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arXiv · 1301.3572

Indoor Semantic Segmentation using depth information

Camille Couprie·Clément Farabet·Laurent Najman·Yann LeCun
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Abstract

This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain state-of-the-art on the NYU-v2 depth dataset with an accuracy of 64.5%. We illustrate the labeling of indoor scenes in videos sequences that could be processed in real-time using appropriate hardware such as an FPGA.

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Camille Couprie, Clément Farabet, Laurent Najman, Yann LeCun. 2013-03-14. Indoor Semantic Segmentation using depth information. https://arxiv.org/abs/1301.3572

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