coarsely aligned CAD geometry to build a photorealistic DT featuring high-quality materials and lighting. This DT can be re-rendered under arbitrary viewpoints and lighting conditions, providing a flexible and powerful tool for visualising and analysing construction projects. For example, a study employing PhotoScene demonstrated its ability to create high-quality, fully relightable 3D scenes from images of indoor environments. The framework was evaluated on objects and layout reconstructions from datasets, like ScanNet, SUN RGB-D, and stock photographs, showing that it could reconstruct scenes with high accuracy and photorealism.