Stereo Matching Algorithm Based on Improved Census Transform and Multi-Feature Fusion

In the three-dimensional reconstruction technology, stereo matching is a key step. Aiming at the problem that local stereo-matching algorithms have poor matching effects in areas with weak texture and discontinuous depth and are easily disturbed by noise, a local stereo-matching algorithm based on multi-feature fusion is proposed. The traditional Census transform is improved to make it more robust to noise and is fused with color features and gradient features for cost calculation; the multiscale guided filtering algorithm is used for cost aggregation, and the disparity map is obtained through disparity calculation and optimization. The experimental results on the Middlebury dataset show that the proposed algorithm has strong antinoise ability, and the matching accuracy is further improved when compared with the current excellent local stereo-matching algorithms.