Smart hypothesis generation for efficient and robust room layout estimation

Martin Hirzer, Peter M. Roth, Vincent Lepetit

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image. As existing approaches based on low-level feature extraction, followed by a vanishing point estimation are very slow and often unreliable in realistic scenarios, we build on semantic segmentation of the input image. To obtain better segmentations, we introduce a robust, accurate and very efficient hypothesize-and-test scheme. The key idea is to use three segmentation hypotheses, each based on a different number of visible walls. For each hypothesis, we predict the image locations of the room corners and select the hypothesis for which the layout estimated from the room corners is consistent with the segmentation. We demonstrate the efficiency and robustness of our method on three challenging benchmark datasets, where we significantly outperform the state-of-the-art.
Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
Number of pages9
ISBN (Electronic)978-1-7281-6553-0
Publication statusPublished - 1 Mar 2020
Eventwacv2020: WACV 2020 - Snowmass Village, United States
Duration: 1 Mar 20205 Mar 2020

Publication series

NameProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020


Abbreviated titleWACV 2020
Country/TerritoryUnited States
CitySnowmass Village

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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