Beyond the line of sight: labeling the underlying surfaces

author: Ruiqi Guo, Department of Computer Science at Illinois
chairman: Aude Oliva, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, MIT
chairman: Silvio Savarese, Department of Electrical Engineering and Computer Science, University of Michigan
published: Nov. 12, 2012,   recorded: October 2012,   views: 3569


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Scene understanding requires reasoning about both what we can see and what is occluded. We offer a simple and general approach to infer labels of occluded background regions. Our approach incorporates estimates of visible surrounding background, detected objects, and shape priors from transferred training regions. We demonstrate the ability to infer the labels of occluded background regions in both the outdoor StreetScenes dataset and an indoor scene dataset using the same approach. Our experiments show that our method outperforms competent baselines.

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