Reference : 3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture
Scientific journals : Article
Engineering, computing & technology : Architecture
Engineering, computing & technology : Computer science
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/227787
3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture
English
[fr] Modélisation sémantique de nuage de points 3D
Poux, Florent mailto [Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie >]
Neuville, Romain mailto [Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie >]
Nys, Gilles-Antoine mailto [Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie >]
Billen, Roland mailto [Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie >]
5-Sep-2018
Remote Sensing
Molecular Diversity Preservation International (MDPI)
10
9
3D Modelling from Point Clouds: Algorithms and Methods
1412
Yes (verified by ORBi)
International
2072-4292
Basel
Switzerland
[en] 3D Point Cloud ; semantic segmentation ; procedural modelling ; voxel ; 3D modelling ; cognition systems ; Point Cloud Database ; feature extraction ; PCA ; ModelNet10 ; ModelNet10
[fr] Nuage de points ; segmentation sémantique ; modélisation procédurale ; voxel ; modélisation 3D ; systèmes cognitifs ; Base de données 3D ; Extraction d'information ; PCA
[en] 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.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/2268/227787
10.3390/rs10091412
http://www.mdpi.com/2072-4292/10/9/1412
http://www.mdpi.com/2072-4292/10/9/1412/htm
https://www.researchgate.net/publication/327471531_3D_Point_Cloud_Semantic_Modelling_Integrated_Framework_for_Indoor_Spaces_and_Furniture/stats
https://www.academia.edu/37378276/3D_Point_Cloud_Semantic_Modelling_Integrated_Framework_for_Indoor_Spaces_and_Furniture
https://www.geovast3d.com/Publications/
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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