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Auteur Yingchao Ren |
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Parsing of urban facades from 3D point clouds based on a novel multi-view domain / Wei Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)
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Titre : Parsing of urban facades from 3D point clouds based on a novel multi-view domain Type de document : Article/Communication Auteurs : Wei Wang, Auteur ; Yuan Xu, Auteur ; Yingchao Ren, Auteur ; Gang Wang, Auteur Année de publication : 2021 Article en page(s) : pp 283-293 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] données localisées 3D
[Termes IGN] façade
[Termes IGN] fusion de données
[Termes IGN] milieu urbain
[Termes IGN] précision de la classification
[Termes IGN] segmentation hiérarchique
[Termes IGN] segmentation multi-échelle
[Termes IGN] semis de pointsRésumé : (Auteur) Recently, performance improvement in facade parsing from 3D point clouds has been brought about by designing more complex network structures, which cost huge computing resources and do not take full advantage of prior knowledge of facade structure. Instead, from the perspective of data distribution, we construct a new hierarchical mesh multi-view data domain based on the characteristics of facade objects to achieve fusion of deep-learning models and prior knowledge, thereby significantly improving segmentation accuracy. We comprehensively evaluate the current mainstream method on the RueMonge 2014 data set and demonstrate the superiority of our method. The mean intersection-over-union index on the facade-parsing task reached 76.41%, which is 2.75% higher than the current best result. In addition, through comparative experiments, the reasons for the performance improvement of the proposed method are further analyzed. Numéro de notice : A2021-333 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.4.283 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.4.283 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97531
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 283-293[article]Exemplaires(1)
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