Title:
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Learning depth-aware deep representations for robotic perception
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Author:
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Porzi, Lorenzo; Rota Bulò, Samuel; Peñate Sánchez, Adrián; Ricci, Elisa; Moreno-Noguer, Francesc
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Other authors:
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Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
Abstract:
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Abstract:
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Exploiting RGB-D data by means of Convolutional Neural Networks (CNNs) is at the core of a number of robotics applications, including object detection, scene semantic segmentation and grasping. Most existing approaches, however, exploit RGB-D data by simply considering depth as an additional input channel for the network. In this paper we show that the performance of deep architectures can be boosted by introducing DaConv, a novel, general-purpose CNN block which exploits depth to learn scale-aware feature representations. We demonstrate the benefits of DaConv on a variety of robotics oriented tasks, involving affordance detection, object coordinate regression and contour detection in RGB-D images. In each of these experiments we show the potential of the proposed block and how it can be readily integrated into existing CNN architectures. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Automàtica i control -computer vision -RGB-D Perception -Visual Learning -Classificació INSPEC::Pattern recognition::Computer vision |
Rights:
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Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Document type:
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Article - Submitted version Article |
Published by:
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Institute of Electrical and Electronics Engineers (IEEE)
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