Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation

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 b...

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Veröffentlicht in:ISPRS international journal of geo-information Jg. 9; H. 9; S. 535
Hauptverfasser: Matrone, Francesca, Grilli, Eleonora, Martini, Massimo, Paolanti, Marina, Pierdicca, Roberto, Remondino, Fabio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2020
Schlagworte:
Algorithms
Annotations
Architectural elements
Architecture
Artificial intelligence
Case studies
Classification
computer vision
Cultural heritage
Cultural resources
data collection
Datasets
Deep learning
digital cultural heritage
digital images
Historic sites
image analysis
Image segmentation
Lasers
Learning algorithms
lidar
Machine learning
Masonry
Methods
Photogrammetry
point clouds
Semantic segmentation
Semantics
Three dimensional models
Italy
ISSN:2220-9964, 2220-9964
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Zusammenfassung: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.
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ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi9090535