Stereo matching algorithm based on improved local consistency constraint

Aiming at the problem of large computational complexity and high mismatch rate caused by the use of random functions to generate planar parameters in the PatchMatchStereo stereo matching algorithm when implementing inclined planes, a stereo matching algorithm based on local consistency constraints is proposed. First, the algorithm obtains support points with high parallax confidence by sparse matching of pixels in the image. Second, the algorithm uses triangulation to determine a triangular plane for each pixel in the image, calculates the plane parameters and assigns them to the points in the plane. Then, a more accurate plane parameter is found for each pixel through iterative propagation, and a local consistent parallel window model is constructed. Finally, the disparity is calculated through the plane parameter and optimized through the disparity post-processing. The algorithm in this paper is tested on the standard test data set of the third edition of the Middlebury evaluation platform. The experimental results show that the average error matching rate after processing is 4. 39% lower than that of the PMS algorithm, and the maximum error matching rate of a single image is reduced by 15. 42%. The algorithm in this paper improves the efficiency of image processing while reducing the false matching rate, and has significant advantages over other algorithms.