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Probability occupancy maps for occluded depth images
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
We propose a novel approach to computing the probabilities of presence of multiple and potentially occluding objects in a scene from a single depth map. To this end, we use a generative model that predicts the distribution of depth images that would be produced if the probabilities of presence were known and then to optimize them so that this distribution explains observed evidence as closely as possible. This allows us to exploit very effectively the available evidence and outperform
doi:10.1109/cvpr.2015.7298900
dblp:conf/cvpr/BagautdinovFF15
fatcat:7yxm5w55ufcdzlcnldylrel7aq