An improved semi-global stereo matching algorithm for 3D reconstruction
Guodong Song, Linan Ren · Third International Conference on Computer Vision and Data Mining (ICCVDM 2022) · 2023
Stereo matching is a key step in 3D reconstruction based on binocular vision. The choice of stereo matching algorithm is directly related to the effect of 3D reconstruction. Aiming at the limitation of the semi-global stereo matching algorithm SGBM, the Census transform relies too much on the center point pixel and is easily disturbed by noise. An improved algorithm based on SGBM is proposed as a stereo matching algorithm in this paper. In the original cost calculation stage, the algorithm replaces the gray value of the original center pixel with the minimum error gray mean value of the multichannel neighborhood to perform the Census operation, and combines the new operation result with the AD cost of the pixel to calculate the initial matching cost. It effectively solves the pixel dependence of the center point and the matching ambiguity problem of a single Census cost in the repeated area. The disparity map is recovered by methods such as multipath cost aggregation and left-right consistency detection after improved cost calculation is completed. This paper uses the Middlebury standard data set to verify the effectiveness of the improved algorithm. Through experimental comparison and analysis, the disparity map generated by the improved algorithm in this paper is effectively improved.