提升乒乓球运动：三维轨迹与旋转估计的鲁棒性实际应用

摘要

从标准单目视频中精确获取乒乓球的三维运动轨迹是一个具有挑战性的难题，因为基于合成数据训练的现有方法难以泛化到现实世界中存在噪声且不完美的球体与球台检测场景。这主要源于现实视频中固有的三维真实轨迹数据和旋转标注的缺失。为解决这一问题，我们提出了一种新颖的两阶段流程，将任务分解为前端感知任务与后端二维转三维提升任务。这种分离设计使我们能够利用新构建的TTHQ数据集中的海量二维标注数据训练前端组件，而后端提升网络则仅基于符合物理规律合成的数据进行训练。我们特别对提升模型进行重构，使其能够适应现实场景中常见的干扰因素，如检测缺失和帧率变化。通过整合球体检测器与球台关键点检测器，本方法将概念验证性的提升技术转化为实用、鲁棒且高性能的端到端三维乒乓球轨迹与旋转分析系统。

English

Obtaining the precise 3D motion of a table tennis ball from standard monocular videos is a challenging problem, as existing methods trained on synthetic data struggle to generalize to the noisy, imperfect ball and table detections of the real world. This is primarily due to the inherent lack of 3D ground truth trajectories and spin annotations for real-world video. To overcome this, we propose a novel two-stage pipeline that divides the problem into a front-end perception task and a back-end 2D-to-3D uplifting task. This separation allows us to train the front-end components with abundant 2D supervision from our newly created TTHQ dataset, while the back-end uplifting network is trained exclusively on physically-correct synthetic data. We specifically re-engineer the uplifting model to be robust to common real-world artifacts, such as missing detections and varying frame rates. By integrating a ball detector and a table keypoint detector, our approach transforms a proof-of-concept uplifting method into a practical, robust, and high-performing end-to-end application for 3D table tennis trajectory and spin analysis.

提升乒乓球运动：三维轨迹与旋转估计的鲁棒性实际应用

Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation

摘要

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