Model for Semantically Rich Point Cloud Data

Poux, Florent; Neuville, Romain; Hallot, Pierre; Billen, Roland

doi:10.5194/isprs-annals-iv-4-w5-107-2017

Cited by 17 publications

(25 citation statements)

References 22 publications

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“…The proposed methodology (described in Section 3) was as general as possible to be extended to other use cases, at the object and local scales. It provides a potential solution for bringing intelligence to spatial data, specifically point clouds as seen in [104]. For example, we tested a point cloud from dense-image matching captured in Denmark (Ny-Calsberg Museum) and processed using Bentley ContextCapture (Figure 17).…”

Section: Discussionmentioning

confidence: 99%

“…The characterization (knowledge representation and data modelling) in Figure 5 is a Level-2 domain meta-model, that can plug to a Smart Point Cloud structure [104]. The general idea is that different hierarchical levels of abstraction are constituted to avoid overlapping with existing models and to enhance the flexibility and opening to all possible formalized structure.…”

Section: Knowledge-based Detection and Classificationmentioning

confidence: 99%

“…Once extracted semantics have been successfully linked to the spatial information, we address structuring for interaction purposes. The data structuration is made in regard to [104]. The main idea is that the structure is decomposed in three meta-models acting at three different conceptual levels to efficiently manage massive point cloud data (and by extension any complex 3D data) while integrating semantics coherently.…”

Section: Knowledge-based Detection and Classificationmentioning

confidence: 99%

“…The Level-1 is an interface between the level-0 and the level 2 (specific domain-based knowledge). As such, the data integration methodology relies on incorporating the point cloud data in the Smart Point Cloud data structure [104] in regard to the workflows described in Section 3. The structuration therefore follows the object decomposition, where points of each object are grouped together to form world objects (i.e., Independent Tesserae) once concepts and meaning have been linked.…”

Section: Knowledge-based Detection and Classificationmentioning

confidence: 99%

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

Poux

Neuville

Wersch³

et al. 2017

Geosciences

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Knowledge-based Detection and Classificationmentioning

confidence: 99%

Section: Knowledge-based Detection and Classificationmentioning

confidence: 99%

Section: Knowledge-based Detection and Classificationmentioning

confidence: 99%

See 2 more Smart Citations

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

Poux

Neuville

Wersch³

et al. 2017

Geosciences

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“…Many experiments were carried out about the segmentation of heritage 3D data at different scales (Manfredini et al, 2008;Barsanti et al, 2017;Cipriani et al, 2017;Poux et al, 2017). Some works aim to define a procedure for the integration of archaeological 3D models with BIM (Saygi et al 2013;De Luca et al 2014).…”

Section: Related Workmentioning

confidence: 99%

From 2d to 3d Supervised Segmentation and Classification for Cultural Heritage Applications

Grilli

Dininno

Petrucci

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The digital management of architectural heritage information is still a complex problem, as a heritage object requires an integrated representation of various types of information in order to develop appropriate restoration or conservation strategies. Currently, there is extensive research focused on automatic procedures of segmentation and classification of 3D point clouds or meshes, which can accelerate the study of a monument and integrate it with heterogeneous information and attributes, useful to characterize and describe the surveyed object. The aim of this study is to propose an optimal, repeatable and reliable procedure to manage various types of 3D surveying data and associate them with heterogeneous information and attributes to characterize and describe the surveyed object. In particular, this paper presents an approach for classifying 3D heritage models, starting from the segmentation of their textures based on supervised machine learning methods. Experimental results run on three different case studies demonstrate that the proposed approach is effective and with many further potentials.

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Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

Berger

Doherty

Rudol

et al. 2021

Auton. Intell. Syst.

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In the context of collaborative robotics, distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans. We describe this as the creation of Hastily Formed Knowledge Networks (HFKNs). The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans. The information collected ranges from low-level sensor data to high-level semantic knowledge, the latter represented in part as RDF Graphs. The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents. This is done through the distributed synchronization of RDF Graphs shared between agents. High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members. The system is empirically validated and complexity results of the proposed algorithms are provided. Additionally, a field robotics case study is described, where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.

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Model for Semantically Rich Point Cloud Data

Cited by 17 publications

References 22 publications

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

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

From 2d to 3d Supervised Segmentation and Classification for Cultural Heritage Applications

Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

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