摘要 : Research on understanding how people watch/explore Virtual Reality (VR) and 360 content is crucial to develop appropriate technologies for processing, encoding, delivering and rendering media content in order to provide high-quali... 展开 Research on understanding how people watch/explore Virtual Reality (VR) and 360 content is crucial to develop appropriate technologies for processing, encoding, delivering and rendering media content in order to provide high-quality immersive experiences to users. In this sense, the work described in this paper aims at supporting the research on visual attention modeling for omnidirectional content, by presenting a dataset and toolbox that were created for the Grand Challenge "Salient360!" organized at ICME'17. In particular, the dataset includes 360 degrees images and their corresponding saliency maps and scanpaths, obtained from a subjective experiment in which eye gazes and head movements of observers were recorded during a free-viewing task. In addition, guidelines to obtain saliency maps and scanpaths from eye and head tracking data for omnidirectional images are provided. Finally, together with the dataset, a toolbox has been made publicly available, containing scripts for comparing saliency maps and scanpaths to benchmark developed models. 收起