INFLUENCE OF VIEWPOINT ON VISUAL SALIENCY MODELS FOR VOLUMETRIC CONTENT

In order to predict where humans look in a 3D immersive environment, saliency can be computed using either 3D saliency models or view-based approaches (2D projection). In fact, building a 3D complete model is still a challenging task that is not investigated enough in the research field while 2D imaging approaches have been extensively studied and have shown solid performances. As 6 degrees of freedom are allowed in volumetric videos, users are able to navigate through the content in different manners. In this case, 2D saliency models might be less robust if applied naively, since advanced parameters such as viewing distance are not considered in such models. The aim of this paper is to investigate the influence of viewpoint on 2D saliency models when applied on volumetric data and this to get a better understanding of how viewpoint information could be integrated into view-based approaches. To do so, a subjective psycho-visual experiment was conducted and a fine analysis was led using the variance analysis statistical method.