Review on Indoor RGB-D Semantic Segmentation ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks
Auteur(s) :
Barchid, Sami [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Mennesson, José [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Djeraba, Chaabane [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Mennesson, José [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Djeraba, Chaabane [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
CBMI 2021 - Content-based Multimedia Indexing
Ville :
Lille / Virtual
Pays :
France
Date de début de la manifestation scientifique :
2021-06-28
Mot(s)-clé(s) en anglais :
RGB-D Indoor Semantic Segmentation
Deep Convolutional Neural Networks
Deep Learning
Deep Convolutional Neural Networks
Deep Learning
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with ...
Lire la suite >Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular public datasets, proposes a categorization of the strategies employed by recent contributions, evaluates the performance of the current state-of-the-art, and discusses the remaining challenges and promising directions for future works.Lire moins >
Lire la suite >Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular public datasets, proposes a categorization of the strategies employed by recent contributions, evaluates the performance of the current state-of-the-art, and discusses the remaining challenges and promising directions for future works.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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- https://hal.archives-ouvertes.fr/hal-03264035/document
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- http://arxiv.org/pdf/2105.11925
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- https://hal.archives-ouvertes.fr/hal-03264035/document
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- https://hal.archives-ouvertes.fr/hal-03264035/document
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- CBMI_2021___Short_Review_Indoor_semantic_segmentation%281%29.pdf
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- 2105.11925
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