  Stereo vision techniques have been widely used in civil engineering to
acquire 3-D road data. The two important factors of stereo vision are accuracy
and speed. However, it is very challenging to achieve both of them
simultaneously and therefore the main aim of developing a stereo vision system
is to improve the trade-off between these two factors. In this paper, we
present a real-time stereo vision system used for road surface 3-D
reconstruction. The proposed system is developed from our previously published
3-D reconstruction algorithm where the perspective view of the target image is
first transformed into the reference view, which not only increases the
disparity accuracy but also improves the processing speed. Then, the
correlation cost between each pair of blocks is computed and stored in two 3-D
cost volumes. To adaptively aggregate the matching costs from neighbourhood
systems, bilateral filtering is performed on the cost volumes. This greatly
reduces the ambiguities during stereo matching and further improves the
precision of the estimated disparities. Finally, the subpixel resolution is
achieved by conducting a parabola interpolation and the subpixel disparity map
is used to reconstruct the 3-D road surface. The proposed algorithm is
implemented on an NVIDIA GTX 1080 GPU for the real-time purpose. The
experimental results illustrate that the reconstruction accuracy is around 3
mm.
