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What and Who
Title:Indoor Scene 3D Layout and Clutter Estimation from RGBD Images
Speaker:Mehdi Noroozi
coming from:Sharif University of Technology
Speakers Bio:
Event Type:PhD Application Talk
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Monday, 4 May 2015
Duration:45 Minutes
Building:E1 4
A new approach for recovering indoor scene layout and clutter from RGB-D images is proposed. Also, a robust method is introduced for obtaining scene coordinates from depth images by solving a restricted quantization problem over normal vectors. Using this quantization and 3D position of pixels, the scene is decomposed into planar surfaces and an orientation labeling is obtained. The segmented image is used to extract features for layout candidates generated by sampled rays from vanishing points. These features are applied in a structured learning algorithm to rank layout candidates. The approach recover clutter by distinguishing different layers of parallel surfaces. The experimental results on the challenging NYU-Depth V2 dataset show that this approach outperforms state-of-the-art methods both in accuracy and computational cost.

[1]. M. Noroozi, M.K. Tabrizi and S.R. Moghadasi. Indoor Scene 3D Layout and Clutter Estimation from RGB-D Images. In 3DV, 2014.

Name(s):Jennifer Gerling
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Attachments, File(s):

Jennifer Gerling/MPI-INF, 05/02/2015 06:59 PM
Last modified:
Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Jennifer Gerling, 05/02/2015 07:02 PM -- Created document.