  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.
