A fusion framework of stereo vision and kinect for high-quality dense depth maps

Abstract

We present a fusion framework of stereo vision and Kinect for high-quality dense depth maps. The fusion problem is formulated as maximum a posteriori estimation of the Markov random field using the Bayes rule. We design a global energy function with a novel data term, which provides a reasonable, straight-forward and scalable way to fuse stereo vision and the depth data from Kinect. Particularly, visibility and pixelwise noises of the depth data from Kinect are taken into account in our fusion approach. Experimental results demonstrate effectiveness and accuracy of the proposed framework. © 2013 Springer-Verlag.