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arxiv: 2606.26780 · v2 · pith:K53HKZLUnew · submitted 2026-06-25 · 💻 cs.CV · eess.IV

Event-based Gaze Control System for Accurate Real-time Spin Estimation in Professional Ball Games

Yunpu Hu , Fabian Schilling , Valentina Cavinato , Asude Aydin , Agis Politis , Ricardo Tapiador Morales , Kirk Y.W. Scheper , Peter D\"urr
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Naoya Takahashi
This is my paper

Pith reviewed 2026-07-01 06:29 UTC · model grok-4.3

classification 💻 cs.CV eess.IV
keywords event-based visionspin estimationactive visionball trackingtable tenniscontrast maximizationreal-time systemsgalvanometer mirrors
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The pith

An event-based active vision system tracks unmodified balls and estimates their spin in real time during professional matches.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a system using an event camera, high-speed pan-tilt mirrors, and a focus-tunable lens to track and measure ball spin despite high speeds and small size. A hybrid approach combines 2D event-based detection for centering with 3D positions from an external localization system for re-initialization and continuous focus. The offline s-CMax method performs contrast maximization on the sphere and reaches state-of-the-art accuracy on static balls across sports. An online version uses an uncertainty-aware CNN trained on offline labels plus GPU-accelerated refinement to deliver real-time performance in table tennis.

Core claim

The offline s-CMax method achieves mean magnitude and axis errors of 1.2 percent and 1.5 degrees on static balls in table tennis, baseball, tennis, and golf. The online method, tested in professional table tennis matches with a three-view setup, shows 8.8 percent magnitude and 6.4 degrees axis mismatch relative to the offline method while running at 3 ms latency and 750 Hz throughput.

What carries the argument

s-CMax contrast maximization performed on the sphere, paired with hybrid 2D event-based detection and 3D re-initialization for active gaze control.

If this is right

  • The offline method supplies high-accuracy spin data for analysis in table tennis, baseball, tennis, and golf without ball modifications.
  • The online method supports real-time spin feedback at 750 Hz during live professional table tennis matches.
  • The active vision hardware maintains focus and centering on fast-moving balls with 3 ms latency.
  • Pseudo-ground-truth labels from the offline method enable training of real-time neural networks for spin estimation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same hardware and tracking pipeline could be adapted to other high-speed ball sports by swapping the external localization module.
  • Combining the spin estimates with existing physics-based trajectory models would allow more precise prediction of ball paths.
  • The 750 Hz throughput opens the possibility of feeding spin data into automated coaching systems or broadcast graphics in real time.

Load-bearing premise

The external 3D ball localization system supplies positions accurate enough to re-initialize and sustain tracking without frequent target loss during fast professional play.

What would settle it

Observation of frequent tracking loss or spin errors larger than 10 percent magnitude or 10 degrees axis mismatch across multiple full professional matches would disprove the reliability of the online method.

Figures

Figures reproduced from arXiv: 2606.26780 by Agis Politis, Asude Aydin, Fabian Schilling, Kirk Y.W. Scheper, Naoya Takahashi, Peter D\"urr, Ricardo Tapiador Morales, Valentina Cavinato, Yunpu Hu.

Figure 1
Figure 1. Figure 1: The gaze control system (GCS) actively tracks the ball and measures its spin in ball games. 1 Introduction Spin plays a key role in many ball sports due to its effect on the ball’s flight and contact dynamics. Athletes routinely apply spin to control the ball’s flight path, deceive opponents, or gain a tactical advantage. Accurate spin estimation is thus of vital importance for performance analysis, sports… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the information flow in the GCS. the event stream to set velocity constraints. However, all existing event-based methods employ static, wide-angle cameras, which severely limits the spatial resolution on the ball and conflates translational motion with spin, a funda￾mental challenge for in-flight estimation. Our work addresses both limitations through active tracking with a telephoto lens, prop… view at source ↗
Figure 3
Figure 3. Figure 3: The effect of different ball types on the s-CMax magnitude and axis error. The bottom row shows a time surface representation of the different balls when their pattern is visible, spinning at 1 krpm. We recorded a dataset with different balls in a sidespin configuration with a total of 19 raw event files, 9 files with the table tennis ball from 1-9 krpm, 4 files for the golf and tennis ball from 1-4 krpm, … view at source ↗
Figure 4
Figure 4. Figure 4: CNN magnitude and axis errors on the held-out test set for different uncertainty percentiles and magnitude ranges. 5 Conclusion We presented an event-based active vision system for real-time ball tracking and spin estimation, combining an event camera with a focus-tunable telephoto lens and fast galvanometer mirrors to obtain a magnified, motion-compensated view of unmodified balls in flight. The hybrid tr… view at source ↗
read the original abstract

Spin plays a crucial role in many ball sports due to its effect on the trajectory of the ball. Vision-based estimation of the ball's spin during a game with conventional cameras is challenging due to the ball's small size, high speed, and fast rotation. To address these challenges, we propose an event-based active vision system that can track unmodified balls and measure their spin in real time. The system consists of an event camera for its high temporal resolution and minimal motion blur, high-speed pan/tilt galvanometer mirrors to keep the ball in the field of view, and a low-latency focus-tunable telephoto lens to increase the spatial resolution on the ball and keep it in focus. To track the ball, we use a hybrid approach that combines 2D event-based detection for centering and 3D positions from a ball localization system for re-initialization. For high-accuracy spin estimation, we propose an offline method that performs contrast maximization on the sphere (s-CMax). This method achieves state-of-the-art accuracy on static balls across multiple sports (table tennis, baseball, tennis, and golf), with mean magnitude and axis errors of 1.2% and 1.5 degrees, respectively. We then develop a low-latency online method for table tennis as a case study in real-time applications. This method uses an uncertainty-aware convolutional neural network trained on pseudo-ground-truth spin labels from the offline approach, combined with a GPU-accelerated batch implementation of contrast maximization for refinement. We demonstrate reliable tracking and spin estimation with a three-view setup during professional table tennis matches, with high accuracy (8.8% magnitude and 6.4 degrees axis mismatch w.r.t. the offline method), 3 ms latency, and 750 Hz throughput.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript presents an event-based active vision system for real-time spin estimation of unmodified balls in professional sports. It introduces an offline s-CMax method for high-accuracy spin estimation on static balls across sports, achieving mean errors of 1.2% in magnitude and 1.5 degrees in axis. For real-time application in table tennis, it proposes a hybrid tracking approach using 2D event-based detection and external 3D positions, combined with an uncertainty-aware CNN trained on pseudo-ground-truth from the offline method, reporting 8.8% magnitude and 6.4 degrees axis mismatch to the offline reference, 3 ms latency, and 750 Hz throughput during matches.

Significance. If the reported accuracies hold under independent validation and sustained tracking, the work would advance event-based vision for sports by enabling markerless high-speed spin measurement. The offline s-CMax accuracy on static balls and the low-latency online pipeline are potential strengths if the dependency on pseudo-ground-truth and external localization can be decoupled.

major comments (3)
  1. [Results (professional table tennis matches)] Results section on professional table tennis matches: the claim of 'reliable tracking and spin estimation' during matches provides no quantitative metrics on track duration, loss frequency, re-initialization rate, or fraction of match time under active control. This is load-bearing for the central claim, as accuracy figures may reflect only short successful segments rather than sustained play.
  2. [Online method description and results] Online method and results: accuracy is reported exclusively as mismatch w.r.t. the offline s-CMax (8.8% magnitude, 6.4° axis), which also supplies the pseudo-ground-truth labels for CNN training. This circular dependency prevents evaluation against independent ground truth and is load-bearing for the real-time accuracy claim.
  3. [Tracking approach] Tracking approach: the hybrid 2D event-based centering plus external 3D re-initialization is described, yet no analysis is given of how 3D localization errors propagate into spin estimates or of re-initialization robustness under fast professional play.
minor comments (1)
  1. [Abstract] Abstract: the 'three-view setup' is mentioned without details on camera configuration or its role in the reported throughput and accuracy.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: Results section on professional table tennis matches: the claim of 'reliable tracking and spin estimation' during matches provides no quantitative metrics on track duration, loss frequency, re-initialization rate, or fraction of match time under active control. This is load-bearing for the central claim, as accuracy figures may reflect only short successful segments rather than sustained play.

    Authors: We agree that quantitative metrics on sustained tracking performance are essential to support the reliability claim. In the revised manuscript we will add statistics computed from the professional match recordings, specifically average track duration, loss frequency, re-initialization rate, and fraction of match time under active control. revision: yes

  2. Referee: Online method and results: accuracy is reported exclusively as mismatch w.r.t. the offline s-CMax (8.8% magnitude, 6.4° axis), which also supplies the pseudo-ground-truth labels for CNN training. This circular dependency prevents evaluation against independent ground truth and is load-bearing for the real-time accuracy claim.

    Authors: The offline s-CMax has been validated independently against known ground-truth spin on static balls (1.2 % magnitude, 1.5° axis errors). The online CNN is trained to replicate this reference in real time; the reported 8.8 % / 6.4° figures therefore quantify how closely the low-latency pipeline matches the offline reference under match conditions. We will revise the text to state explicitly that accuracy is measured relative to the offline method and to discuss the implications of this reference choice. revision: partial

  3. Referee: Tracking approach: the hybrid 2D event-based centering plus external 3D re-initialization is described, yet no analysis is given of how 3D localization errors propagate into spin estimates or of re-initialization robustness under fast professional play.

    Authors: We will add a sensitivity study quantifying the effect of 3D localization error on the final spin estimates together with empirical re-initialization success rates observed during the professional matches. revision: yes

Circularity Check

0 steps flagged

No circularity: offline validated on static balls vs external SOTA; online accuracy explicitly reported as mismatch to offline pseudo-GT

full rationale

The paper's derivation chain separates the offline s-CMax (contrast maximization on the sphere) which is claimed to achieve SOTA accuracy on static balls across sports with explicit mean errors (1.2% magnitude, 1.5 deg axis), from the online CNN+refinement method trained on pseudo-labels generated by that offline stage. The online results are presented only as relative mismatch (8.8% magnitude, 6.4 deg axis) to the offline reference rather than as an independent absolute accuracy claim. No equation or step reduces by construction to its own inputs, no self-citation is load-bearing for a uniqueness theorem or ansatz, and the evaluation does not rename a known result or smuggle an ansatz. The hybrid tracking description does not contain a self-referential loop. This is a standard pseudo-label training setup with transparent relative reporting; the derivation remains self-contained against external benchmarks for the offline stage.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the approach relies on standard event-camera properties and existing mirror/lens hardware without new postulated physical entities.

pith-pipeline@v0.9.1-grok · 5893 in / 1213 out tokens · 33922 ms · 2026-07-01T06:29:46.017558+00:00 · methodology

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