  Image-based salient object detection (SOD) has been extensively explored in
the past decades. However, SOD on 360$^\circ$ omnidirectional images is less
studied owing to the lack of datasets with pixel-level annotations. Toward this
end, this paper proposes a 360$^\circ$ image-based SOD dataset that contains
500 high-resolution equirectangular images. We collect the representative
equirectangular images from five mainstream 360$^\circ$ video datasets and
manually annotate all objects and regions over these images with precise masks
with a free-viewpoint way. To the best of our knowledge, it is the first public
available dataset for salient object detection on 360$^\circ$ scenes. By
observing this dataset, we find that distortion from projection, large-scale
complex scene and small salient objects are the most prominent characteristics.
Inspired by these foundings, this paper proposes a baseline model for SOD on
equirectangular images. In the proposed approach, we construct a
distortion-adaptive module to deal with the distortion caused by the
equirectangular projection. In addition, a multi-scale contextual integration
block is introduced to perceive and distinguish the rich scenes and objects in
omnidirectional scenes. The whole network is organized in a progressively
manner with deep supervision. Experimental results show the proposed baseline
approach outperforms the top-performanced state-of-the-art methods on
360$^\circ$ SOD dataset. Moreover, benchmarking results of the proposed
baseline approach and other methods on 360$^\circ$ SOD dataset show the
proposed dataset is very challenging, which also validate the usefulness of the
proposed dataset and approach to boost the development of SOD on 360$^\circ$
omnidirectional scenes.
