  Various 3D reconstruction methods have enabled civil engineers to detect
damage on a road surface. To achieve the millimetre accuracy required for road
condition assessment, a disparity map with subpixel resolution needs to be
used. However, none of the existing stereo matching algorithms are specially
suitable for the reconstruction of the road surface. Hence in this paper, we
propose a novel dense subpixel disparity estimation algorithm with high
computational efficiency and robustness. This is achieved by first transforming
the perspective view of the target frame into the reference view, which not
only increases the accuracy of the block matching for the road surface but also
improves the processing speed. The disparities are then estimated iteratively
using our previously published algorithm where the search range is propagated
from three estimated neighbouring disparities. Since the search range is
obtained from the previous iteration, errors may occur when the propagated
search range is not sufficient. Therefore, a correlation maxima verification is
performed to rectify this issue, and the subpixel resolution is achieved by
conducting a parabola interpolation enhancement. Furthermore, a novel disparity
global refinement approach developed from the Markov Random Fields and Fast
Bilateral Stereo is introduced to further improve the accuracy of the estimated
disparity map, where disparities are updated iteratively by minimising the
energy function that is related to their interpolated correlation polynomials.
The algorithm is implemented in C language with a near real-time performance.
The experimental results illustrate that the absolute error of the
reconstruction varies from 0.1 mm to 3 mm.
