  Stereo vision technique has been widely used in robotic systems to acquire
3-D information. In recent years, many researchers have applied bilateral
filtering in stereo vision to adaptively aggregate the matching costs. This has
greatly improved the accuracy of the estimated disparity maps. However, the
process of filtering the whole cost volume is very time consuming and therefore
the researchers have to resort to some powerful hardware for the real-time
purpose. This paper presents the implementation of fast bilateral stereo on a
state-of-the-art GPU. By highly exploiting the parallel computing architecture
of the GPU, the fast bilateral stereo performs in real time when processing the
Middlebury stereo datasets.
