Version 1
: Received: 17 June 2024 / Approved: 17 June 2024 / Online: 18 June 2024 (12:15:54 CEST)
How to cite:
Zhang, K.; Fassi, F. Advancements in AI-Driven 3D Reality Capture: Transforming Architectural Digitisation and Modelling. Preprints2024, 2024061162. https://doi.org/10.20944/preprints202406.1162.v1
Zhang, K.; Fassi, F. Advancements in AI-Driven 3D Reality Capture: Transforming Architectural Digitisation and Modelling. Preprints 2024, 2024061162. https://doi.org/10.20944/preprints202406.1162.v1
Zhang, K.; Fassi, F. Advancements in AI-Driven 3D Reality Capture: Transforming Architectural Digitisation and Modelling. Preprints2024, 2024061162. https://doi.org/10.20944/preprints202406.1162.v1
APA Style
Zhang, K., & Fassi, F. (2024). Advancements in AI-Driven 3D Reality Capture: Transforming Architectural Digitisation and Modelling. Preprints. https://doi.org/10.20944/preprints202406.1162.v1
Chicago/Turabian Style
Zhang, K. and Francesco Fassi. 2024 "Advancements in AI-Driven 3D Reality Capture: Transforming Architectural Digitisation and Modelling" Preprints. https://doi.org/10.20944/preprints202406.1162.v1
Abstract
3D reality capturing has demonstrated increased efficiency and consistently accurate outcomes in architectural digitisation. Nevertheless, despite advancements in data collecting, 3D reality capturing 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 to the digitisation process and its subsequent utilisation. This article aims to examine the potential outcomes of integrating AI technology into the field of 3D reality-capturing, with a particular focus on its use in architectural 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. The 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.
Keywords
digitalization; artificial intelligence; 3D modelling; object detection; semantic segmentation; machine learning; deep learning
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
