A Hybrid Bottom-up Top-down AI Method for Improved Digital Twinning of the Buildings Using Point Cloud Data and RGB Images

Abstract: Digital twins have become transformative tools in design and operations, providing critical capabilities for real-time monitoring and management of building assets. However, creating high-quality digital building models required for the digital twinning of the built environment on a large scale remains challenging and requires significant human effort. This paper introduces an AI-based end-to-end automatic procedure for the creation of digital building models using point clouds and RGB images. The results demonstrate the effectiveness of the proposed method across multiple case studies, achieving an average accuracy of approximately 7 cm in estimating the parameters of the building’s structural and opening elements.