# Spin Detection in Robotic Table Tennis

arXiv: https://arxiv.org/abs/1905.07967 · HTML: https://mlsanity.com/paper/1905.07967/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 9 of 9)

- [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024)
- [SpinDOE: A ball spin estimation method for table tennis robot](https://mlsanity.com/paper/2303.03879/) (2023)
- [Multi-Camera Asynchronous Ball Localization and Trajectory Prediction with Factor Graphs and Human Poses](https://mlsanity.com/paper/2401.17185/) (2024)
- [Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction](https://mlsanity.com/paper/2506.07860/) (2025)
- [A multi-modal table tennis robot system](https://mlsanity.com/paper/2310.19062/) (2023)
- [TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos](https://mlsanity.com/paper/2605.01234/) (2026)
- [Catching Spinning Table Tennis Balls in Simulation with End-to-End Curriculum Reinforcement Learning](https://mlsanity.com/paper/2503.01251/) (2025)
- [1000 Rallies: An Event-Camera Dataset and Real-Time Learned Ball-State Estimation for Robotic Table Tennis](https://mlsanity.com/paper/2606.25620/) (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)

In table tennis, the rotation (spin) of the ball plays a crucial role. A table tennis match will feature a variety of strokes. Each generates different amounts and types of spin. To develop a robot that can compete with a human player, the robot needs to detect spin, so it can plan an appropriate return stroke. In this paper we compare three methods to estimate spin. The first two approaches use a high-speed camera that captures the ball in flight at a frame rate of 380 Hz. This camera allows the movement of the circular brand logo printed on the ball to be seen. The first approach uses background difference to determine the position of the logo. In a second alternative, we train a CNN to predict the orientation of the logo. The third method evaluates the trajectory of the ball and derives the rotation from the effect of the Magnus force. This method gives the highest accuracy and is used for a demonstration. Our robot successfully copes with different spin types in a real table tennis rally against a human opponent.