Learning depth-aware deep representations for robotic perception
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
European Commission's projectEC-H2020-644271-AEROARMS
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.
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CitationPorzi, L., Rota, S., Peñate, A., Ricci, E., Moreno-Noguer, F. Learning depth-aware deep representations for robotic perception. "IEEE robotics and automation letters", 2017, vol. 2, núm. 2, p. 468-475.