Publications of Florent Poux
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See detailA Built Heritage Information System Based on Point Cloud Data: HIS-PC
Poux, Florent ULiege; Billen, Roland ULiege; Kasprzyk, Jean-Paul ULiege et al

in ISPRS International Journal of Geo-Information (2020), 9(10), 588

The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling ... [more ▼]

The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling, archaeological or heritage information systems now tend to propose a common framework where all the materials are managed from a central database and visualised through a 3D representation. In this research, we offer the development of a built heritage information system prototype based on a high-resolution 3D point cloud data set. The particularity of the approach is to consider a user-centred development methodology while avoiding meshing/down-sampling operations. The proposed system is initiated by a close collaboration between multi-modal users (managers, visitors, curators) and a development team (designers, developers, architects). The developed heritage information system permits the management of spatial and temporal information, including a wide range of semantics using relational along with NoSQL databases. The semantics used to describe the artifacts are subject to conceptual modelling. Finally, the system proposes a bi-directional communication with a 3D interface able to stream massive point clouds, which is a big step forward to provide a comprehensive site representation for stakeholders while minimising modelling costs. [less ▲]

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See detailUnsupervised segmentation of indoor 3D point cloud: application to object-based classification
Poux, Florent ULiege; Mattes, Christian; Kobbelt, Leif

in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2020, September 03), XLIV-4(W1-2020), 111-118

Abstract. Point cloud data of indoor scenes is primarily composed of planar-dominant elements. Automatic shape segmentation is thus valuable to avoid labour intensive labelling. This paper provides a ... [more ▼]

Abstract. Point cloud data of indoor scenes is primarily composed of planar-dominant elements. Automatic shape segmentation is thus valuable to avoid labour intensive labelling. This paper provides a fully unsupervised region growing segmentation approach for efficient clustering of massive 3D point clouds. Our contribution targets a low-level grouping beneficial to object-based classification. We argue that the use of relevant segments for object-based classification has the potential to perform better in terms of recognition accuracy, computing time and lowers the manual labelling time needed. However, fully unsupervised approaches are rare due to a lack of proper generalisation of user-defined parameters. We propose a self-learning heuristic process to define optimal parameters, and we validate our method on a large and richly annotated dataset (S3DIS) yielding 88.1\% average F1-score for object-based classification. It permits to automatically segment indoor point clouds with no prior knowledge at commercially viable performance and is the foundation for efficient indoor 3D modelling in cluttered point clouds. [less ▲]

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See detailCityJSON Building Generation from Airborne LiDAR 3D Point Clouds
Nys, Gilles-Antoine ULiege; Poux, Florent ULiege; Billen, Roland ULiege

in ISPRS International Journal of Geo-Information (2020), 9(521),

The relevant insights provided by 3D City models greatly improve Smart Cities and their management policies. In the urban built environment, buildings frequently represent the most studied and modeled ... [more ▼]

The relevant insights provided by 3D City models greatly improve Smart Cities and their management policies. In the urban built environment, buildings frequently represent the most studied and modeled features. CityJSON format proposes a lightweight and developer-friendly alternative to CityGML. This paper proposes an improvement to the usability of 3D models providing an automatic generation method in CityJSON, to ensure compactness, expressivity, and interoperability. In addition to a compliance rate in excess of 92% for geometry and topology, the generated model allows the handling of contextual information, such as metadata and refined levels of details (LoD), in a built-in manner. By breaking down the building-generation process, it creates consistent building objects from the unique source of Light Detection and Ranging (LiDAR) point clouds. [less ▲]

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See detailAutomatic 3D Buildings Compact Reconstruction from LiDAR point clouds
Nys, Gilles-Antoine ULiege; Billen, Roland ULiege; Poux, Florent ULiege

in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2020, August 12), (XLIII-B2-2020), 473-478

Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing ... [more ▼]

Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing of underlying information. The generation of consistent large-scale 3D city models from this real-world data is a major challenge. Specifically, the integration in workflows usable by decision-making scenarios demands that the data is structured, rich and exchangeable. CityGML permits new advances in terms of interoperable endeavour to use city models in a collaborative way. Efforts have led to render good-looking digital twins of cities but few of them take into account their potential use in finite elements simulations (wind, floods, heat radiation model, etc.). In this paper, we target the automatic reconstruction of consistent 3D city buildings highlighting closed solids, coherent surface junctions, perfect snapping of vertices, etc. It specifically investigates the topological and geometrical consistency of generated models from aerial LiDAR point cloud, formatted following the CityJSON specifications. These models are then usable to store relevant information and provides geometries usable within complex computations such as computational fluid dynamics, free of local inconsistencies (e.g. holes and unclosed solids). [less ▲]

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See detailMARKER-LESS MOBILE AUGMENTED REALITY APPLICATION FOR MASSIVE 3D POINT CLOUDS AND SEMANTICS
Kharroubi, Abderrazzaq ULiege; Billen, Roland ULiege; Poux, Florent ULiege

in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2020, August 12), XLIII(B2), 255261

Mobile Augmented Reality (MAR) attracts significant research and development efforts from both the industry and academia, but rarely integrate massive 3D dataset’s interactions. The emergence of dedicated ... [more ▼]

Mobile Augmented Reality (MAR) attracts significant research and development efforts from both the industry and academia, but rarely integrate massive 3D dataset’s interactions. The emergence of dedicated AR devices and powerful Software Development Kit (ARCore for android and ARKit for iOS) improves performance on mobile devices (Smartphones and tablets). This is aided by new sensor integration and advances in computer vision that fuels the development of MAR. In this paper, we propose a direct integration of massive 3D point clouds with semantics in a web-based marker-less mobile Augmented Reality (AR) application for real-time visualization. We specifically investigate challenges linked to point cloud data structure and semantic injection. Our solution consolidates some of the overarching principles of AR, of which pose estimation, registration and 3D tracking. The developed AR system is tested on mobile phones web-browsers providing clear insights on the performance of the system. Promising results highlight a number of frame per second varying between 27 and 60 for a real-time point budget of 4.3 million points. The point cloud tested is composed of 29 million points and shows how our indexation strategy permits the integration of massive point clouds aiming at the point budget. The results also gives research directions concerning the dependence and delay related to the quality of the network connection, and the battery consumption since portable sensors are used all the time. [less ▲]

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See detailSELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD
Poux, Florent ULiege; Ponciano, Jean-Jacques

in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2020, August 12), XLIII(B2), 309316

Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on ... [more ▼]

Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed by an ontology-based classification reinforced by self-learning. We use both shape-based features that only leverages the raw X, Y, Z attributes as well as relationship and topology between voxel entities to obtain a 3D structural connectivity feature describing the point cloud. These are then used through a planar-based unsupervised segmentation to create relevant clusters constituting the input of the ontology of classification. Guided by semantic descriptions, the object characteristics are modelled in an ontology through OWL2 and SPARQL to permit structural elements classification in an interoperable fashion. The process benefits from a self-learning procedure that improves the object description iteratively in a fully autonomous fashion. Finally, we benchmark the approach against several deep-learning methods on the S3DIS dataset. We highlight full automation, good performances, easy-integration and a precision of 99.99% for planar-dominant classes outperforming state-of-the-art deep learning. [less ▲]

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See detailInitial User-Centered Design of a Virtual Reality Heritage System: Applications for Digital Tourism
Poux, Florent ULiege; Valembois, Quentin ULiege; Mattes, Christian et al

in Remote Sensing (2020), 12(16), 2583

Reality capture allows for the reconstruction, with a high accuracy, of the physical reality of cultural heritage sites. Obtained 3D models are often used for various applications such as promotional ... [more ▼]

Reality capture allows for the reconstruction, with a high accuracy, of the physical reality of cultural heritage sites. Obtained 3D models are often used for various applications such as promotional content creation, virtual tours, and immersive experiences. In this paper, we study new ways to interact with these high-quality 3D reconstructions in a real-world scenario. We propose a user-centric product design to create a virtual reality (VR) application specifically intended for multi-modal purposes. It is applied to the castle of Jehay (Belgium), which is under renovation, to permit multi-user digital immersive experiences. The article proposes a high-level view of multi-disciplinary processes, from a needs analysis to the 3D reality capture workflow and the creation of a VR environment incorporated into an immersive application. We provide several relevant VR parameters for the scene optimization, the locomotion system, and the multi-user environment definition that were tested in a heritage tourism context [less ▲]

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See detailPoint Cloud vs. Mesh Features for Building Interior Classification
Bassier, Maarten; Vergauwen, Maarten; Poux, Florent ULiege

in Remote Sensing (2020), 12(14), 2224

Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is ... [more ▼]

Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is challenging due to inherent obstacles such as noise, occlusions, sparsity or variance in the density. Alternatively, 3D mesh geometries derived from point clouds benefit from preprocessing routines that can surmount these obstacles and potentially result in more refined geometry and topology descriptions. In this article, we provide a rigorous comparison of both geometries for scene interpretation. We present an empirical study on the suitability of both geometries for the feature extraction and classification. More specifically, we study the impact for the retrieval of structural building components in a realistic environment which is a major endeavor in Building Information Modeling (BIM) reconstruction. The study runs on segment-based structuration of both geometries and shows that both achieve recognition rates over 75% F1 score when suitable features are used. [less ▲]

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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.

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See detailHow to represent 3D Data?
Poux, Florent ULiege

in Towards Data Science (2020, May 11)

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 ▲]

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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 ▲]

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See detailCLASSIFICATION AND INTEGRATION OF MASSIVE 3D POINTS CLOUDS IN A VIRTUAL REALITY (VR) ENVIRONMENT
Kharroubi, Abderrazzaq ULiege; 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 ▲]

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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 ▲]

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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 ▲]

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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 ▲]

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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 ▲]

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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 ▲]

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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 ▲]

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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)

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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 ▲]

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