We propose an innovative stereo matching algorithm using three constraints and collaborative optimization between pixels. First, to initialize a better disparity plane, the random points will be selected using matching energy stability, disparity stability, and left–right consistency as restrictions, instead of choosing the commonly used random sample consensus (RANSAC) method, which has certain randomness and affects the final optimization. Second, for the problem that the grid-based propagation method cannot effectively propagate the labels between adjacent similar pixels and its easiness to fall into local minimums, a cooperative competition mechanism between adjacent similar pixels is proposed, and the matching energy is minimized through collaborative optimization. Third, we iteratively optimize the parallax plane parameters and minimize the matching energy function, and thus improve the stereo matching accuracy. Finally, we test our method on Middlebury V3 test set, which achieves better performance against other state-of-the-art algorithms and ranks first in the matching error <1  pixel all metric, ranks second in the matching error <0.5  pixel all metric, and ranks third in the matching error <2  pixels all metric, and further the number of iterations is reduced and the processing speed was accelerated (recorded on May 3, 2021).