Digital Investigations of an Archaeological Smart Point Cloud: A Real Time Web-Based Platform to Manage the Visualisation of Semantical Queries

Wersch³

et al. 2017

Geosciences

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.

Section: Resultsmentioning

confidence: 99%

“…By a simple interaction with the GUI, the users can access and share a common viewpoint result of a semantic query. Figure 14 presents the optimal viewpoint for two classes of tesserae similarly to [12]. maximum flexibility and extensibility in regard to the 4 prerequisites of digital archaeology as defined in [5,6].…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wersch³

et al. 2017

Geosciences

“…Besides, projecting, unwrapping and processing raster images would prove useful to speed up computation and leverage existing libraries. First experiments were conducted and results are encouraging: a prototype that directly allows semantic extraction and visualisation of pertinent information for the end users was proposed in (Poux et al, 2017). Future work will also include region growing algorithms based on the center of previously extracted segments.…”

Section: Discussionmentioning

confidence: 99%

Point Cloud Classification of Tesserae From Terrestrial Laser Data Combined With Dense Image Matching for Archaeological Information Extraction

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Billen

2017

Self Cite

ABSTRACT:Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the highdensity point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

“…Therefore, point grouping rules influence the performances of information extractions. While this is done spatially in the prototype via a voxel-based topological analysis as in (Poux et al, 2017), other criteria will be explored. Each detected element is parsed in semantic patches arbitrary subdivided based on a point maximum number of 800 points and directly integrated in PostgreSQL.…”

Section: Prototype and Discussionmentioning

confidence: 99%

Model for Semantically Rich Point Cloud Data

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Hallot

et al. 2017

Self Cite

ABSTRACT:This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.