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REVIEW 3 major objections 5 minor 1 cited by

An Event-Based Perception Pipeline for a Table Tennis Robot

T0 review · 3 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read An event-camera-only perception pipeline can track a table tennis ball at roughly 28 times the update rate of a frame-based system while matching its pixel accuracy.

desk verdict Solid event-only perception pipeline with a well-supported 28x update-rate advantage; the trajectory-prediction benefit is confounded by camera placement and in-sample EM fitting. read the letter →

arxiv 2502.00749 v1 pith:M57Y6R7G submitted 2025-02-02 cs.RO cs.CV

Andreas Ziegler , Thomas Gossard , Arren Glover , Andreas Zell This is my paper · ORCID
classification cs.ROcs.CV
keywords tabletennisroboteventcameraevent-basedperceptionEROSsurfaceballdetectiontrajectorypredictionExtendedKalmanFilter
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that a table tennis robot can perceive the ball using only event-based cameras, with no frame-based cameras at all, and that this is the first such real-time pipeline. The proposed pipeline detects the ball at roughly 28 times the update rate of a conventional frame-based pipeline, about 4,140 updates per second versus 149, while keeping nearly identical pixel accuracy (1.34 vs. 1.32 pixels). Because the ball is reported far more frequently, an extended Kalman filter fed with these positions produces lower uncertainties in the ball's position, velocity, and spin, which matters for a robot that has to swing back within a fraction of a second. The paper argues that event-based cameras remove the frame-rate bottleneck and the motion blur that limits frame-based systems at high ball speeds.

What carries the argument

The load-bearing object is the EROS (Exponential Reduced Ordinal Surface) event representation, a decaying gray-level surface that is updated incrementally with each incoming event, so the latest state of the scene is always available without recomputing from scratch. The pipeline runs two threads: one updates EROS event-by-event, and the other samples the surface and runs a fast C++ Hough circle detector, first at full resolution for initialization and then within a region of interest centered on the previous detection. EROS makes the representation velocity-independent, so a fast-moving ball appears as a compact circle rather than a blurred trail, and its cheap per-event update lets the pipeline keep up with event streams of roughly 10 million events per second.

What would settle it

Rerun the experiment with the two event cameras placed at the same four ceiling-corner positions used by the frame-based cameras, keeping all other settings identical; if the event-based pipeline's lower trajectory uncertainty disappears, the reported advantage is caused by camera placement rather than by the higher update rate.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that a perception pipeline built entirely on event-based cameras—two synchronized Prophesee EVK4 sensors whose asynchronous event stream updates an EROS surface event-by-event—can replace a frame-based pipeline in a table tennis robot. In their measurements, the proposed pipeline reaches (4.14 ± 0.10) × $10^{3}$ position updates per second with a mean pixel error of 1.34 ± 0.79, compared with 149 Hz and 1.32 pixels for the frame-based baseline from [3]. When the resulting 3D positions are fed into an extended Kalman filter, the event-based pipeline yields lower positional error and lower uncertainty in position, velocity, and spin throughout most of the flight. The authors conclude that the update rate of an event-based pipeline is no longer bounded by the sensor but by the detection algorithm, and that the higher rate is directly useful for robot control in the 0.1–1 s available before the robot must hit the ball.

Load-bearing premise

The comparison assumes that the event cameras and the frame-based cameras are placed so similarly that the event pipeline's lower errors and uncertainties come from its higher update rate, not from its more favorable camera positions; the paper itself notes that the two setups differ, with event cameras on the side and frame cameras on the ceiling.

Editorial extensions

If this is right

  • An event-based-only pipeline can match a frame-based pipeline's pixel accuracy while delivering roughly 28 times more position updates per second, so frame rate is no longer the bottleneck for high-speed ball perception.
  • Feeding these frequent updates into an extended Kalman filter reduces the uncertainty of the ball's position, velocity, and spin earlier in the flight, giving the robot controller a better basis for planning a return stroke within the 0.1–1 s flight time.
  • The pipeline's update rate is set by the detection algorithm's runtime (about 91 µs in ROI mode), not by the camera's frame rate, so faster detection algorithms would directly translate into even more frequent updates.
  • Since EROS is updated event-by-event, the representation always reflects the latest events, avoiding the motion blur that degrades frame-based detection of fast balls.
  • The full pipeline, including source code, is released, so other groups can integrate event-only perception into their table tennis robots without building the representation and detector from scratch.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • It is a plausible extension that the same event-by-event EROS plus Hough circle design would transfer to other fast ball sports (tennis, cricket, badminton) or to evasive robotics, wherever the limiting factor is how often the sensor can report position.
  • The paper's comparison is not fully controlled: the event cameras sit on the side while the frame-based cameras hang from four ceiling corners, and the ground-truth 2D positions come from e2vid reconstructions that were manually cleaned; a controlled side-by-side with co-located cameras and an independent high-speed ground truth would isolate the contribution of update rate from the contribution o
  • Because the EROS update cost scales with the number of events, the pipeline's advantage may shrink in low-contrast or slow-motion scenes where the ball generates few events; an event-based pipeline shines when motion is fast and high-rate updates are needed.
  • A testable next step is to replace the Hough circle detector with a learned detector operating on EROS surfaces; since the ROI detection already runs in about 91 µs, a learned detector of similar speed could raise accuracy while preserving the update-rate advantage.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes what it claims to be the first real-time perception pipeline for a table tennis robot that uses only event-based cameras. The pipeline combines an EROS event surface, updated event-by-event, with a fast Hough-based circle detector running as-fast-as-possible in a separate thread. The experiments compare this pipeline with a frame-based baseline and two event-based baselines on 2D detection accuracy, runtime, update rate, and EKF-based trajectory prediction. The headline results are an update rate of (4.14 ± 0.10) × 10^3 updates/s with a pixel error of 1.34 ± 0.79 pixels, versus 149 Hz and 1.32 pixels for the frame-based baseline, and lower position, velocity, and spin uncertainties in the trajectory-prediction experiment. The code is publicly released.

Significance. The update-rate and runtime measurements are direct and clean, and they support the paper's central quantitative claim that an event-based pipeline can provide roughly 28 times more ball-position updates per second than a frame-based pipeline while retaining comparable 2D accuracy. This is a useful and creditable contribution, and the release of the pipeline is a practical benefit to the community. However, the more ambitious claim that the higher update rate 'results in lower mean errors and uncertainties' for position, velocity, and spin rests on a trajectory-prediction comparison that is confounded by different camera geometries and by in-sample EM fitting of the EKF parameters. The accuracy comparison also uses ground truth derived from reconstructed event frames with manual cleaning. If the trajectory-prediction claim is either supported by a controlled comparison or appropriately softened, the paper would be a solid and useful systems contribution.

major comments (3)
  1. [Section IV-E] The trajectory-prediction comparison, which grounds the conclusion that the higher update rate leads to lower mean errors and uncertainties, is confounded by different camera geometries. The event cameras are mounted on the side of the robot while the frame-based cameras are placed on the four ceiling corners, as shown in Fig. 2, and the paper itself states in Section IV-E that the difference in positional error 'is partially influenced by the different camera setups.' With different baselines, resolutions, and viewing angles, the lower positional errors and uncertainties in Figs. 7 and 8 cannot be attributed to the update rate. Please either compare both pipelines using the same camera geometry (for example, frame-based detections derived from the same views, or event-based detections from the ceiling-mounted setup), or explicitly limit the paper's claim to update rate and 2D accuracy and remove the trajectory-prediction benefit claim.
  2. [Section IV-E] The EKF transition covariance, observation covariance, and initial-state statistics are fitted via EM on the same data used for the predictions shown in Figs. 7 and 8, and the text does not describe any held-out trajectory. In-sample EM fitting can artificially reduce both the errors and the reported uncertainties, so the comparison does not demonstrate that the event-based update rate itself improves prediction. Please add a train/test split or cross-validation, report the number of trajectories used, and show the variability across trajectories or folds rather than plotting a single trajectory.
  3. [Section IV-B] The ground-truth 2D positions for the accuracy comparison in Table I are obtained by running a circle detector on e2vid-reconstructed frames from the same event streams and then manually removing inaccurate detections. This ground truth is not independent of the event-based methods being evaluated, and the manual cleaning is not quantified. Please validate the pixel-error comparison against an independent source, such as a high-speed frame camera or motion capture, or report the sensitivity of the results to the reconstruction parameters and cleaning decisions.
minor comments (5)
  1. [Section II-C] The placeholder citation '[ ?]' in the discussion of speed-invariant time surfaces must be resolved before publication; a missing reference is not acceptable in the final version.
  2. [Table I] The entry for the event-based median-filter baseline ('0.000 08(1.1)') mixes the update runtime and the initialization runtime in an unclear way; please use a consistent notation for runtime columns and define what the parenthetical values represent.
  3. [Section IV-D] The trajectory durations used to compute update rates are measured manually in Metavision Studio; since update rate is one of the paper's headline results, please state the uncertainty of this manual measurement or replace it with an automatic timestamp-based duration.
  4. [Figure 2] The caption states that the camera setup has 'baselines of 3m to 5m'; please clarify whether this refers to the baseline distance between cameras or to the distance from the table/setup, as the schematic alone is ambiguous.
  5. [Algorithm 1] The 'Require:' line contains 'd = 0.31.0/kEROS', which appears to be a typographical error; should it read 'd = 0.31/kEROS' or 'd = 1.0/kEROS'? Please correct the notation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported update-rate, accuracy, and uncertainty results are empirical measurements against external baselines and not derived from fitted inputs.

full rationale

The paper's central claims rest on direct measurements. The update rate in Table I is computed as the number of position updates divided by the measured trajectory duration and is compared against the frame-based baseline (149 Hz) and two event-based baselines; the pixel error is measured against ground-truth 2D positions obtained from e2vid-reconstructed frames with manual cleaning (Section IV-B). The trajectory-prediction comparison in Section IV-E uses the EKF from [3] with EM-tuned transition and observation covariances; while the EM fit is performed on the evaluated data and the camera geometries differ between the event-based and frame-based systems, this is a statistical and experimental-design limitation rather than a circular derivation, because the reported lower uncertainties are not equal by construction to the fitted parameters or to the update rate. EROS is adopted from prior published work by a co-author, but it is used as a component of the pipeline and is not invoked as an unverified premise to force the paper's conclusions. No step reduces to its own input by definition, so no circularity is found.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The pipeline introduces no new physical entities. It depends on hand-set parameters (kEROS, camera bias settings, Hough detector settings, ROI size), on EM-fitted EKF noise covariances, and on assumptions inherited from prior work about EROS velocity independence, the mid-air ball dynamics model, and the reliability of reconstructed-frame ground truth. The EM-fitted covariances are particularly notable because the reported uncertainty reduction is partly a result of fitting.

free parameters (5)
  • kEROS = 10
    Update region size parameter in Algorithm 1, chosen by hand; it controls the decay of the EROS surface and affects detection performance.
  • EKF noise covariances and initial state = Not reported in paper; optimized via EM on the same trajectories used for evaluation
    Transition covariance, observation covariance, initial state means, and initial covariance are estimated by the EM algorithm in Section IV-E, making the uncertainty comparison in-sample.
  • Hough circle detector parameters = Not specified
    Radius range, thresholds, and accumulator settings are not reported, but they are necessary to reproduce the detection results.
  • Event camera bias and STC/Trail filter settings = Not quantified
    Section III-A states the bias settings were configured to minimize noise and that an Event Trail Filter was used, but the exact values are not given.
  • ROI size = Not specified
    Section III-D describes an ROI centered on the previous ball position, but its dimensions are not stated.
assumptions (4)
  • domain assumption EROS with kEROS=10 is velocity-independent enough for the Hough circle detector to work on the surface.
    The velocity-independence of EROS is inherited from prior work [13][14]; the paper does not re-derive it and relies on it for detection quality.
  • domain assumption The mid-air ball trajectory physics model used in the EKF is accurate.
    The EKF uses the nonlinear projectile model with drag and spin effects from [40] and [3]; this is a standard assumption in the table tennis robot literature.
  • ad hoc to paper Ground truth 2D positions from e2vid reconstructed frames are reliable after manual cleaning.
    Section IV-B obtains 286 ground truth positions from reconstructed frames and manually removes inaccurate detections, which can bias the accuracy comparison.
  • domain assumption The wand-based calibration of the event camera rig is accurate.
    Calibration from [33] is used to triangulate 3D positions; no calibration residual error is reported.

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Cite this review

Pith. "Pith review of An Event-Based Perception Pipeline for a Table Tennis Robot." pith.science (2026). https://pith.science/paper/M57Y6R7G

@misc{pith2026250200749,
  author       = {Pith},
  title        = {Pith review of: An Event-Based Perception Pipeline for a Table Tennis Robot},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M57Y6R7G}},
  note         = {Machine review of arXiv:2502.00749}
}
read the original abstract

Table tennis robots gained traction over the last years and have become a popular research challenge for control and perception algorithms. Fast and accurate ball detection is crucial for enabling a robotic arm to rally the ball back successfully. So far, most table tennis robots use conventional, frame-based cameras for the perception pipeline. However, frame-based cameras suffer from motion blur if the frame rate is not high enough for fast-moving objects. Event-based cameras, on the other hand, do not have this drawback since pixels report changes in intensity asynchronously and independently, leading to an event stream with a temporal resolution on the order of us. To the best of our knowledge, we present the first real-time perception pipeline for a table tennis robot that uses only event-based cameras. We show that compared to a frame-based pipeline, event-based perception pipelines have an update rate which is an order of magnitude higher. This is beneficial for the estimation and prediction of the ball's position, velocity, and spin, resulting in lower mean errors and uncertainties. These improvements are an advantage for the robot control, which has to be fast, given the short time a table tennis ball is flying until the robot has to hit back.

Figures

Figures reproduced from arXiv: 2502.00749 by the authors.

Figure 1
Figure 1. Background: The industrial robot arm of our table tennis robot [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Our camera setup consists of four frame-based cameras (in blue) and two event-based cameras (in red) with baselines of 3m to 5m. Only the event-based cameras are used in this work. Schematic is up to scale. event-by-event update of our approach. In the last part, the as￾fast-as-as-possible ball detection is covered in Section III-D. A. Setup We used a table tennis robot setup similar to [3], but extended it with two… view at source ↗
Figure 3
Figure 3. The first thread of our event-based perception pipeline. We process [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Example surface using the EROS algorithm to enable velocity [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 4
Figure 4. Figure 4: The second thread of our event-based perception pipeline. We [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 6
Figure 6. Figure 6: (a) A visualization of the EROS surface on the data captured for the [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 7
Figure 7. Figure 7: The absolute positional error and the positional uncertainty of the [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

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Forward citations

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.