Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation
Datos Bibliográficos
| ID | 22033843 |
|---|---|
| Autores | Francesca Matrone (0000-0002-9160-1674, Politecnico di Torino, autor de correspondencia), Eleonora Grilli (0000-0003-3400-9364, Fondazione Bruno Kessler), Massimo Martini (0000-0002-1855-9334, Marche Polytechnic University), Marina Paolanti (0000-0002-5523-7174, Marche Polytechnic University), Roberto Pierdicca (0000-0002-9160-834X, Marche Polytechnic University), Fabio Remondino (0000-0001-6097-5342, Fondazione Bruno Kessler) |
| Año | 2020 |
| Volumen | 9 |
| Número | 9 |
| Páginas | 535 |
| Fecha de publicación | 2020-09-07 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | ISPRS International Journal of Geo-Information (JOURNAL) |
| Identificadores de la revista | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Editorial | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9090535 |
| OpenAlex | W3083248912 |
| Idioma | EN |
| Citas recibidas | 23 |
| Referencias citadas | 37 |
In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented
Archaeology · Cartography · Cultural heritage · Deep learning · Geography · Machine learning · Photogrammetry · Point cloud · Segmentation · 3D Surveying and Cultural Heritage · Archaeological Research and Protection · Computer Science · Mathematics · Remote Sensing and LiDAR Applications · Artificial Intelligence
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Beyond scenic views
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A Multilevel Multiresolution Machine Learning Classification Approach
Artificial Intelligence at the Interface between Cultural Heritage and Photography
Novel Paradigms in the Cultural Heritage Digitization with Self and Custom-Built Equipment
Under-Canopy Archaeological Mapping Using LiDAR Data and AI Methods
Digital Innovation for the Documentation, Management, and Fruition of Cultural Heritage
Data Quality, Semantics, and Classification Features
Transforming Architectural Digitisation
Semi-automatic classification of digital heritage on the Aïoli open source 2D/3D annotation platform via machine learning and deep learning
Automatic generation of synthetic heritage point clouds
Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds
People and places
Using Topic Modelling to Reassess Heritage Values from a People-centred Perspective
A machine learning-based prediction model for architectural heritage
EnsArtNet
An artificial neural network framework for classifying the style of cypriot hybrid examples of built heritage in 3D
Semantic modelling and HBIM
| Obras citantes distintas | 23 |
|---|---|
| Citas por año | 3,83 |
| Intervalo de citas | 2020 - 2026 (7) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 22 |