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Open AccessFeature PaperArticle

Classification of 3D Digital Heritage

1
3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Via Sommarive 18, 38121 Trento, Italy
2
Department of Architecture, Alma Mater Studiorum—University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 847; https://doi.org/10.3390/rs11070847
Received: 21 February 2019 / Revised: 23 March 2019 / Accepted: 26 March 2019 / Published: 8 April 2019
(This article belongs to the Special Issue Heritage 3D Modeling from Remote Sensing Data)
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PDF [19559 KB, uploaded 8 April 2019]
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Abstract

In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better interpret the object under study. Indeed, the high complexity of 3D data along with the large diversity of heritage assets themselves have constituted segmentation and classification methods as currently active research topics. Although machine learning methods brought great progress in this respect, few advances have been developed in relation to cultural heritage 3D data. Starting from the existing literature, this paper aims to develop, explore and validate reliable and efficient automated procedures for the classification of 3D data (point clouds or polygonal mesh models) of heritage scenarios. In more detail, the proposed solution works on 2D data (“texture-based” approach) or directly on the 3D data (“geometry-based approach) with supervised or unsupervised machine learning strategies. The method was applied and validated on four different archaeological/architectural scenarios. Experimental results demonstrate that the proposed approach is reliable and replicable and it is effective for restoration and documentation purposes, providing metric information e.g. of damaged areas to be restored. View Full-Text
Keywords: classification; segmentation; cultural heritage; machine learning; random forest classification; segmentation; cultural heritage; machine learning; random forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Grilli, E.; Remondino, F. Classification of 3D Digital Heritage. Remote Sens. 2019, 11, 847.

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