Spin plays a considerable role in table tennis, making a shot's trajectory harder to read and predict. However, the spin is challenging to measure because of the ball's high velocity and the magnitude of the spin values. Existing methods either require extremely high framerate cameras or are unreliable because they use the ball's logo, which may not always be visible. Because of this, many table tennis-playing robots ignore the spin, which severely limits their capabilities. This paper proposes an easily implementable and reliable spin estimation method. We developed a dotted-ball orientation estimation (DOE) method, that can then be used to estimate the spin. The dots are first localized on the image using a CNN and then identified using geometric hashing. The spin is finally regressed from the estimated orientations. Using our algorithm, the ball's orientation can be estimated with a mean error of 2.4{\deg} and the spin estimation has an relative error lower than 1%. Spins up to 175 rps are measurable with a camera of 350 fps in real time. Using our method, we generated a dataset of table tennis ball trajectories with position and spin, available on our project page.

翻译：旋转在乒乓球中扮演着重要角色，使击球轨迹更难以解读和预测。然而，由于球的高速运动和高旋转值，旋转测量极具挑战性。现有方法要么需要极高帧率的相机，要么因依赖可能不可见的球体标识而不可靠。因此，许多乒乓球机器人忽略了旋转，这严重限制了其能力。本文提出了一种易于实施且可靠的旋转估计方法。我们开发了一种点状球体姿态估计（DOE）方法，可用于后续旋转估计。首先利用CNN在图像上定位斑点，然后通过几何哈希进行识别，最后通过估计的姿态回归旋转角速度。使用我们的算法，球体姿态的估计平均误差为2.4°，旋转估计相对误差低于1%。通过350 fps相机可实时测量高达175 rps的旋转。利用该方法，我们生成了包含位置和旋转数据的乒乓球轨迹数据集，该数据集已发布在项目页面上。