A Rao-Blackwellized MCMC Algorithm for Recovering Piecewise Planar 3D Models From Multiple View RGBD Images
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In this paper, we propose a reconstruction technique that uses 2D regions/superpixels rather than point features. We use pre-segmented RGBD data as input and obtain piecewise planar 3D models of the world. We solve the problem of superpixel labeling within single and multiple views simultaneously by using a Rao-Blackwellized Markov Chain Monte Carlo (MCMC) algorithm. We present our output as a labeled 3D model of the world by integrating out over all possible 3D planes in a fully Bayesian fashion. We present our results on the new SUN3D dataset [?].