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Geosciences 2017, 7(4), 96; doi:10.3390/geosciences7040096

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

1
Geomatics Unit, University of Liège (ULiege), Quartier Agora, Allée du six Août, 19, 4000 Liège, Belgium
2
Institute of civilizations (Arts and Letters), University of Louvain (UCL), Rue du Marathon 3, 1348 Louvain-la-Neuve, Belgium
*
Author to whom correspondence should be addressed.
Received: 20 July 2017 / Revised: 22 September 2017 / Accepted: 25 September 2017 / Published: 30 September 2017
(This article belongs to the Special Issue Remote Sensing and Geosciences for Archaeology)
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Abstract

Digital investigations of the real world through point clouds and derivatives are changing how curators, cultural heritage researchers and archaeologists work and collaborate. To progressively aggregate expertise and enhance the working proficiency of all professionals, virtual reconstructions demand adapted tools to facilitate knowledge dissemination. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. In this paper, we review the state of the art of point cloud integration within archaeological applications, giving an overview of 3D technologies for heritage, digital exploitation and case studies showing the assimilation status within 3D GIS. Identified issues and new perspectives are addressed through a knowledge-based point cloud processing framework for multi-sensory data, and illustrated on mosaics and quasi-planar objects. A new acquisition, pre-processing, segmentation and ontology-based classification method on hybrid point clouds from both terrestrial laser scanning and dense image matching is proposed to enable reasoning for information extraction. Experiments in detection and semantic enrichment show promising results of 94% correct semantization. Then, we integrate the metadata in an archaeological smart point cloud data structure allowing spatio-semantic queries related to CIDOC-CRM. Finally, a WebGL prototype is presented that leads to efficient communication between actors by proposing optimal 3D data visualizations as a basis on which interaction can grow. View Full-Text
Keywords: point cloud; data fusion; laser scanning; dense image-matching; feature extraction; classification; knowledge integration; cultural heritage; ontology point cloud; data fusion; laser scanning; dense image-matching; feature extraction; classification; knowledge integration; cultural heritage; ontology
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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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MDPI and ACS Style

Poux, F.; Neuville, R.; Van Wersch, L.; Nys, G.-A.; Billen, R. 3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects. Geosciences 2017, 7, 96.

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