Abstract
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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Höft, N., Schulz, H., Behnke, S. (2014). Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networks. In: Lutz, C., Thielscher, M. (eds) KI 2014: Advances in Artificial Intelligence. KI 2014. Lecture Notes in Computer Science(), vol 8736. Springer, Cham. https://doi.org/10.1007/978-3-319-11206-0_9
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DOI: https://doi.org/10.1007/978-3-319-11206-0_9
Publisher Name: Springer, Cham
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