Transforming Architectural Digitisation: Advancements in AI-Driven 3D Reality-Based Modelling

The capture of 3D reality has demonstrated increased efficiency and consistently accurate outcomes in architectural digitisation. Nevertheless, despite advancements in data collection, 3D reality-based modelling still lacks full automation, especially in the post-processing and modelling phase. Artificial intelligence (AI) has been a significant focus, especially in computer vision, and tasks such as image classification and object recognition might be beneficial for the digitisation process and its subsequent utilisation. This study aims to examine the potential outcomes of integrating AI technology into the field of 3D reality-based modelling, with a particular focus on its use in architecture and cultural-heritage scenarios. The main methods used for data collection are laser scanning (static or mobile) and photogrammetry. As a result, image data, including RGB-D data (files containing both RGB colours and depth information) and point clouds, have become the most common raw datasets available for object mapping. This study comprehensively analyses the current use of 2D and 3D deep learning techniques in documentation tasks, particularly downstream applications. It also highlights the ongoing research efforts in developing real-time applications with the ultimate objective of achieving generalisation and improved accuracy.