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Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networks

Nico Hoft, Hannes Schulz, Sven Behnke
Rheinische Friedrich-Wilhelms-Universitat Bonn Institut fur Informatik VI, Friedrich-Ebert-Allee 144
37th German Conference on Artificial Intelligence (KI), pp. 80-85, Springer LNCS 8736, 2014

@article{hoft2014fast,

   title={Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networks},

   author={H{"o}ft, Nico and Schulz, Hannes and Behnke, Sven},

   year={2014}

}

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In semantic scene segmentation, every pixel of an image is assigned a category label. This task can be made easier by incorporating depth information, which structured light sensors provide. Depth, however, has very different properties from RGB image channels. In this paper, we present a novel method to provide depth information to convolutional neural networks. For this purpose, we apply a simplified version of the histogram of oriented depth (HOD) descriptor to the depth channel. We evaluate the network on the challenging NYU Depth V2 dataset and show that with our method, we can reach competitive performance at a high frame rate.
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