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http://dx.doi.org/10.5302/J.ICROS.2015.15.0159

Efficient 3D Scene Labeling using Object Detectors & Location Prior Maps  

Kim, Joo-Hee (Artificial Intelligence Laboratory, Kyonggi University)
Kim, In-Cheol (Artificial Intelligence Laboratory, Kyonggi University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.21, no.11, 2015 , pp. 996-1002 More about this Journal
Abstract
In this paper, we present an effective system for the 3D scene labeling of objects from RGB-D videos. Our system uses a Markov Random Field (MRF) over a voxel representation of the 3D scene. In order to estimate the correct label of each voxel, the probabilistic graphical model integrates both scores from sliding window-based object detectors and also from object location prior maps. Both the object detectors and the location prior maps are pre-trained from manually labeled RGB-D images. Additionally, the model integrates the scores from considering the geometric constraints between adjacent voxels in the label estimation. We show excellent experimental results for the RGB-D Scenes Dataset built by the University of Washington, in which each indoor scene contains tabletop objects.
Keywords
3D scene labeling; RGB-D video; Markov random field; object detection; location prior map;
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Times Cited By KSCI : 2  (Citation Analysis)
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