Spatial interactions minimize relative disparity between adjacent surfaces.

Computational models of stereopsis employ a number of algorithms that constrain stereo matches to produce the smallest absolute disparity and to minimize the relative disparity between nearby features. In some natural scenes, such as large slanted textured surfaces, these two constraints lead to different matching solutions. The current study utilized a stimulus in which there was a large discrepancy in both the magnitude and direction of matches that solved for minimum absolute and minimum relative disparity. This discrepancy revealed a dominance for the minimum relative disparity over the minimum absolute disparity matching solution that increased with spatial proximity, spatial frequency and width of adjacent features. The likelihood of a minimum-relative-disparity matching solution also increased when the difference between the amplitudes of the alternative relative disparities was large. When alternative relative disparity matching solutions had similar amplitudes but opposite signs (crossed vs. uncrossed), an idiosyncratic depth bias served as a tie-breaker. The present results show that absolute disparity matches are constrained to minimize relative disparity between adjacent features.