Reference : Semantic enrichment of point cloud by automatic extraction and enhancement of 360° pa...
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/2268/242149
Semantic enrichment of point cloud by automatic extraction and enhancement of 360° panoramas
English
[fr] ENRICHISSEMENT SÉMANTIQUE DE NUAGE DE POINTS PAR EXTRACTION AUTOMATIQUE ET AMÉLIORATION DE PANORAMAS 360°
Tabkha, A. [> >]
Hajji, R. [> >]
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
355-362
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] 3D Point cloud ; Semantic information ; Feature extraction, ; point cloud representation ; Deep learning ; Image recognition
[en] 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.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/2268/242149
10.5194/isprs-archives-XLII-2-W17-355-2019
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W17/355/2019/
https://www.researchgate.net/publication/337645025_SEMANTIC_ENRICHMENT_OF_POINT_CLOUD_BY_AUTOMATIC_EXTRACTION_AND_ENHANCEMENT_OF_360_PANORAMAS
http://www.pointcloudproject.com

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