We measured the performance of several area-based stereo matching algorithms with noise added to synthetic images. Dense disparity maps were computed and compared with the ground truth using three metrics: the fraction of correctly computed disparities, the mean and standard deviation of the distribution of disparity errors.

For a noise-free image, Birch.eld and Tomasi?s Pixel-to-Pixel — a dynamic algorithm — performed slightly better than a simple sum-of-absolute differences algorithm (67% correct matches vs 65%) - considered to be within experimental error. A Census algorithm performed worst at only 54%. The dynamic algorithm performed well until the S/N ratio reached 36dB after which its performance started to drop. However, with correctly chosen parameters, it was superior to correlation and Census algorithms until the images became very noisy (~ 15dB). The dynamic algorithm also ran faster than the fastest correlation algorithms using an optimum window radius of 4 and more than 10 times faster than the Census algorithm.