remote sensing Article Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage Roberto Pierdicca 1 , Marina Paolanti 2,* , Francesca Matrone 3 , Massimo Martini 2 , Christian Morbidoni 2 , Eva Savina Malinverni 1 , Emanuele Frontoni 2 and Andrea Maria Lingua 3 1 Dipartimento di Ingegneria Civile, Edile e dell’Architettura, Università Politecnica delle Marche, 60100 Ancona, Italy;
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[email protected] (E.S.M.) 2 Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, 60100 Ancona, Italy;
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[email protected] (E.F.) 3 Dipartimento di Ingegneria dell’Ambiente, del Territorio e delle Infrastrutture, Politecnico di Torino, 10129 Torino, Italy;
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[email protected] (A.M.L.) * Correspondence:
[email protected] Received: 27 February 2020; Accepted: 16 March 2020; Published: 20 March 2020 Abstract: 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.