Adjusting stereoscopic parameters by evaluating the point of regard in a virtual environment

Abstract

Despite the growth in research and development in the area of virtual reality over the past few years, virtual worlds do not yet convey a feeling of presence that matches reality. This is particularly due to the difference in visual perception of flat images as compared to actual 3D. We studied the impact of two parameters of the stereoscopic configuration, namely, the inter-camera distance (ICD) and the presence of a depth of field blur (DOF blur). We conducted an experiment involving 18 participants in order to evaluate this impact, based on both subjective and objective criteria. We examined six configurations which differed in the presence or absence of DOF blur and the value of the ICD: fixed and equal to the anatomical interpupillary distance, fixed and chosen by the participant, or variable, depending on the depth of the viewer’s point of regard (POR). The DOF blur and the variable ICD require the use of an eye tracking system in order to be adjusted with respect to the POR. To our knowledge, no previously published research has tested a gaze-contingent variable ICD along with dynamic DOF blur in a Cave Automatic Virtual Environment. Our results show that the anatomical and variable ICD performed similarly regarding each criterion of the experiment, both being more efficient than the fixed ICD. Besides, as with earlier similar attempts, the configurations with DOF blur obtained lower subjective evaluations. Although mainly not significant, the results obtained by the variable ICD and DOF blur are likely due to a noticeable delay in the parameters update. We also designed a new methodology to objectively compare the geometry and depth rendering, based on the reproduction of the same scene in the real and virtual setups, and then on the study of resulting ocular convergence and angular deviation from a target. This leads to a new comparative criterion for the perceptual realism of immersive virtual environments based on the visual behavior similarity between real and virtual setups.