Stereo Fusion from Multiple Viewpoints

Advanced driver assistance using cameras is a first important step towards autonomous driving tasks. However, the computational power in automobiles is highly limited and hardware platforms with enormous processing resources such as GPUs are not available in serial production vehicles. In our paper we address the need for a highly efficient fusion method that is well suited for standard CPUs. We assume that a number of pairwise disparity maps are available, which we project to a reference view pair and fuse them efficiently to improve the accuracy of the reference disparity map. We estimate a probability density function of disparities in the reference image using projection uncertainties. In the end the most probable disparity map is selected from the probability distribution. We carried out extensive quantitative evaluations on challenging stereo data sets and real world images. These results clearly show that our method is able to recover very accurate disparity maps in real-time.