Indoor semantic segmentation using depth information: 1st International Conference on Learning Representations, ICLR 2013

Camille Couprie, Clément Farabet, Laurent Najman, Yann LeCun

Research output: Contribution to conferencePaperpeer-review

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.

Original languageEnglish (US)
StatePublished - Jan 1 2013
Event1st International Conference on Learning Representations, ICLR 2013 - Scottsdale, United States
Duration: May 2 2013May 4 2013

Conference

Conference1st International Conference on Learning Representations, ICLR 2013
CountryUnited States
CityScottsdale
Period5/2/135/4/13

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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