Transforming Architectural Digitisation
Advancements in AI-Driven 3D Reality-Based Modelling
Bibliographic Data
| ID | 13125507 |
|---|---|
| Authors | Kai Zhang (0000-0002-7401-2931, Politecnico di Milano, corresponding author), Francesco Fassi (0000-0001-6555-8310, Politecnico di Milano) |
| Year | 2025 |
| Volume | 8 |
| Issue | 2 |
| Pages | 81-81 |
| Publication date | 2025-02-18 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Heritage (JOURNAL) |
| Journal identifiers | ISSN: 2571-9408 • E-ISSN: 2571-9408 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/heritage8020081 |
| OpenAlex | W4407677919 |
| Language | EN |
| Citations received | 3 |
| References cited | 112 |
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
Computer graphics (images · Data science · 3D Modeling in Geospatial Applications · 3D Surveying and Cultural Heritage · Architecture and Computational Design · Computer Science
Faster R-CNN
Fully convolutional networks for semantic segmentation
SSD
ImageNet
Mask R-CNN
Distinctive Image Features from Scale-Invariant Keypoints
Microsoft Coco
You Only Look Once
Machine Learning Generalisation across Different 3D Architectural Heritage
Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation
A Multilevel Multiresolution Machine Learning Classification Approach
An object detection approach for detecting damages in heritage sites using 3-D point clouds and 2-D visual data
| Citations per year | 3 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |