3D Digital Twin Representation of Building Indoors

The Industry 4.0 has revolutionized the way of working for many sectors especially the manufacturing, distribution and construction industries. Integration of new technologies such as cloud computing, data analytics, machine learning, artificial intelligence and internet of things into production facilities, operations and management for increased efficiency and better performance is happening rapidly which led to the rise of digital twin technology in the AEC industry. There are various studies that are carried out as a part of 3D digital representations of structures. Some of them use neural networks, rule based analysis and knowledge based approaches to generate valuable insights for the maintenance and development of each stage of the building's life cycle but this thesis uses model driven approach for building indoors model. The potential use cases of the work include crisis management, material shelving analysis, indoor navigation, path planning for robots and synthetic data generation for AI research. Due to extensive labour and time that goes into developing 3D models via sketches or third party tools, this thesis proposes an automated way of generating 3D models from 2D floorplan images through python scripts. The digital twin capabilities are introduced to the indoors model with the help of Pixar's USD technology. As a result of this approach, the generated 3D models are built in no time and accuracy to a highest level of detail is achieved for a building indoors environment.