Quantitative evaluation of perceived depth of transparently-visualized medical 3D data presented with a multi-view 3D display

Abstract

Transparent visualization is used in many fields because it can visualize not only the frontal object but also other important objects behind it. Although in many situations, it would be very important for the 3D structures of the visualized transparent images to be perceived as they are simulated, little is known quantitatively as to how such transparent 3D structures are perceived. To address this question, in the present study, we conducted a psychophysical experiment in which the observers reported the perceived depth magnitude of a transparent object in medical images, presented with a multi-view 3D display. For the visualization, we employed a stochastic point-based rendering (SPBR) method, which was developed recently as a technique for efficient transparent-rendering. Perceived depth of the transparent object was smaller than the simulated depth. We found, however, that such depth underestimation can be alleviated to some extent by (1) applying luminance gradient inherent in the SPBR method, (2) employing high opacities, and (3) introducing binocular disparity and motion parallax produced by a multi-view 3D display.