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Remote Sens. 2018, 10(9), 1412; https://doi.org/10.3390/rs10091412

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

Geomatics Unit, University of Liège (ULiege), Quartier Agora, Allée du six Août, 19, 4000 Liège, Belgium
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Received: 16 July 2018 / Revised: 25 August 2018 / Accepted: 27 August 2018 / Published: 5 September 2018
(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)
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Abstract

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. View Full-Text
Keywords: 3D point cloud; semantic segmentation; procedural modelling; voxel; 3D modelling; cognition systems; Point Cloud Database; feature extraction; PCA; ModelNet10 3D point cloud; semantic segmentation; procedural modelling; voxel; 3D modelling; cognition systems; Point Cloud Database; feature extraction; PCA; ModelNet10
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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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Poux, F.; Neuville, R.; Nys, G.-A.; Billen, R. 3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture. Remote Sens. 2018, 10, 1412.

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