# SpinDOE: A ball spin estimation method for table tennis robot

arXiv: https://arxiv.org/abs/2303.03879 · HTML: https://mlsanity.com/paper/2303.03879/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Learning to Play Table Tennis From Scratch using Muscular Robots](https://mlsanity.com/paper/2006.05935/) (2020)
- [i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops](https://mlsanity.com/paper/2207.06572/) (2022)
- [Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation](https://mlsanity.com/paper/2104.09907/) (2021)
- [Spin Detection in Robotic Table Tennis](https://mlsanity.com/paper/1905.07967/) (2019)

## Built on by (most cited first, 8 of 8)

- [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024)
- [Multi-Camera Asynchronous Ball Localization and Trajectory Prediction with Factor Graphs and Human Poses](https://mlsanity.com/paper/2401.17185/) (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)
- [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)
- [A multi-modal table tennis robot system](https://mlsanity.com/paper/2310.19062/) (2023)
- [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)

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 2.4deg 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.