发表机构
AI Center, Lomonosov Moscow State University; MSU Institute for Artificial Intelligence; Lomonosov Moscow State University（莫斯科国立大学人工智能中心; 莫斯科国立大学人工智能研究所; 莫斯科国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对VR眼动数据采集成本高的问题，提出开源众包平台OpenSAL360，仅需屏幕鼠标即可采集360°视频显著性数据，并发布含500个视频、2000+评估者的最大规模数据集。
AI 中文摘要
全景视频显著性预测在许多沉浸式多媒体应用中发挥着重要作用，包括视口自适应流媒体与压缩、注视点渲染、网格简化和感知质量评估。然而，该领域的进展仍受限于使用VR头显收集眼动数据的成本与复杂性，这使得大规模数据集的构建难以扩展。我们提出了OpenSAL360，这是首个用于可扩展、低成本360°视频显著性采集的开源平台。与传统的基于VR的协议不同，它仅需标准屏幕、鼠标和互联网连接，即可从普通众包评估者处并行收集显著性数据，无需专用硬件。我们针对七个成熟的VR眼动数据集验证了我们的采集协议，并对关键界面、预处理和后处理参数进行了消融研究。为证明所提方法的有效性和可扩展性，我们收集并公开发布了一个包含500个全景视频、由2000多名众包评估者标注的显著性数据集，据我们所知，这是该领域最大的数据集。我们将OpenSAL360公开于https URL。
英文摘要
Omnidirectional video saliency prediction plays an important role in many immersive multimedia applications, including viewport-adaptive streaming and compression, foveated rendering, mesh simplification, perceptual quality assessment. Yet progress in this area remains constrained by the cost and complexity of collecting eye-tracking data with VR headsets, which makes large-scale dataset creation difficult to extend. We present OpenSAL360, the first open-source platform for scalable, low-cost 360° video saliency collection. Unlike conventional VR-based protocols, it requires only a standard screen, mouse, and internet connection, enabling parallel saliency data collection from common crowdsourcing assessors without specialized hardware. We validate our collection protocol against seven well-established VR eye-tracking datasets and conduct ablation studies on key interface, pre-, and post-processing parameters. To demonstrate the effectiveness and scalability of the proposed methodology, we collect and publicly release a saliency dataset covering 500 omnidirectional videos annotated by 2,000+ crowdsourcing assessors, making it, to the best of our knowledge, the largest dataset in this field. We make OpenSAL360 publicly available at https://github.com/msu-video-group/OpenSAL360.
CommentsAccepted by ACM MM 2026
