AbstractA Semi-Global Stereo matching algorithm with reordered Census transform and unidirectional dynamic programming optimization is proposed for improving match accuracy and weak immunity in the computation of matching cost. Firstly, the pixels in the Census Transform window in different scales are reordered and the median values are taken to calculate the Hamming distance, which solves the problem of over-reliance on the center pixel of the Census Transform window in traditional algorithm. Then, to improve the matching accuracy, the path aggregation algorithm based on unidirectional dynamic programming is applied to optimize the initial generation value, which can reduce the abnormal matching points and perfect the parallax reconstruction of the weak texture parts. Finally, an winner-take-all strategy is adopted to select the parallax corresponding to the minimum cost aggregation value for pixel selection, and the wrong parallax is eliminated by using left-right consistency detection in the parallax optimization stage. The experiment shows that this improved semi-global stereo matching algorithm generates an 8.22% reduction in the average mis-match rate of the initial parallax map, which is relatively higher in quality, and the flat mismatch rate under different noises is below 8%, which effectively enhances the robustness against noise and improves the matching accuracy.