Labeling Complete Surfaces in Scene Understanding

Ruiqi Guo, Derek Hoiem

Research output: Contribution to journalArticlepeer-review

Abstract

Scene understanding requires reasoning about both what we can see and what is occluded. We offer a simple and general approach to infer labels of occluded background regions. Our approach incorporates estimates of visible surrounding background, detected objects, and shape priors from transferred training regions. We demonstrate the ability to infer the labels of occluded background regions in three datasets: the outdoor StreetScenes dataset, IndoorScene dataset and SUN09 dataset, all using the same approach. Furthermore, the proposed approach is extended to 3D space to find layered support surfaces in RGB-Depth scenes. Our experiments and analysis show that our method outperforms competent baselines.

Original languageEnglish (US)
Pages (from-to)172-187
Number of pages16
JournalInternational Journal of Computer Vision
Volume112
Issue number2
DOIs
StatePublished - Apr 2015

Keywords

  • Geometric layout
  • Image parsing
  • RGB-depth
  • Scene understanding

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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