Abstract: For dense stereo matching, a local stereo matching algorithm based on region growing is proposed to solve the difficulties in choosing the size and shape of support windows as well as the pixels that have similar depth, and the difficulties in dealing with the boundaries of objects as well as the occlusion areas. Firstly, color similarity and connectivity constraint are adopted to obtain the adaptive support window dynamically by means of a method based on region growing. Then, the weights of pixels in support window or search window are assigned by different strategies, to reduce the effect of the pixels with different depths on the support window, and abandon the support windows that don't conform to the shape similarity rule. Finally, the matching cost is computed. The initial disparity is obtained by means of WTA (winner takes all) strategy. For the support windows, the frequencies of disparities of the pixels in accord with the confidence level standard are computed, in which the disparity with the highest frequency is taken as the optimal disparity. Moreover, the algorithm complexity is reduced through analyzing and optimizing the processes of support region computation, cost aggregation, and disparity search. The proposed algorithm is tested on Middlebury platform, and the result shows its excellent performance.