从直播视频重建乒乓球三维轨迹，无需依赖人体姿态或球拍追踪。
TT3D: Table Tennis 3D Reconstruction
- 利用物理规律最小化重投影误差，实现高精度3D轨迹重建。
- 无需人体姿态或球拍数据，直接推断球的旋转状态。
- 自动校准摄像机运动，支持完整回合的三维还原，适合体育分析。
体育分析需处理海量数据，耗时且成本高。神经网络的进步显著减轻了这一负担，使比赛直播中的球体跟踪达到高精度。然而，仅依赖2D跟踪受限于视角，难以支撑全面分析。为此，我们提出一种新方法，从在线乒乓球比赛录像中重建精确的3D球轨迹。该方法基于球体运动的物理规律，通过最小化飞行轨迹的重投影误差，识别出最优的击球落点状态，确保3D重建的准确可靠。关键优势在于无需依赖人体姿态估计或球拍追踪即可推断球的旋转，而这两者在直播画面中常不可靠或缺失。我们还开发了自动摄像机校准方法，可稳定追踪摄像机运动；并改进了一个原有3D姿态估计模型（缺乏深度运动捕捉），使其能精准追踪球员动作。上述贡献共同实现了乒乓球回合的完整3D重建。
原文摘要 · Abstract (English)
Sports analysis requires processing large amounts of data, which is time-consuming and costly. Advancements in neural networks have significantly alleviated this burden, enabling highly accurate ball tracking in sports broadcasts. However, relying solely on 2D ball tracking is limiting, as it depends on the camera's viewpoint and falls short of supporting comprehensive game analysis. To address this limitation, we propose a novel approach for reconstructing precise 3D ball trajectories from online table tennis match recordings. Our method leverages the underlying physics of the ball's motion to identify the bounce state that minimizes the reprojection error of the ball's flying trajectory, hence ensuring an accurate and reliable 3D reconstruction. A key advantage of our approach is its ability to infer ball spin without relying on human pose estimation or racket tracking, which are often unreliable or unavailable in broadcast footage. We developed an automated camera calibration method capable of reliably tracking camera movements. Additionally, we adapted an existing 3D pose estimation model, which lacks depth motion capture, to accurately track player movements. Together, these contributions enable the full 3D reconstruction of a table tennis rally.
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