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Article

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

1
Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
2
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Via Sommarive 18, 38123 Trento, Italy
3
Department of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche 12, 60100 Ancona, Italy
4
Department of Construction, Civil Engineering and Architecture (DICEA), Università Politecnica delle Marche, Via Brecce Bianche, 60100 Ancona, Italy
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 535; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090535
Received: 29 July 2020 / Revised: 19 August 2020 / Accepted: 25 August 2020 / Published: 7 September 2020
(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)
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. View Full-Text
Keywords: classification; semantic segmentation; digital cultural heritage; point clouds; machine learning; deep learning classification; semantic segmentation; digital cultural heritage; point clouds; machine learning; deep learning
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MDPI and ACS Style

Matrone, F.; Grilli, E.; Martini, M.; Paolanti, M.; Pierdicca, R.; Remondino, F. Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation. ISPRS Int. J. Geo-Inf. 2020, 9, 535. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090535

AMA Style

Matrone F, Grilli E, Martini M, Paolanti M, Pierdicca R, Remondino F. Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation. ISPRS International Journal of Geo-Information. 2020; 9(9):535. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090535

Chicago/Turabian Style

Matrone, Francesca; Grilli, Eleonora; Martini, Massimo; Paolanti, Marina; Pierdicca, Roberto; Remondino, Fabio. 2020. "Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation" ISPRS Int. J. Geo-Inf. 9, no. 9: 535. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090535

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