Indoor Semantic Segmentation using depth information

Camille Couprie 1 Clément Farabet 2, 3 Laurent Najman 3 Yann Lecun 2
1 Direction Technologie, Informatique et Mathématiques appliquées
2 CIMS - Courant Institute of Mathematical Sciences [New York]
3 LIGM - Laboratoire d'Informatique Gaspard-Monge
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.
Type de document :
Communication dans un congrès
First International Conference on Learning Representations (ICLR 2013), May 2013, Scottsdale, AZ, United States. pp.1-8, 2013
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https://hal.archives-ouvertes.fr/hal-00805105
Contributeur : Laurent Najman <>
Soumis le : mercredi 27 mars 2013 - 09:33:18
Dernière modification le : mardi 15 mai 2018 - 14:50:01

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  • HAL Id : hal-00805105, version 1
  • ARXIV : 1301.3572

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Camille Couprie, Clément Farabet, Laurent Najman, Yann Lecun. Indoor Semantic Segmentation using depth information. First International Conference on Learning Representations (ICLR 2013), May 2013, Scottsdale, AZ, United States. pp.1-8, 2013. 〈hal-00805105〉

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