Abstract

Most previous studies on visual saliency have only focused on static or dynamic 2D scenes. Since the human visual system has evolved predominantly in natural three dimensional environments, it is important to study whether and how depth information influences visual saliency. In this work, we first collect a large human eye fixation database com- piled from a pool of 600 2D-vs-3D image pairs viewed by 80 sub jects, where the depth information is directly provided by the Kinect camera and the eye tracking data are captured in both 2D and 3D free-viewing experiments. We then analyze the ma jor discrepancies between 2D and 3D human fixation data of the same scenes, which are further abstracted and modeled as novel depth priors. Finally, we evaluate the performances of state-of-the-art saliency detection models over 3D images, and propose solutions to enhance their performances by integrating the depth priors.