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Open AccessArticle

Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

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Dipartimento di Ingegneria Civile, Edile e dell’Architettura, Università Politecnica delle Marche, 60100 Ancona, Italy
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Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, 60100 Ancona, Italy
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Dipartimento di Ingegneria dell’Ambiente, del Territorio e delle Infrastrutture, Politecnico di Torino, 10129 Torino, Italy
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1005; https://doi.org/10.3390/rs12061005
Received: 27 February 2020 / Revised: 11 March 2020 / Accepted: 16 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Point Cloud Processing in Remote Sensing)
In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach. View Full-Text
Keywords: classification; semantic segmentation; Digital Cultural Heritage; Point Clouds; Deep Learning classification; semantic segmentation; Digital Cultural Heritage; Point Clouds; Deep Learning
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Pierdicca, R.; Paolanti, M.; Matrone, F.; Martini, M.; Morbidoni, C.; Malinverni, E.S.; Frontoni, E.; Lingua, A.M. Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage. Remote Sens. 2020, 12, 1005.

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