Spin plays a pivotal role in ball-based sports. Estimating spin becomes a key
skill due to its impact on the ball's trajectory and bouncing behavior. Spin
cannot be observed directly, making it inherently challenging to estimate. In
table tennis, the combination of high velocity and spin renders traditional low
frame rate cameras inadequate for quickly and accurately observing the ball's
logo to estimate the spin due to the motion blur. Event cameras do not suffer
as much from motion blur, thanks to their high temporal resolution. Moreover,
the sparse nature of the event stream solves communication bandwidth
limitations many frame cameras face. To the best of our knowledge, we present
the first method for table tennis spin estimation using an event camera. We use
ordinal time surfaces to track the ball and then isolate the events generated
by the logo on the ball. Optical flow is then estimated from the extracted
events to infer the ball's spin. We achieved a spin magnitude mean error of
$10.7 \pm 17.3$ rps and a spin axis mean error of $32.9 \pm 38.2\deg$ in real
time for a flying ball.

使用事件摄像机利用光流从提取的事件中推断球的自旋，在实时环境中实现飞行球的自旋估计，平均自旋幅度误差为 10.7±17.3 rps，自旋轴平均误差为 32.9±38.2°。

使用事件相机进行乒乓球旋转估计

Table tennis ball spin estimation with an event camera

In recent years, robotic table tennis has become a popular research challenge
for perception and robot control. Here, we present an improved table tennis
robot system with high accuracy vision detection and fast robot reaction. Based
on previous work, our system contains a KUKA robot arm with 6 DOF, with four
frame-based cameras and two additional event-based cameras. We developed a
novel calibration approach to calibrate this multimodal perception system. For
table tennis, spin estimation is crucial. Therefore, we introduced a novel, and
more accurate spin estimation approach. Finally, we show how combining the
output of an event-based camera and a Spiking Neural Network (SNN) can be used
for accurate ball detection.

基于高准确度视觉检测和快速机器人反应的改进型乒乓球机器人系统，通过多模态感知系统的创新校准方法实现了对旋转估计至关重要的精确度更高的旋转估计方法，并展示了结合基于事件的摄像头和脉冲神经网络（SNN）输出进行准确球检测的方法。