The use of 3D models for the documentation and dissemination of cultural and archaeological heritage is widespread today. Nevertheless, to provide useful 3D data, it is important to associate semantic information that can help operators understand the heritage. This study aims at carrying out an optimal, repeatable and reliable segmentation procedure to manage various types of 3D survey data and associate them with heterogeneous information and attributes to characterize and describe a surveyed object. The developed method starts from 2D supervised machine learning segmentation of orthoimages or UV maps and then projects the segmentation results on the 3D data.
Supervised segmentation of 3D cultural heritage / Grilli, Eleonora; Dininno, Domenica; Marsicano, Lucia; Petrucci, Giulio; Remondino, Fabio. - ELETTRONICO. - (2018), pp. 487-494. (Intervento presentato al convegno 2018 3rd Digital Heritage International Congress (DigitalHERITAGE) held jointly with 2018 24th International Conference on Virtual Systems & Multimedia (VSMM 2018) tenutosi a San Francisco, USA nel 26-30 October, 2018) [10.1109/DigitalHeritage.2018.8810107].
Supervised segmentation of 3D cultural heritage
Grilli, Eleonora
;Remondino, Fabio
2018
Abstract
The use of 3D models for the documentation and dissemination of cultural and archaeological heritage is widespread today. Nevertheless, to provide useful 3D data, it is important to associate semantic information that can help operators understand the heritage. This study aims at carrying out an optimal, repeatable and reliable segmentation procedure to manage various types of 3D survey data and associate them with heterogeneous information and attributes to characterize and describe a surveyed object. The developed method starts from 2D supervised machine learning segmentation of orthoimages or UV maps and then projects the segmentation results on the 3D data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
