Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation

Martin Hirzer, Peter M. Roth, Vincent Lepetit

Research output: Contribution to journal › Article › Research › peer-review

Abstract

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
JournalarXiv.org e-Print archive
Publication statusPublished - 2019

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Feature extraction
Semantics

Cite this

Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation. / Hirzer, Martin; Roth, Peter M.; Lepetit, Vincent.

In: arXiv.org e-Print archive, 2019.

Research output: Contribution to journal › Article › Research › peer-review

Hirzer, M, Roth, PM & Lepetit, V 2019, 'Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation' arXiv.org e-Print archive.
Hirzer M, Roth PM, Lepetit V. Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation. arXiv.org e-Print archive. 2019.
Hirzer, Martin ; Roth, Peter M. ; Lepetit, Vincent. / Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation. In: arXiv.org e-Print archive. 2019.
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