3D Pop-Ups: Omnidirectional image visual saliency prediction based on crowdsourced eye-tracking data in VR

Abstract

The prediction of the visual saliency of omnidirectional images in VR is valuable for understanding visual behaviors. However, the equipment cost, software setups, hardware operation, and other constraints in acquiring eye-tracking data of omnidirectional images for visual saliency prediction would lead to a low training efficiency and prediction performance. Therefore, this paper proposed a crowdsourcing method based on recall fixations, which was used to collect and construct an omnidirectional image with eye-tracking dataset called CrowdSourcing360, which contained 16,200 pieces of data on 180 images. Using this dataset, a visual saliency prediction model CSnet360 was trained. Experiments demonstrated that the visual saliency prediction performance of the CSnet360 outperformed most existing models even without using actual gaze fixations. Finally, a VR interior design assistance prototype system was built and the preliminary study results indicated that the system could help designers to improve the quality of their design solutions.