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REVIEW 2 major objections 6 minor 54 references

Event Ellipsometer: Event-based Mueller-Matrix Video Imaging

T0 review · 2 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Rotating quarter-wave plates and an event camera estimate the normalized Mueller matrix per pixel at 30 fps.

desk verdict Clever combination of event cameras and rotating retarders for 30 fps Mueller-matrix imaging; the printed derivative drops the ω factor, and 'dynamic' really means quasi-static within each frame. read the letter →

arxiv 2411.17313 v2 pith:U75YPHW2 submitted 2024-11-26 cs.CV

Ryota Maeda , Yunseong Moon , Seung-Hwan Baek This is my paper · ORCID
classification cs.CV
keywords eventcameraMuellermatrixellipsometrypolarimetricimagingdynamicscenecapturerotatingquarter-waveplatehighrangephotoelasticity
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's central claim is that ellipsometry, the measurement of a material's 4x4 Mueller matrix, can be taken from minutes-long static captures down to 33 ms video frames. The system rotates two quarter-wave plates continuously, one in front of a light source and one in front of an event camera, so that polarization modulation alone drives the camera's events. A derivation relates the time differences between consecutive events to the normalized Mueller matrix of each pixel, and a two-stage estimator with physical-validity filtering reconstructs per-pixel matrices at 30 fps. Experiments report a mean-squared error of 0.045 on known polarimetric samples and show working Mueller-matrix videos of human faces, hair, gelatine under stress, and transparent tape.

What carries the argument

The central object is the dual-rotating-retarder ellipsometer arrangement: a linear polarizer and quarter-wave plate in front of the light source, and another quarter-wave plate and linear polarizer in front of the event camera, with the camera-side wave plate rotating five times faster than the source-side one. This continuous rotation encodes the sixteen Mueller-matrix elements into the temporal intensity profile seen by the event camera. The key identity is the event-camera threshold relation $\partial \log I_t / \partial t = p_k C / \Delta t_k$, which converts measured inter-event time differences into linear equations in the unknown Mueller matrix, so the camera's asynchronous events are used directly as the measurement signal.

What would settle it

Move a known polarimetric target, such as a linear polarizer, fast enough that motion-induced events outnumber modulation-induced events, reconstruct the Mueller matrix from that frame, and check whether the mean-squared error against the known ground truth exceeds the 0.045 reported for static scenes.

Watch

Extended reading notes

Core claim

The paper establishes that the asynchronous event stream of a dual rotating quarter-wave-plate ellipsometer is sufficient to estimate the normalized Mueller matrix per pixel. In the image formation model $I_t = A_t \hat{M}$, $\hat{M}$ is the vectorized Mueller matrix and $A_t$ captures the time-varying polarimetric modulation; because the event camera responds to $\partial \log I_t / \partial t$, the observed time differences $\Delta t_k$ of events satisfy $B_{t_k} \hat{M} = 0$ for a known system matrix $B$. Stacking these constraints over events in one frame gives a weighted least-squares problem, solved by SVD, then refined by Cloude's physical-validity projection and a spatiotemporal propagation scheme. The result is a 30 fps Mueller-matrix video for non-planar, dynamic, and high-dynamic-range scenes, reconstructed from 33 ms of events per frame rather than many minutes of frame-based capture.

Load-bearing premise

The load-bearing premise is that within each 33 ms frame the scene's Mueller matrix is constant and that the rotating wave plates, not object motion, dominate event generation.

Editorial extensions

If this is right

  • Mueller-matrix imaging extends from static samples to dynamic scenes, because capture time per frame drops from minutes to 33 ms.
  • Non-planar objects can be measured without sacrificing sensor spatial resolution, unlike snapshot metasurface-based approaches.
  • High-dynamic-range scenes are captured in a single event stream, without bracketed exposures.
  • Demonstrated applications include photoelastic stress visualization in transparent materials, transparent tape detection, and polarimetric capture of human faces and hair.
  • The reconstructed quantity is the normalized Mueller matrix $M/M_{00}$, and recovering absolute scale is left as future work.

Reading between the lines

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

  • Pairing this event camera with a conventional intensity sensor could recover the absolute, unnormalized Mueller matrix, since the missing scale is exactly the intensity the event camera discards.
  • The fixed 33 ms frame boundary could become an adaptive window that detects when motion-induced events dominate, yielding a motion-aware confidence per pixel.
  • Adding a narrow bandpass filter would remove the wavelength-dependent mixing of the white LED and monochrome sensor, at the cost of lower light levels.
  • A GPU implementation of the reconstruction would likely close the gap between 30 fps capture and the current offline reconstruction time, making live ellipsometric video feasible.
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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

2 major / 6 minor

Summary. This paper presents Event Ellipsometer, a Mueller-matrix video imaging system that pairs a Prophesee EVK4 event camera with two fast-rotating quarter-wave plates (QWPs), one in the illumination path and one in the detection path, plus fixed linear polarizers. The authors derive a Stokes-Mueller image formation model in which the logarithmic intensity derivative is expressed as a ratio of linear forms in the vectorized Mueller matrix (Eqs. (4)-(7)), and they formulate reconstruction as a per-pixel weighted least-squares SVD with Cloude physical-validity filtering followed by spatio-temporal propagation and refinement. A one-time calibration estimates the per-pixel contrast threshold and the QWP offset angles. Experiments include synthetic validation, real measurements of known optical elements and a metal plate, and demonstrations on photoelasticity, transparent tape detection, dynamic human face/hair, and HDR scenes. The paper claims Mueller-matrix video capture at 30 fps, i.e., a 33 ms frame duration, and reports mean-squared errors on the order of 0.013-0.020 for known elements.

Significance. The contribution is potentially significant: if the 30 fps capture claim holds, it would extend Mueller-matrix ellipsometry from static samples requiring minutes of acquisition to dynamic scenes, opening new applications in material inspection, photoelasticity, and human capture. The paper provides a substantial amount of engineering detail (hardware prototype, part list, motor synchronization, calibration procedures) that would aid reproducibility, and it clearly identifies limitations such as the non-real-time reconstruction pipeline. The synthetic validation and the comparison with a frame-based method in the supplement are useful. However, the significance hinges on two points: the correctness of the derivative in Eq. (5), which as printed is wrong, and the validity of the dynamic-scene claim, which is only weakly supported.

major comments (2)
  1. [Section 4, Eq. (5)] The printed expression for dA_t/dt omits the angular velocity factors from every nonzero entry. For example, the derivative of α1^2 is -4ω α1α2, but the paper lists -4α1α2; similarly the α3/α4 terms miss the factor 5ω. With ω = 30π rad/s, the derivative term in Eq. (7) is consequently smaller by two orders of magnitude than it should be, and the units of the B_tk matrix become inconsistent. Because Eq. (7) is the basis of the entire reconstruction (Section 5) and the reported experimental MSE values, the results cannot be reproduced from the equations as written. The authors must correct Eq. (5) (and any dependent equations in the supplement) and confirm whether the implementation used the corrected formula.
  2. [Sections 3 and 7; Supplement 5.3] The paper's headline claim is Mueller-matrix video imaging at 30 fps for dynamic scenes, but the method assumes that the scene Mueller matrix is constant within each 33 ms frame (Eq. (4)) and that event generation is dominated by the rotating QWPs. Supplement 5.3 explicitly states that reconstruction accuracy degrades when object motion dominates event generation and reports motion artifacts in the human hair scene. Yet the paper offers no quantitative evaluation of how reconstruction error depends on motion amplitude, motion speed, or the ratio of motion-induced to polarization-induced events. The dynamic demonstrations (facial expression change, head rotation) therefore show that plausible images can be produced, not that correct Mueller matrices are recovered under the demonstrated dynamics. Please add a controlled motion experiment (e.g., a known Mueller-matrix target undergoing translations or rotations with varying speed) or substantially weaken the dynamic-scene claim to quasi-static scenes.
minor comments (6)
  1. [Introduction and Section 7] The introduction states 'achieving a mean-squared error of 0.045 for materials with known Mueller matrices,' but Figure 5(a) reports MSEs of 0.016, 0.015, 0.013, 0.020, and 0.020; please clarify which number is the reported MSE and harmonize the text.
  2. [Supplement 3.5] The depolarization factor α is set to 0.8 for the reference QWP model without a sensitivity analysis; please report how the calibration error varies with α or justify the choice.
  3. [Supplement 4.6, Eq. (17)] The polarization preservation formula reads ρ = 1/3(M∆,11 + M∆,20 + M∆,11), which appears to contain a typo (likely M∆,22 instead of M∆,20); please correct.
  4. [Section 5.2, Eq. (12)] The notation '1' for the all-ones matrix and N for the Gaussian perturbation should be defined more explicitly to avoid confusion with the identity matrix.
  5. [Figure 2(b)] The timeline schematic is hard to read at the current size; the event markers and the log-intensity trace should be enlarged for legibility.
  6. [Section 2] The statement that existing event-based vision methods cannot capture full polarization reflectance properties as a Mueller-matrix image would benefit from a citation to the most recent works in event-based polarimetry.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Mueller-matrix reconstruction is an independent inverse solve of B_t M = 0 using separately calibrated sensor parameters, and the central claims are validated on analytic optical elements, synthetic data, and a frame-based comparison.

full rationale

The derivation chain is self-contained. The forward model starts from physical Mueller calculus: I_t = [L(0)Q(θ2,t)MQ(θ1,t)L(0)s]_0 (Eq. 1), which is rearranged exactly into the linear system I_t = A_t M̂ (Eq. 2). Differentiating the logarithm gives Eq. (4), and equating it with the event camera's standard contrast-threshold relation p_k C/Δt_k (Eq. 6) yields the homogeneous reconstruction system B_t M̂ = 0 (Eq. 7). The reconstructed M̂ is the weighted least-squares/robust solution of these equations; no target Mueller matrix of any validation object is used during reconstruction. The two calibrated quantities, C and (i1, i2), are obtained independently: C is fit from an LED intensity ramp via Δt = pC(t + b/a) (Supplement 3.4), and the QWP offset angles are found by grid search against a reference QWP with known fast axis (Supplement 3.5). The reported MSE of 0.045 on known optical elements is therefore a genuine independent check rather than a fitted value reported as a prediction. The only circularity-adjacent concern is that a QWP is used both for offset-angle calibration and as one of the validation targets; even if the same optical element were reused, the two fitted offset scalars do not determine the 16 entries of the validation Mueller matrix, and validation on air, linear polarizers at 0° and 45°, synthetic ground truth, and a frame-based comparison provides independent support. The motion-artifact admission in Supplement 5.3 is a scope limitation on the dynamic-scene claim, not a circular reduction: it identifies when the time-invariant-M assumption in Eq. (4) breaks down, but the reconstruction itself does not presuppose the correctness of the claimed Mueller-matrix output.

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

The central claim rests on two fitted camera/optics parameters (C, QWP offsets) and several standard physical assumptions. No new physical entities are introduced. The Eq. (5) factor issue is listed under red flags because it is a manuscript-level consistency problem, not a fitted parameter.

free parameters (4)
  • Contrast threshold C = C=0.14 (on), 0.19 (off) for one example pixel; per-pixel calibrated
    Fit by linear regression on a linearly ramped LED (Supplement 3.4). Used directly in Eq. (7); errors in C directly scale the B matrix and bias the Mueller estimate.
  • QWP offset angles i1, i2 (phi_calib1, phi_calib2) = Not reported numerically; found by grid search
    Calibrated with a reference QWP (Supplement 3.5). They set the phase of A_t; wrong offsets corrupt all Mueller entries.
  • Depolarization factor alpha in calibration = 0.8
    Hand-chosen in Supplement 3.5 Eq. (13) to model non-ideal optics during offset calibration. Affects the calibration step, not the reconstruction formula itself.
  • Rotation speeds and ratio = omega = 30pi rad/s, camera QWP at 5x speed
    Design choice setting the 33 ms frame duration and frequency content of A_t. A chosen system constant, not fitted to target results.
assumptions (5)
  • domain assumption Event camera threshold relation d log I/dt = p C / Delta t holds via first-order Taylor expansion
    Assumed in Eq. (6) and used throughout. Valid for slowly varying photocurrent, but not when Delta t is large relative to intensity dynamics.
  • domain assumption Scene Mueller matrix M is constant within each 33 ms frame and motion-induced events are negligible
    Assumed in Eq. (4), discussed in Supplement 5.3. Violated in fast motion, producing artifacts the authors observe in the hair scene.
  • domain assumption The optical train can be described by ideal LP/QWP Mueller matrices with a single effective wavelength
    Used to derive Eq. (3). White LED and monochrome sensor ignore wavelength dependence, which is acknowledged in Supplement 5.1.
  • domain assumption Motor rotation angles are known synchronously via encoder triggers at constant speed
    Supplement 3.3 maps event times to angles using trigger events. Assumes speed stability and accurate trigger timestamps, supported by measured stability plots.
  • domain assumption Cloude filtering projects onto physically realizable Mueller matrices and does not distort the estimate
    Used in Eqs. (9) and (12). Standard in polarimetry, but can bias estimates when noise is large.

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Cite this review

Pith. "Pith review of Event Ellipsometer: Event-based Mueller-Matrix Video Imaging." pith.science (2026). https://pith.science/paper/U75YPHW2

@misc{pith2026241117313,
  author       = {Pith},
  title        = {Pith review of: Event Ellipsometer: Event-based Mueller-Matrix Video Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U75YPHW2}},
  note         = {Machine review of arXiv:2411.17313}
}
read the original abstract

Light-matter interactions modify both the intensity and polarization state of light. Changes in polarization, represented by a Mueller matrix, encode detailed scene information. Existing optical ellipsometers capture Mueller-matrix images; however, they are often limited to capturing static scenes due to long acquisition times. Here, we introduce Event Ellipsometer, a method for acquiring a Mueller-matrix video for dynamic scenes. Our imaging system employs fast-rotating quarter-wave plates (QWPs) in front of a light source and an event camera that asynchronously captures intensity changes induced by the rotating QWPs. We develop an ellipsometric-event image formation model, a calibration method, and an ellipsometric-event reconstruction method. We experimentally demonstrate that Event Ellipsometer enables Mueller-matrix video imaging at 30fps, extending ellipsometry to dynamic scenes.

Figures

Figures reproduced from arXiv: 2411.17313 by the authors.

Figure 1
Figure 1. Overview of Event Ellipsometer. (a) Our imaging sys [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Imaging system of Event Ellipsometer. (a) Schematic diagram illustrating the optical arrangement and hardware operation. (b) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of our Mueller-matrix reconstruction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (20 more)
Figure 4
Figure 4. Figure 4: Synthetic data evaluation result. (a) The rendered images include two materials: blue silicone and brass. (b) The plot shows the [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Assessment of reconstructed Mueller matrix on real data. (a) Evaluation with known optical elements. We show the corresponding [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Photoelasticity analysis. (a) Experimental setup for measuring a gelatine disk in transmission mode. We gradually apply force for [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Transparent tape detection. (a) The target object is trans [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Mueller matrix acquisition for capturing dynamic human (a) face and (b) hair, demonstrating the capture of diffuse and specular [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Mueller matrix measurement for a HDR scene. (a) The [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 1
Figure 1. Figure 1: Spatial-temporal propagation pattern. The center pixel (depicted in black) is updated using Mueller-matrix candidates from [PITH_FULL_IMAGE:figures/full_fig_p013_1.png]
Figure 2
Figure 2. Figure 2: Timeline of motor rotation and trigger events. [PITH_FULL_IMAGE:figures/full_fig_p014_2.png]
Figure 3
Figure 3. Figure 3: Calibration of contrast threshold. (a) Calibration setup. (b) We linearly increase/decrease the incident current to the LED, and [PITH_FULL_IMAGE:figures/full_fig_p015_3.png]
Figure 4
Figure 4. Figure 4: Calibration of QWP offset angles. (a) Calibration setup. We placed the QWP between light and camera in transmission mode. (b) [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: Measured rotation speed of the two motors. The set point is 15 Hz with a standard deviation of 0.31 for the light-side motor and [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: Synchronization and reconstruction stability. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Evaluation with the frame-based method [ [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Comparison with the single-shot method [ [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Detailed visualization of Mueller matrix of the photoelasticity analysis scene. The ROI is 500 [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Detailed visualization of Mueller matrix of the face scene. The ROI is 600 [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: Detailed visualization of Mueller matrix of the hair scene. The ROI is 600 [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: Detailed visualization of Mueller matrix of the metal plate scene. The ROI is 400 [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]
Figure 13
Figure 13. Figure 13: Detailed visualization of Mueller matrix of the owl statue scene. The ROI is 400 [PITH_FULL_IMAGE:figures/full_fig_p022_13.png]
Figure 14
Figure 14. Figure 14: Detailed visualization of Mueller matrix of the tape scene. The ROI is 256 [PITH_FULL_IMAGE:figures/full_fig_p023_14.png]

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