Robust local stereo matching under varying radiometric conditions

The authors present a local stereo matching algorithm whose performance is insensitive to changes in radiometric conditions between the input images. First, a prior on the disparities is built by combining the DAISY descriptor and Census filtering. Then, a Census-based cost aggregation with a self-adaptive window is performed. Finally, the maximum a-posteriori estimation is carried out to compute the disparity. The authors' algorithm is compared with both local and global stereo matching algorithms (NLCA, ELAS, ANCC, Adapt Weight and CSBP) by using Middlebury datasets. The results show that the proposed algorithm achieves high-accuracy dense disparity estimations and is more robust to radiometric differences between input images than other algorithms.