Fig. 1. Weight propagation based on horizontal tree structure

Fig. 2. Support weights for selected regions (the brightness value of neighborhood pixel represents the support weight for central pixel which is marked with red box). (a) Image partial block; (b) support weight map without iterative cost aggregation; (c) support weight map after iterative cost aggregation

Fig. 3. Disparity variation maps computed before and after iterative cost aggregation. (a) Reindeer left image; (b) Reindeer right image; (c) left disparity map computed before iterative cost aggregation; (d) left disparity map computed after iterative cost aggregation

Fig. 4. Principle of disparity refinement. (a) Dolls left image; (b) Dolls right image; (c) left-right consistence test; (d) initial left disparity map without disparity refinement

Fig. 5. Disparity maps with different disparity refinement methods. (a) Disparity refinement method in Ref. [11]; (b) improved disparity refinement method

Fig. 6. Disparity maps obtained by different cost aggregation algorithms (mismatched pixels are marked in red area). (a) Real disparity map; (b) by minimum spanning tree; (c) by cross-scale minimum spanning tree; (d) by guided filtering; (e) by cross-scale segment tree; (f) by cost aggregation algorithm in Ref. [11]; (g) by improved cost aggregation algorithm

Fig. 7. Experimental results of the Middlebury benchmark images. (a) Left reference images; (b) right reference images; (c) left real disparity maps; (d) by disparity refinement method in Ref. [11]; (e) by improved disparity refinement method

Table 1. Parameters of the proposed stereo matching algorithm

Table 2. Error of different stereo matching methods in non-occluded areas without disparity refinement (unit: %)

Table 3. Matching error of different stereo matching methods in image areas with disparity refinement (unit: %)