Publications of Florent Poux
Bookmark and Share    
Full Text
See detailAutomatic extraction and management of semantics within point cloud data
Poux, Florent ULiege

Conference (2020, May 28)

In the context of 3D point clouds, reality capture and artificial intelligence, the presentation covers exciting research potential of point clouds, semantics and unsupervised frameworks.

Detailed reference viewed: 65 (3 ULiège)
Full Text
See detailHow to represent 3D Data?
Poux, Florent ULiege

in Towards Data Science (2020)

The 3D datasets in our computerized ecosystem — of which an increasing number comes directly from reality capture devices — are found in different forms that vary in both the structure and the properties ... [more ▼]

The 3D datasets in our computerized ecosystem — of which an increasing number comes directly from reality capture devices — are found in different forms that vary in both the structure and the properties. Interestingly, they can be somehow mapped with success to point clouds thanks to its canonical nature. This article gives you the main 3D data representations modes to choose from when bindings point clouds to your application [less ▲]

Detailed reference viewed: 34 (8 ULiège)
Full Text
See detailThe Smart Point Cloud Model: Integration of point intelligence
Poux, Florent ULiege

Conference (2019, December 05)

Point cloud acquisition and processing workflows are usually application-dependent following a classic progression from data gathering to deliverable creation. While the collection step may be specific to ... [more ▼]

Point cloud acquisition and processing workflows are usually application-dependent following a classic progression from data gathering to deliverable creation. While the collection step may be specific to the sensor at hands, point-cloud-as-a-deliverable upsurges, becoming one de-facto choice for many industries. This task-oriented scenario mainly considers these as a spatial reference – which is used by experts to create other deliverables – thus being a project’s closest link to reality. It brings accurate real-world information which could allow decision-making based on digital-reality instead of interpreted or not up-to-date information. However, there are several considerations to address for a suitable integration. Point clouds are often very large depending on how much data is collected – usually in the realms of Gigabytes, if not Terabytes – and are destined to be archived as a reusable support to create new type of data and products. This can lead to a dead-end with exponential storage needs, incompatibility between outputs, loss of information and complicated collaboration. These practices also show limited to no attempt to generalize a framework which could in turn play as a common ground for further interoperability and generalization. This lack is counterproductive and could lead in term to a chaotic data repartition among actors and worsen the dependency to several outsourced service, each aiming an application independently. This primarily emphasize a strong need to study interoperable scenarios in which one point cloud could be used by many users from different domains, each having a different need (E.g. the object of interest can be a building or only the roof of this building). This will in turn introduce new constraints at the acquisition level to define the needed exhaustivity of the 3D representation for use with reasoning engines. Of course, this serialize additional challenges for interconnecting processes and insuring a compatibility with the different sources, volumes and other data-driven parameters. Secondly, robotics research has made a leap forward providing autonomous 3D recording systems, where we obtain a 3D point cloud of environments with no human intervention. Of course, following this idea to develop autonomous surveying demands that the data can be used for decision-making. The collected point cloud without context does not permit to take a valid decision, and the knowledge of experts is needed to extract the necessary information and to creates a viable data support for decision-making. Automating this process for fully autonomous cognitive decision systems is very tempting but poses many challenges mainly link to Knowledge Extraction (KE), Knowledge Integration (KI) and Knowledge Representation (KR) from point cloud. Therefore, point cloud structuration must be specifically designed to allow the computer to use it as a base for information extraction using reasoning and agent-based systems. Interoperable approaches which permits several actors to leverage one common information system (E.g. Facility Management 4.0) based on a digital twin is a great exploration motor. In this continuum, the presentation feeds a broader reflexion to go from a human-centered process to an autonomous workflow which highlights a need to improve automation, data management and interaction to speed-up inference processes, crucial to the development of point clouds in 3D capture workflows. The presentation primarily aims at providing all the necessary information for the development of an infrastructure: The Smart Point Cloud (SPC). It permits to handle point cloud data, manage heterogeneity, process and group points that retain a relationship regarding a specific domain ontology that allow to query and reason for decision-making tools including smart modelling. The resulting implementation of the SPC is based on new meta-models that permit to structure the information (3D geometry and semantics) and leverage available knowledge for accessing decision-making support tools and reasoning capabilities. At the frontier between a point cloud GIS system and a spatial infrastructure for agent-based decision support systems, its flexibility allows to evolve with future developments using artificial intelligence and new machine learning approaches. The proposed modular infrastructure includes Knowledge Discovery processes with Knowledge Integration and Knowledge Representation as ontologies, proving efficient context-specific adaptation. [less ▲]

Detailed reference viewed: 80 (7 ULiège)
Full Text
See detailSemantic enrichment of point cloud by automatic extraction and enhancement of 360° panoramas
Tabkha, A.; Hajji, R.; Billen, Roland ULiege et al

in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2019, November 29), XLII-2(W17), 355-362

The raw nature of point clouds is an important challenge for their direct exploitation in architecture, engineering and construction applications. Particularly, their lack of semantics hinders their ... [more ▼]

The raw nature of point clouds is an important challenge for their direct exploitation in architecture, engineering and construction applications. Particularly, their lack of semantics hinders their utility for automatic workflows (Poux, 2019). In addition, the volume and the irregularity of the structure of point clouds makes it difficult to directly and automatically classify datasets efficiently, especially when compared to the state-of-the art 2D raster classification. Recently, with the advances in deep learning models such as convolutional neural networks (CNNs) , the performance of image-based classification of remote sensing scenes has improved considerably (Chen et al., 2018; Cheng et al., 2017). In this research, we examine a simple and innovative approach that represent large 3D point clouds through multiple 2D projections to leverage learning approaches based on 2D images. In other words, the approach in this study proposes an automatic process for extracting 360° panoramas, enhancing these to be able to leverage raster data to obtain domain-base semantic enrichment possibilities. Indeed, it is very important to obtain a rigorous characterization for use in the classification of a point cloud. Especially because there is a very large variety of 3D point cloud domain applications. In order to test the adequacy of the method and its potential for generalization, several tests were performed on different datasets. The developed semantic augmentation algorithm uses only the attributes X, Y, Z and camera positions as inputs. [less ▲]

Detailed reference viewed: 41 (8 ULiège)
Full Text
See detailCLASSIFICATION AND INTEGRATION OF MASSIVE 3D POINTS CLOUDS IN A VIRTUAL REALITY (VR) ENVIRONMENT
Kharroubi, Abderrazaq; Hajji, Rafika; Billen, Roland ULiege et al

in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2019, November 29), XLII-2(W17), 165-171

With the increasing volume of 3D applications using immersive technologies such as virtual, augmented and mixed reality, it is very interesting to create better ways to integrate unstructured 3D data such ... [more ▼]

With the increasing volume of 3D applications using immersive technologies such as virtual, augmented and mixed reality, it is very interesting to create better ways to integrate unstructured 3D data such as point clouds as a source of data. Indeed, this can lead to an efficient workflow from 3D capture to 3D immersive environment creation without the need to derive 3D model, and lengthy optimization pipelines. In this paper, the main focus is on the direct classification and integration of massive 3D point clouds in a virtual reality (VR) environment. The emphasis is put on leveraging open-source frameworks for an easy replication of the findings. First, we develop a semi-automatic segmentation approach to provide semantic descriptors (mainly classes) to groups of points. We then build an octree data structure leveraged through out-of-core algorithms to load in real time and continuously only the points that are in the VR user's field of view. Then, we provide an open-source solution using Unity with a user interface for VR point cloud interaction and visualisation. Finally, we provide a full semantic VR data integration enhanced through developed shaders for future spatio-semantic queries. We tested our approach on several datasets of which a point cloud composed of 2.3 billion points, representing the heritage site of the castle of Jehay (Belgium). The results underline the efficiency and performance of the solution for visualizing classifieds massive point clouds in virtual environments with more than 100 frame per second. [less ▲]

Detailed reference viewed: 159 (35 ULiège)
Full Text
See detail3D Point Clouds on the Web: A cost-efficient support for decision making
Poux, Florent ULiege

Conference (2019, October 29)

This paper presents a 3D web platform and the main reflexion that initiated the project: Flyvast. This real-time 3D media permits to manage, Point Cloud Data, Raster Data and vector Data. Moreover, it ... [more ▼]

This paper presents a 3D web platform and the main reflexion that initiated the project: Flyvast. This real-time 3D media permits to manage, Point Cloud Data, Raster Data and vector Data. Moreover, it permits to work with full resolution data organised in projects. The users have the ability to collaborate, annotate, segment, classify and export any or part of datasets, as well a using it as a direct support for additional services, including external DBMS and GIS systems. The full structure is based by considering point clouds as the main spatial backbone of the different collaborations, demanding massive data structuration and indexing for a real time use over 30 fps on the web. [less ▲]

Detailed reference viewed: 55 (4 ULiège)
Full Text
See detailThe Smart Point Cloud: Structuring 3D intelligent point data
Poux, Florent ULiege

Doctoral thesis (2019)

Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. E.g., we can use such data as a reference for autonomous cars and robot’s navigation, as a layer ... [more ▼]

Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. E.g., we can use such data as a reference for autonomous cars and robot’s navigation, as a layer for floor-plan’s creation and building’s construction, as a digital asset for environment modelling and incident prediction... Applications are numerous, and potentially increasing if we consider point clouds as digital reality assets. Yet, this expansion faces technical limitations mainly from the lack of semantic information within point ensembles. Connecting knowledge sources is still a very manual and time-consuming process suffering from error-prone human interpretation. This highlights a strong need for domain-related data analysis to create a coherent and structured information. The thesis clearly tries to solve automation problematics in point cloud processing to create intelligent environments, i.e. virtual copies that can be used/integrated in fully autonomous reasoning services. We tackle point cloud questions associated with knowledge extraction – particularly segmentation and classification – structuration, visualisation and interaction with cognitive decision systems. We propose to connect both point cloud properties and formalized knowledge to rapidly extract pertinent information using domain-centered graphs. The dissertation delivers the concept of a Smart Point Cloud (SPC) Infrastructure which serves as an interoperable and modular architecture for a unified processing. It permits an easy integration to existing workflows and a multi-domain specialization through device knowledge, analytic knowledge or domain knowledge. Concepts, algorithms, code and materials are given to replicate findings and extend current applications. [less ▲]

Detailed reference viewed: 923 (49 ULiège)
Full Text
See detailVoxel-Based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods
Poux, Florent ULiege; Billen, Roland ULiege

in ISPRS International Journal of Geo-Information (2019), 8(5), 213

Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with ... [more ▼]

Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that better characterize point clusters and provide strong support to supervised or unsupervised classification. We provide different feature generalization levels to permit interoperable frameworks. First, we recommend a shape-based feature set (SF1) that only leverages the raw X, Y, Z attributes of any point cloud. Afterwards, we derive relationship and topology between voxel entities to obtain a three-dimensional (3D) structural connectivity feature set (SF2). Finally, we provide a knowledge-based decision tree to permit infrastructure-related classification. We study SF1/SF2 synergy on a new semantic segmentation framework for the constitution of a higher semantic representation of point clouds in relevant clusters. Finally, we benchmark the approach against novel and best-performing deep-learning methods while using the full S3DIS dataset. We highlight good performances, easy-integration, and high F1-score (> 85%) for planar-dominant classes that are comparable to state-of-the-art deep learning. [less ▲]

Detailed reference viewed: 259 (25 ULiège)
Full Text
See detail3D Viewpoint Management and Navigation in Urban Planning: Application to the Exploratory Phase
Neuville, Romain ULiege; Pouliot, Jacynthe; Poux, Florent ULiege et al

in Remote Sensing (2019), 11(3), 236

3D geovisualization is essential in urban planning as it assists the analysis of geospatial data and decision making in the design and development of land use and built environment. However, we noted that ... [more ▼]

3D geovisualization is essential in urban planning as it assists the analysis of geospatial data and decision making in the design and development of land use and built environment. However, we noted that 3D geospatial models are commonly visualized arbitrarily as current 3D viewers often lack of design instructions to assist end users. This is especially the case for the occlusion management in most 3D environments where the high density and diversity of 3D data to be displayed require efficient visualization techniques for extracting all the geoinformation. In this paper, we propose a theoretical and operational solution to manage occlusion by automatically computing best viewpoints. Based on user’s parameters, a viewpoint management algorithm initially calculates optimal camera settings for visualizing a set of 3D objects of interest through parallel projections. Precomputed points of view are then integrated into a flythrough creation algorithm for producing an automatic navigation within the 3D geospatial model. The algorithm’s usability is illustrated within the scope of a fictive exploratory phase for the public transport services access in the European quarter of Brussels. Eventually, the proposed algorithms may also assist additional urban planning phases in achieving their purposes. [less ▲]

Detailed reference viewed: 78 (24 ULiège)
Full Text
See detail3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture
Poux, Florent ULiege; Neuville, Romain ULiege; Nys, Gilles-Antoine ULiege et al

in Remote Sensing (2018), 10(9), 1412

3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each ... [more ▼]

3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework that leverages segmented point cloud data and domain ontologies. Our approach follows a part-to-whole conception which models a point cloud in parametric elements usable per instance and aggregated to obtain a global 3D model. We first extract analytic features, object relationships and contextual information to permit better object characterization. Then, we propose a multi-representation modelling mechanism augmented by automatic recognition and fitting from the 3D library ModelNet10 to provide the best candidates for several 3D scans of furniture. Finally, we combine every element to obtain a consistent indoor hybrid 3D model. The method allows a wide range of applications from interior navigation to virtual stores. [less ▲]

Detailed reference viewed: 86 (24 ULiège)
Full Text
See detailLa transition numérique dans le domaine du patrimoine bâti: un retour d'expériences
Billen, Roland ULiege; Jonlet, Benoît ULiege; Luczfalvy Jancsó, Andrea ULiege et al

in Bulletin de la commission royale des monuments, sites et fouilles - Tome 30 (2018)

Detailed reference viewed: 189 (30 ULiège)
Full Text
See detailA Formalized 3D Geovisualization Illustrated to Selectivity Purpose of Virtual 3D City Model
Neuville, Romain ULiege; Pouliot, Jacynthe; Poux, Florent ULiege et al

in ISPRS International Journal of Geo-Information (2018), 7(5),

Virtual 3D city models act as valuable central information hubs supporting many aspects of cities, from management to planning and simulation. However, we noted that 3D city models are still ... [more ▼]

Virtual 3D city models act as valuable central information hubs supporting many aspects of cities, from management to planning and simulation. However, we noted that 3D city models are still underexploited and believe that this is partly due to inefficient visual communication channels across 3D model producers and the end-user. With the development of a formalized 3D geovisualization approach, this paper aims to support and make the visual identification and recognition of specific objects in the 3D models more efficient and useful. The foundation of the proposed solution is a knowledge network of the visualization of 3D geospatial data that gathers and links mapping and rendering techniques. To formalize this knowledge base and make it usable as a decision-making system for the selection of styles, second-order logic is used. It provides a first set of efficient graphic design guidelines, avoiding the creation of graphical conflicts and thus improving visual communication. An interactive tool is implemented and lays the foundation for a suitable solution for assisting the visualization process of 3D geospatial models within CAD and GIS-oriented software. Ultimately, we propose an extension to OGC Symbology Encoding in order to provide suitable graphic design guidelines to web mapping services. [less ▲]

Detailed reference viewed: 77 (25 ULiège)
Full Text
See detailNumérisation 3D par lasergrammétrie et photogrammétrie: applications à la Réalité Virtuelle
Poux, Florent ULiege; Luczfalvy Jancsó, Andrea ULiege; Jonlet, Benoît ULiege et al

Conference (2018, March 08)

Alors que les copies virtuelles du monde réel ont tendance à être créées plus rapidement que jamais sous forme de nuages de points et produits dérivés, leur utilisation par tous les professionnels demande ... [more ▼]

Alors que les copies virtuelles du monde réel ont tendance à être créées plus rapidement que jamais sous forme de nuages de points et produits dérivés, leur utilisation par tous les professionnels demande de nouveaux outils pour faciliter la diffusion des connaissances. Les analyses numériques changent la manière dont les chercheurs, les archéologues et les conservateurs du patrimoine travaillent et collaborent pour combiner progressivement leur expertise grâce à une plate-forme commune. Néanmoins, l’adjonction d'information sémantique pour compléter les nuages de points est essentielle pour permettre une utilisation complète comme donnée de référence. Les concepts et les outils qui simplifient ce processus sont rares, ce qui complique la mise en commun des analyses. Différentes méthodes seront présentées permettant d'extraire des informations archéologiques aux nuages de points et créer une connexion avec les connaissances structurées sous forme d'ontologie. Les outils de communication et de visualisation 3D développés pour permettre plusieurs interactions seront illustrées sur le château de Jehay. Un partenariat a été établi entre cette association et l’Unité de Géomatique de l’Université de Liège. Il porte non seulement sur l’acquisition de données 3D mais aussi sur l’accompagnement de l’association dans ses projets de recherches archéologiques, de réhabilitation du site et de valorisation. Plusieurs campagnes de mesures ont été réalisées à différents stades de la rénovation. De nouvelles missions sont prévues notamment par des étudiants de l’Université. Outre plusieurs vidéos promotionnelles et quelques produits dérivés utiles à la rénovation, une exploitation en réalité virtuelle retient pour le moment l’attention de nos développeurs. En effet, le Château étant actuellement en grande partie fermé au public, il est prévu cet été d’offrir sur le site des visites en réalité virtuelle basées sur des levés lasergrammétriques et photogrammétriques. À plus long terme, il est prévu d’établir un système d’information spatialisée dédié au Château ; les bases conceptuelles de ce système ont déjà été dressées lors d’une récente étude. [less ▲]

Detailed reference viewed: 206 (16 ULiège)
Full Text
See detailFusion de données lasergrammétriques/photogrammétriques et techniques d'extraction d'information archéologique sur base de nuage de points 3D
Poux, Florent ULiege; Billen, Roland ULiege; Hallot, Pierre ULiege et al

Conference (2018, March 08)

While virtual copies of the real world tend to be created faster than ever in the form of point clouds and derived products, their use by all professionals requires new tools to facilitate the ... [more ▼]

While virtual copies of the real world tend to be created faster than ever in the form of point clouds and derived products, their use by all professionals requires new tools to facilitate the dissemination of knowledge. Digital analyses are changing the way researchers, archaeologists and heritage curators work and collaborate to gradually combine their expertise through a common platform. Nevertheless, the addition of semantic information to complete the point clouds is essential to allow a complete use as reference data. There are few concepts and tools that simplify this process, making it difficult to share analyses. Different methods will be presented to extract archaeological information from the point clouds and create a connection with the structured knowledge in the form of ontology. Classification and structuring procedures for reasoning from the data will be discussed. These methodologies will be illustrated at the Germigny-des-prés (France) and Château de Jehay (Belgium) sites. The communication and 3D visualization tools developed to allow several interactions will also be presented. While the Germigny-des-prés site (France) is almost exclusively dedicated to research, the same is not true for the Jehay site. This exceptional site belongs to the Province of Liège and is managed by a non-profit association. A partnership has been established between this association and the Geomatics Unit of the University of Liège. It concerns not only the acquisition of 3D data but also the support of the association in its archaeological research, site rehabilitation and enhancement projects. Several measurement campaigns were carried out at different stages of the renovation. New missions are planned, notably by students from the University. In addition to several promotional videos and a few derivative products useful for renovation, a virtual reality operation is currently attracting the attention of our developers. Indeed, the Castle is currently largely closed to the public, it is planned this summer to offer virtual reality tours based on lasergrammetric and photogrammetric surveys. In the longer term, it is planned to establish a spatialized information system dedicated to the Castle; the conceptual bases of this system have already been established during a recent study. [less ▲]

Detailed reference viewed: 130 (26 ULiège)
Full Text
See detailMODEL FOR REASONING FROM SEMANTICALLY RICH POINT CLOUD DATA
Poux, Florent ULiege; Neuville, Romain ULiege; Hallot, Pierre ULiege et al

in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2017, October 26), IV-4/W5

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 149 (27 ULiège)
Full Text
See detailTOWARDS A DECISION SUPPORT TOOL FOR 3D VISUALISATION: APPLICATION TO SELECTIVITY PURPOSE OF SINGLE OBJECT IN A 3D CITY SCENE
Neuville, Romain ULiege; Pouliot, Jacynthe; Poux, Florent ULiege et al

in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2017, October 26), IV-4/W5

This paper deals with the establishment of a comprehensive methodological framework that defines 3D visualisation rules and its application in a decision support tool. Whilst the use of 3D models grows in ... [more ▼]

This paper deals with the establishment of a comprehensive methodological framework that defines 3D visualisation rules and its application in a decision support tool. Whilst the use of 3D models grows in many application fields, their visualisation remains challenging from the point of view of mapping and rendering aspects to be applied to suitability support the decision making process. Indeed, there exists a great number of 3D visualisation techniques but as far as we know, a decision support tool that facilitates the production of an efficient 3D visualisation is still missing. This is why a comprehensive methodological framework is proposed in order to build decision tables for specific data, tasks and contexts. Based on the second-order logic formalism, we define a set of functions and propositions among and between two collections of entities: on one hand static retinal variables (hue, size, shape…) and 3D environment parameters (directional lighting, shadow, haze…) and on the other hand their effect(s) regarding specific visual tasks. It enables to define 3D visualisation rules according to four categories: consequence, compatibility, potential incompatibility and incompatibility. In this paper, the application of the methodological framework is demonstrated for an urban visualisation at high density considering a specific set of entities. On the basis of our analysis and the results of many studies conducted in the 3D semiotics, which refers to the study of symbols and how they relay information, the truth values of propositions are determined. 3D visualisation rules are then extracted for the considered context and set of entities and are presented into a decision table with a colour coding. Finally, the decision table is implemented into a plugin developed with three.js, a cross-browser JavaScript library. The plugin consists of a sidebar and warning windows that help the designer in the use of a set of static retinal variables and 3D environment parameters. [less ▲]

Detailed reference viewed: 66 (16 ULiège)
Full Text
See detail3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects
Poux, Florent ULiege; Neuville, Romain ULiege; Van Wersch, Line ULiege et al

in Geosciences (2017), 7(4), 96

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 231 (51 ULiège)
Full Text
See detailPOINT CLOUD CLASSIFICATION OF TESSERAE FROM TERRESTRIAL LASER DATA COMBINED WITH DENSE IMAGE MATCHING FOR ARCHAEOLOGICAL INFORMATION EXTRACTION
Poux, Florent ULiege; Neuville, Romain ULiege; Hallot, Pierre ULiege et al

in International Journal on Advances in Life Sciences (2017, August 16), IV-2/W2

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 ... [more ▼]

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 high-density 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. [less ▲]

Detailed reference viewed: 189 (25 ULiège)
Full Text
See detailDIGITAL INVESTIGATIONS OF AN ARCHAEOLOGICAL SMART POINT CLOUD: A REAL TIME WEB-BASED PLATFORM TO MANAGE THE VISUALISATION OF SEMANTICAL QUERIES
Poux, Florent ULiege; Neuville, Romain ULiege; Hallot, Pierre ULiege et al

in Conservation of Cultural Heritage in the Digital Era (2017, May 16)

While virtual copies of the real world tend to be created faster than ever through point clouds and derivatives, their working proficiency by all professionals’ demands adapted tools to facilitate ... [more ▼]

While virtual copies of the real world tend to be created faster than ever through point clouds and derivatives, their working proficiency by all professionals’ demands adapted tools to facilitate knowledge dissemination. Digital investigations are changing the way cultural heritage researchers, archaeologists, and curators work and collaborate to progressively aggregate expertise through one common platform. In this paper, we present a web application in a WebGL framework accessible on any HTML5-compatible browser. It allows real time point cloud exploration of the mosaics in the Oratory of Germigny-des-Prés, and emphasises the ease of use as well as performances. Our reasoning engine is constructed over a semantically rich point cloud data structure, where metadata has been injected a priori. We developed a tool that directly allows semantic extraction and visualisation of pertinent information for the end users. It leads to efficient communication between actors by proposing optimal 3D viewpoints as a basis on which interactions can grow. [less ▲]

Detailed reference viewed: 133 (19 ULiège)
Full Text
See detailSMART POINT CLOUD: DEFINITION AND REMAINING CHALLENGES
Poux, Florent ULiege; Neuville, Romain ULiege; Hallot, Pierre ULiege et al

in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2016, October 05), IV-2(W1), 119-127

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the smart point cloud. This concept arises with the ... [more ▼]

Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the smart point cloud. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure [less ▲]

Detailed reference viewed: 727 (86 ULiège)