# Table tennis ball spin estimation with an event camera

arXiv: https://arxiv.org/abs/2404.09870 · HTML: https://mlsanity.com/paper/2404.09870/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Dense Continuous-Time Optical Flow from Events and Frames](https://mlsanity.com/paper/2203.13674/) (2022)
- [Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking](https://mlsanity.com/paper/2002.05583/) (2020)
- [Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation](https://mlsanity.com/paper/2104.09907/) (2021)
- [Event-based Agile Object Catching with a Quadrupedal Robot](https://mlsanity.com/paper/2303.17479/) (2023)
- [High Speed Event Camera TRacking](https://mlsanity.com/paper/2010.02771/) (2020)
- [Feedback control of event cameras](https://mlsanity.com/paper/2105.00409/) (2021)
- [LuvHarris: A Practical Corner Detector for Event-cameras](https://mlsanity.com/paper/2105.11443/) (2021)
- [CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras](https://mlsanity.com/paper/2401.02826/) (2024)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [Spin Detection in Robotic Table Tennis](https://mlsanity.com/paper/1905.07967/) (2019)
- [SpinDOE: A ball spin estimation method for table tennis robot](https://mlsanity.com/paper/2303.03879/) (2023)
- [PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras](https://mlsanity.com/paper/2205.07657/) (2022)
- [Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts](https://mlsanity.com/paper/2305.15189/) (2023)

## Built on by (most cited first, 11 of 11)

- [3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach](https://mlsanity.com/paper/2408.16638/) (2024)
- [Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer](https://mlsanity.com/paper/2504.19863/) (2025)
- [EEPPR: Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D](https://mlsanity.com/paper/2408.06899/) (2024)
- [Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction](https://mlsanity.com/paper/2506.07860/) (2025)
- [Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation](https://mlsanity.com/paper/2511.20250/) (2025)
- [BiasBench: A reproducible benchmark for tuning the biases of event cameras](https://mlsanity.com/paper/2504.18235/) (2025)
- [Locating Tennis Ball Impact on the Racket in Real Time Using an Event Camera](https://mlsanity.com/paper/2506.08327/) (2025)
- [TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos](https://mlsanity.com/paper/2605.01234/) (2026)
- [VIFSS: View-Invariant and Figure Skating-Specific Pose Representation Learning for Temporal Action Segmentation](https://mlsanity.com/paper/2508.10281/) (2025)
- [Event-based Batting Impact Estimation](https://mlsanity.com/paper/2605.25656/) (2026)
- [Event-based Gaze Control System for Accurate Real-time Spin Estimation in Professional Ball Games](https://mlsanity.com/paper/2606.26780/) (2026)

## Abstract (arXiv)

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 ± 17.3 rps and a spin axis mean error of 32.9 ± 38.2deg in real time for a flying ball.