视觉同步：通过跨视角物体运动实现多摄像头同步
Visual Sync: Multi-Camera Synchronization via Cross-View Object Motion
AI总结：
VisualSync通过多视角动态优化，实现跨摄像头流的毫秒级同步，有效降低同步误差至50毫秒以下。
摘要：
如今，人们可以轻松记录难忘的时刻，从音乐会、体育赛事、讲座、家庭聚会和生日派对等，使用多个消费级摄像头。然而，同步这些跨摄像头流仍然具有挑战性。现有方法假设受控环境、特定目标、手动校正或昂贵的硬件。我们提出了VisualSync，一个基于多视角动态的优化框架，能够以毫秒级精度对齐未标记、不同步的视频。我们的关键洞察是，任何移动的3D点，当在两个摄像头中同时可见时，在正确同步后会遵守视图约束。为了利用这一点，VisualSync利用现成的3D重建、特征匹配和密集跟踪来提取轨迹、相对姿态和跨视角对应关系。然后，它通过联合最小化视图误差来估计每个摄像头的时间偏移。在四个多样、具有挑战性的数据集上的实验表明，VisualSync优于基线方法，实现了中位同步误差低于50毫秒。
英文摘要：
Today, people can easily record memorable moments, ranging from concerts, sports events, lectures, family gatherings, and birthday parties with multiple consumer cameras. However, synchronizing these cross-camera streams remains challenging. Existing methods assume controlled settings, specific targets, manual correction, or costly hardware. We present VisualSync, an optimization framework based on multi-view dynamics that aligns unposed, unsynchronized videos at millisecond accuracy. Our key insight is that any moving 3D point, when co-visible in two cameras, obeys epipolar constraints once properly synchronized. To exploit this, VisualSync leverages off-the-shelf 3D reconstruction, feature matching, and dense tracking to extract tracklets, relative poses, and cross-view correspondences. It then jointly minimizes the epipolar error to estimate each camera's time offset. Experiments on four diverse, challenging datasets show that VisualSync outperforms baseline methods, achieving an median synchronization error below 50 ms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
