Dot Density Effects on Stereoscopic Transparency: A Cross-Correlation Model Analysis

Depth perception is one of the cornerstones of human vision, with binocular disparity serving as one of its most powerful cues. This study employs simulation experiments to assess the cross-correlation model’s capability to explain the depth reduction phenomenon in stereoscopic transparency stimuli with identical binocular disparities. We also examined the influence of dot density on perceived depth. Our findings indicate that the cross-correlation model accurately detects the number of surfaces in 2-POTS (parallel, overlapping, transparent stereoscopic surfaces) and 3-POTS across various conditions. The model effectively accounts for the depth reduction phenomenon except when large binocular disparities are involved. Notably, dot density was not a significant factor in affecting the simulation results. This outcome aligns with existing psychophysical observations. This study provides critical insights into the model’s robustness and its ability to explain intricate phenomena like stereoscopic transparency and depth reduction. The study underscores the versatility of the cross-correlation model and sets the stage for future research aiming at quantitative comparisons with psychophysical metrics.