Reference : CLASSIFICATION AND INTEGRATION OF MASSIVE 3D POINTS CLOUDS IN A VIRTUAL REALITY (VR) ...
Scientific congresses and symposiums : Paper published in a journal
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/242146
CLASSIFICATION AND INTEGRATION OF MASSIVE 3D POINTS CLOUDS IN A VIRTUAL REALITY (VR) ENVIRONMENT
English
[fr] CLASSIFICATION ET INTÉGRATION DE NUAGES DE POINTS 3D MASSIFS DANS UN ENVIRONNEMENT DE RÉALITÉ VIRTUELLE (VR)
Kharroubi, Abderrazzaq [> >]
Hajji, Rafika [> >]
Billen, Roland mailto [Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie >]
Poux, Florent mailto [Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie >]
29-Nov-2019
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Copernicus
XLII-2
W17
165-171
Yes (verified by ORBi)
No
International
1682-1750
2194-9034
Goettingen
Germany
6th International Workshop LowCost 3D – Sensors, Algorithms, Applications
du 2 décembre 2019 au 3 décembre 2019
INSA Strasbourg
Strasbourg
France
[en] Virtual reality ; 3D Point cloud ; Octree data structure ; Segmentation ; Spatial Indexation ; Classification
[en] 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.
Researchers ; Professionals ; Students ; Others
http://hdl.handle.net/2268/242146
10.5194/isprs-archives-XLII-2-W17-165-2019
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W17/165/2019/
https://www.researchgate.net/publication/337638335_CLASSIFICATION_AND_INTEGRATION_OF_MASSIVE_3D_POINTS_CLOUDS_IN_A_VIRTUAL_REALITY_VR_ENVIRONMENT
http://www.pointcloudproject.com

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