Symmetry-Aware façade parsing with occlusions

Andrea Cohen, Martin R. Oswald, Yanxi Liu, Marc Pollefeys

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Symmetries and repetitions are common and valuable features in urban scenes. We propose to leverage such regularity information in an efficient optimization scheme in order to segment a rectified image of a facade into semantic categories. Our method retrieves a parsing which respects common architectural constraints as well as detected repetitive structures and edge information. Additionally, the use of symmetry information allows us to efficiently deal with large occluded areas and to recover plausible facade images with a minimum of occlusions. Our approach yields state-of-The-Art accuracy on datasets with challenging occlusions. Competitive works either fully fail to deal with large occlusions or they are an order of magnitude slower than our approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 International Conference on 3D Vision, 3DV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages393-401
Number of pages9
ISBN (Electronic)9781538626108
DOIs
StatePublished - May 25 2018
Event7th IEEE International Conference on 3D Vision, 3DV 2017 - Qingdao, China
Duration: Oct 10 2017Oct 12 2017

Publication series

NameProceedings - 2017 International Conference on 3D Vision, 3DV 2017

Other

Other7th IEEE International Conference on 3D Vision, 3DV 2017
CountryChina
CityQingdao
Period10/10/1710/12/17

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Cohen, A., Oswald, M. R., Liu, Y., & Pollefeys, M. (2018). Symmetry-Aware façade parsing with occlusions. In Proceedings - 2017 International Conference on 3D Vision, 3DV 2017 (pp. 393-401). (Proceedings - 2017 International Conference on 3D Vision, 3DV 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/3DV.2017.00052