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 the founding, 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.

中文翻译：

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360$^\circ$ 全向图像中的失真自适应显着对象检测

在过去的几十年中，基于图像的显着目标检测 (SOD) 得到了广泛的探索。但是，360 上的 SOD$^\circ$由于缺乏具有像素级注释的数据集，全向图像的研究较少。为此，本文提出了一个 360$^\circ$基于图像的 SOD 数据集，包含 500 个高分辨率等距柱状图。我们从五个主流 360 中收集了具有代表性的等距柱状图$^\circ$视频数据集，并使用自由视点的方式使用精确的蒙版手动注释这些图像上的所有对象和区域。据我们所知，它是第一个在 360 上进行显着物体检测的公开可用数据集$^\circ$场景。通过观察这个数据集，我们发现投影失真、大规模复杂场景和小的显着物体是最突出的特征。受创立的启发，本文提出了等距柱状图图像上的 SOD 基线模型。在所提出的方法中，我们构建了一个失真自适应模块来处理由等距柱状投影引起的失真。此外，引入了多尺度上下文集成块来感知和区分全方位场景中的丰富场景和物体。全网有序组织，深度监督。实验结果表明，所提出的基线方法在 360 度上优于性能最佳的最新方法$^\circ$SOD 数据集。此外，建议的基线方法和其他方法在 360 上的基准测试结果$^\circ$ SOD 数据集显示所提出的数据集非常具有挑战性，这也验证了所提出的数据集的实用性和促进 SOD 在 360 上发展的方法$^\circ$ 全方位场景。