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3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

Poux, Florent ; Neuville, Romain ; Nys, Gilles-Antoine ; Billen, Roland

Remote sensing (Basel, Switzerland), 2018-09, Vol.10 (9), p.1412 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture
Autor: Poux, Florent ; Neuville, Romain ; Nys, Gilles-Antoine ; Billen, Roland
Assuntos: 3D modelling ; 3D Point Cloud ; Architecture ; Base de données 3D ; cognition systems ; Computer graphics ; Computer science ; Data mining ; Earth sciences & physical geography ; Emergency preparedness ; Engineering, computing & technology ; Extraction d'information ; Feature extraction ; Furniture ; Ingénierie, informatique & technologie ; International conferences ; Knowledge ; Modelling ; ModelNet10 ; modélisation 3D ; modélisation procédurale ; Navigation ; Nuage de points ; Ontology ; PCA ; Physical, chemical, mathematical & earth Sciences ; Physique, chimie, mathématiques & sciences de la terre ; Planning ; Point Cloud Database ; procedural modelling ; Robotics ; Sciences de la terre & géographie physique ; Sciences informatiques ; segmentation sémantique ; semantic segmentation ; Semantics ; Shape optimization ; systèmes cognitifs ; Three dimensional models ; voxel
É parte de: Remote sensing (Basel, Switzerland), 2018-09, Vol.10 (9), p.1412
Notas: scopus-id:2-s2.0-85053634547
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

Poux, Florent ; Neuville, Romain ; Nys, Gilles-Antoine ; Billen, Roland

Basel: MDPI AG

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