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REVIEW 4 major objections 6 minor 3 cited by

ADV2E: Bridging the Gap Between Analogue Circuit and Discrete Frames in the Video-to-Events Simulator

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that modelling DVS pixels as brightness-dependent low-pass filters makes synthetic events realistic enough that networks trained on them transfer to real event cameras.

desk verdict A plausible simulator extension with consistent downstream gains, but Eq. (4) has a real DC-gain error that undermines the 'faithful analogue' claim; worth a serious referee, not citable as-is. read the letter →

arxiv 2411.12250 v1 pith:UA43OMRB submitted 2024-11-19 cs.CV cs.RO

Xiao Jiang , Fei Zhou , Jiongzhi Lin This is my paper · ORCID
classification cs.CVcs.RO
keywords eventcamerasDynamicVisionSensorsimulationvideo-to-eventslow-passfiltercontinuitysamplingsim-to-realgeneralizationsemanticsegmentation
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

Event cameras do not record frames; each pixel continuously emits a timestamped event when logarithmic brightness crosses a threshold. Simulators that turn ordinary video into synthetic events usually model only this logical rule, and their events drift from real camera output, especially in high-contrast scenes. This paper argues that the missing ingredient is the analogue pixel circuit, which behaves like a first-order low-pass filter whose cutoff frequency rises and falls with instantaneous brightness. The authors build ADV2E, which over-samples brightness changes and filters them through that time-varying model, and show that networks trained on its events transfer to real event data in semantic segmentation and image reconstruction. If true, existing frame-based video datasets become usable for training event-camera networks without costly real-event collection.

What carries the argument

The load-bearing object is a time-varying first-order low-pass filter used as a model of the DVS pixel's analogue front-end. Its cutoff frequency $\omega_0(t)$ is taken to be proportional to instantaneous brightness $I(t)$, so bright regions filter faster than dark ones; the filter is discretised by impulse invariance, giving the recurrence $Y=e^{-\alpha}Y+\alpha I'_{n,l,k}$. The companion mechanism is continuity sampling: each interpolated frame is linearly over-sampled $K$ times so that the effective sampling rate $KLf_b$ exceeds the Nyquist rate implied by the filter's cutoff, which prevents aliasing and lets the cutoff frequency track brightness within each short interval.

What would settle it

Point a real event camera at a screen that suddenly jumps from dark to bright, and record the timestamps of the resulting events. If the event rate after the jump is not a single exponential whose time constant shrinks as the brightness rises, the first-order brightness-proportional filter model is wrong.

Watch

Extended reading notes

Core claim

The paper's central claim is that the visually obvious difference between synthetic and real events, especially at light-dark edges, is caused less by frame-rate interpolation than by the analogue low-pass filtering inside the DVS pixel. In a real DVS, brightness changes pass through a cascode feedback loop and a source follower whose finite bandwidth delays the signal; the paper models this delay as a first-order low-pass filter with system function $H_a(s)=\omega_0(t)/(s+\omega_0(t))$, where the cutoff frequency $\omega_0(t)$ is proportional to the current brightness. To apply this model to discrete frames without aliasing, ADV2E first linearly over-samples every interpolated frame by a factor $K$, making the effective sampling rate $KLf_b$, and then runs the filter through the recurrence $Y=e^{-\alpha}Y+\alpha I'_{n,l,k}$ with $\alpha=\omega_0^{n,l,k}T_b/(KL)$. The paper reports that this procedure yields synthetic events with the smallest temporal-bin distance to real DVS recordings (49.58) among the compared simulators, and that segmentation and reconstruction networks trained on ADV2E events achieve the best mIoU (51.20) and best MSE (0.02) and SSIM (0.55) on real test data.

Load-bearing premise

The argument rests on treating the DVS pixel's analogue behaviour as a first-order low-pass filter whose cutoff frequency is directly proportional to instantaneous brightness; the paper notes the exact filter order is unknown and defers the derivation to a supplementary file absent from this preprint.

Editorial extensions

If this is right

  • Synthetic events from ordinary APS video become reliable training data for event-camera networks, reducing dependence on scarce and expensive real event datasets.
  • High-contrast scenes, where prior simulators show events abruptly vanishing, should retain realistic event tails caused by the brightness-dependent filter delay.
  • The simulator's only tunable parameter is the continuity sampling factor $K$, so the same pipeline can be applied to any frame-based video without per-scene calibration.
  • If the first-order model is right, networks trained on ADV2E events should keep their advantage as more high-contrast driving and indoor sequences are added.

Reading between the lines

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

  • Beyond the paper, the same analogue-first approach suggests that other DVS non-idealities, such as leakage noise, threshold mismatch, and temperature-dependent bandwidth, could be simulated by the same continuity-sampling discretisation rather than bolted on as post-processing.
  • A testable extension the paper does not run is a controlled step-response experiment: exposing a real DVS to a sudden uniform brightness step and measuring the event-rate decay should show an exponential tail with time constant inversely proportional to brightness; a different functional form would locate where the first-order assumption breaks.
  • An ablation that varies the over-sampling factor $K$ at fixed interpolation $L$ would isolate how much of the reported improvement comes from alias removal versus the brightness-dependent cutoff itself.
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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

4 major / 6 minor

Summary. The paper proposes ADV2E, a video-to-events simulator that incorporates the analogue low-pass filtering behavior of DVS pixel circuits into the event generation pipeline. The method has two main components: continuity sampling, which over-samples interpolated video frames by a factor K to increase the effective sampling rate, and an analogue low-pass filter whose cutoff frequency is assumed proportional to instantaneous brightness, discretized with an impulse-invariance-style update. The authors evaluate ADV2E against four baselines (vid2e, v2e, v2ce, DVS-Voltmeter) on three tasks: direct event comparison using temporal-bin distance on the DAVIS240C 'urban' sequence, semantic segmentation on DDD17 using Ev-Segnet, and image reconstruction on DAVIS240C using E2VID trained on GOPRO-simulated events. ADV2E reports the best temporal-bin distance (49.58), best mIoU (51.20), best MSE (0.02), and best SSIM (0.55), with mixed LPIPS results. The paper claims that these results validate that modeling analogue circuit properties improves the realism of simulated events and sim-to-real generalization.

Significance. If the central modeling claim is correct, ADV2E addresses a genuine gap in video-to-events simulation: existing simulators treat event generation as a purely logical thresholding process and largely ignore the analogue low-pass filtering that is physically present in DVS pixels. The experimental protocol is appropriate and externally grounded: training on simulated events and testing on real event datasets (DDD17, DAVIS240C) is a meaningful sim-to-real test, and the temporal-bin distance on a real DAVIS sequence provides a direct event-level comparison. The paper also honestly notes in §2.1 that the exact filter order of the DVS pixel is unclear, and it names the assumptions underlying the first-order model. However, the current manuscript does not yet support the strong causal claim that the observed improvements come from the specific analogue filtering mechanism: the discretization in Eq. (4) is not the exact first-order ZOH update, the two proposed components are never ablated separately, and no error bars or statistical significance tests are reported. The contribution is promising but needs substantial strengthening before the claims can be accepted.

major comments (4)
  1. [§3.2.2, Eq. (4)] The discretization of the first-order low-pass filter is not the exact zero-order-hold update for H(s)=ω0/(s+ω0). With sample interval Δt=T_b/(KL), the correct recurrence with unity DC gain is Y ← e^{-α}Y + (1−e^{-α})I', where α=ω0Δt, whereas Eq. (4) uses Y = e^{-α}Y + αI'. These agree only when α≪1. At the paper's own settings (§4.1: 24 FPS, K=10, L=10), if the stated 250 Hz cutoff is interpreted as ω0=2π·250 rad/s, then α≈0.65 and the DC gain is α/(1−e^{-α})≈1.37, a 37% amplitude error that grows for brighter pixels because ω0∝I. If ω0 is instead read as 250 rad/s, the paper is ambiguous about Hz versus rad/s and the error is smaller but still systematic. This directly undermines the claim that the filter 'faithfully' replicates the analogue DVS response, and it means the reported gains over baselines may be contaminated by a brightness-dependent amplitude distortion. The derivation for the first-order model is also deferred to a supplementary file that is absent from this preprint, so the central model cannot be fully checked as printed.
  2. [§4.1 and Tables 1–3] The two main contributions—continuity sampling (K) and the brightness-dependent analogue low-pass filter—are never ablated separately. K=10 is set in §4.1, and all other parameters are inherited from v2e/vid2e, but no experiment removes the time-varying cutoff or varies K to show that the improvements in Table 1, Table 2, and Table 3 are due to the analogue filtering mechanism rather than simply to the higher effective sampling rate. Without such an ablation, the causal claim that 'the gap between analogue pixel circuit and discrete video frames' is the source of improvement is not established.
  3. [Tables 1–3 (all experiments)] No error bars, standard deviations, or numbers of random seeds are reported for any of the three evaluations. The margins in the headline results are small—mIoU 51.20 versus 50.60, SSIM 0.55 versus 0.53—and in Table 3 the LPIPS result is not even the best (0.44 versus vid2e's 0.41, and per-sequence LPIPS is worse for shapes6dof, 0.48 versus DVS-Voltmeter's 0.28). These differences may be within run-to-run variability when training Ev-Segnet and E2VID from scratch, so the current evidence does not support the strength of the claims without a significance analysis.
  4. [§3.2.2, §4.1] The proportionality ω0(t) ∝ I(t) is a load-bearing assumption, but the proportionality constant is never specified, calibrated, or studied for sensitivity. The paper states in §4.1 that K is the only parameter requiring adjustment, yet the brightness-to-cutoff mapping introduces at least one additional free parameter whose value is not reported. Since this parameter directly controls the magnitude of α in Eq. (4) and hence the amplitude distortion noted above, the quantitative results are not reproducible without this information.
minor comments (6)
  1. [§1, Abstract] The phrase 'as an infantile sensor' is nonstandard and should be replaced with, for example, 'as a nascent sensor technology'.
  2. [§3.2.1] The Nyquist argument is internally inconsistent: for a 250 Hz cutoff the text says the minimum sampling rate is 500 FPS, which with 24 FPS input requires an interpolation factor of at least 20.9, not 'at least 16x' as stated.
  3. [§4.1, captions and table headers] The names 'DA VIS240C' and 'DVS-V oltmeter' appear with broken spacing throughout, presumably from LaTeX control sequences; these should be rendered as 'DAVIS240C' and 'DVS-Voltmeter'.
  4. [Figure 3] Figure 3 is never cited in the body text; either add a reference at the appropriate point in §3.2.2 or remove the figure.
  5. [§4.4 and Table 3] The text says ADV2E achieves the second-best LPIPS, which matches Table 3, but the qualitative claim that ADV2E produces the most realistic events should be tempered given that vid2e has better average LPIPS and several per-sequence LPIPS comparisons favor other methods.
  6. [References] Reference [17] appears to be mislabeled: it cites a paper by Kim et al. but with the journal and page numbers of the DVS paper [21]; please verify the citation details.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the simulator parameters are set a priori or inherited from v2e, and all evaluations use external real-event benchmarks against independent baselines.

full rationale

The claimed derivation chain—bridging analogue pixel low-pass filtering to more realistic synthetic events—is not circular. The low-pass filter assumption (first-order, cutoff proportional to brightness) is imported from external sources [6, 11, 12, 24] (v2e and DVS pixel biasing literature), none of which share authors with the present paper, and the paper explicitly flags the exact filter order as unclear (§2.1). The only tunable parameter, K, is set a priori to 10, with all other parameters inherited from v2e and vid2e (§4.1), so no parameter is fitted to the evaluation targets (temporal-bin distance, mIoU, MSE/SSIM/LPIPS). Evaluation is conducted on external real-event benchmarks (DAVIS240C, DDD17, GOPRO) against four independent baselines; the improvements are therefore empirically contingent, not forced by construction. Two limitations are noted, but they are not circularity: (i) §3.2 states that the first-order filter derivation is 'demonstrated in the supplementary materials,' yet the supplementary file is absent from this preprint, leaving the core physical assumption unverified; and (ii) Eq. (4) discretizes H(s)=ω0/(s+ω0) as Y=e^{-α}Y+αI', whereas a unity-DC-gain zero-order-hold discretization would use 1-e^{-α} as the input coefficient, so the printed filter has a brightness-dependent DC-gain error (~1.37 at α=0.65). These are correctness and verifiability risks, not reductions of the result to its inputs.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central model rests on a first-order low-pass filter with brightness-proportional cutoff, borrowed from v2e and other prior work; the paper defers its derivation to an absent supplementary. The only parameter introduced and tuned by the authors is the continuity sampling rate K=10, with no sensitivity analysis. The brightness-to-cutoff constant and all event-generation parameters are inherited from v2e and vid2e but not enumerated. No new physical entities are introduced.

free parameters (2)
  • Continuity sampling rate K = 10
    The only parameter the authors say requires adjustment; set to 10 so that KLfb = 2400 Hz exceeds twice the assumed 250 Hz cutoff. No sensitivity analysis or search is reported.
  • Brightness-to-cutoff proportionality constant = unspecified
    The paper states omega0(t) is proportional to I(t) and says other parameters match v2e, but does not give the constant of proportionality or the mapping from log-intensity to cutoff frequency. This prevents re-implementation from the paper alone.
assumptions (5)
  • domain assumption DVS pixel analogue behavior is a first-order low-pass filter
    The core analogue behaviors are modeled as a first-order low-pass filter in Eq. (2). The derivation is deferred to supplementary materials (absent from the arXiv v1), and §2.1 concedes the exact filter order remains unclear.
  • domain assumption Filter cutoff frequency is directly proportional to current brightness (omega0(t) proportional to I(t))
    This relationship is cited to v2e [6] without measurement or validation in this paper. It is load-bearing because the time-varying coefficient alpha in Eq. (4) depends on it.
  • domain assumption Brightness changes linearly within each over-sampled interval Tb/(KL)
    Eq. (1) linearly interpolates between frames. The paper argues K can be increased indefinitely to make the interval brief, but scene motion and exposure transients are not guaranteed linear.
  • standard math Nyquist-Shannon sampling theorem applies to the interpolated frame signal
    The paper invokes Nyquist to justify a minimum 500 FPS sampling rate. The signal being over-sampled is a linearly interpolated frame sequence, which contains no new high-frequency information, so the anti-aliasing argument is questionable.
  • domain assumption Event threshold, noise model, and interpolation settings inherit from v2e and vid2e
    Section 4.1 states all other parameters match v2e and vid2e, but does not enumerate them. The simulator's behavior therefore depends on unstated values from prior systems.

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Cite this review

Pith. "Pith review of ADV2E: Bridging the Gap Between Analogue Circuit and Discrete Frames in the Video-to-Events Simulator." pith.science (2026). https://pith.science/paper/UA43OMRB

@misc{pith2026241112250,
  author       = {Pith},
  title        = {Pith review of: ADV2E: Bridging the Gap Between Analogue Circuit and Discrete Frames in the Video-to-Events Simulator},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UA43OMRB}},
  note         = {Machine review of arXiv:2411.12250}
}
read the original abstract

Event cameras operate fundamentally differently from traditional Active Pixel Sensor (APS) cameras, offering significant advantages. Recent research has developed simulators to convert video frames into events, addressing the shortage of real event datasets. Current simulators primarily focus on the logical behavior of event cameras. However, the fundamental analogue properties of pixel circuits are seldom considered in simulator design. The gap between analogue pixel circuit and discrete video frames causes the degeneration of synthetic events, particularly in high-contrast scenes. In this paper, we propose a novel method of generating reliable event data based on a detailed analysis of the pixel circuitry in event cameras. We incorporate the analogue properties of event camera pixel circuits into the simulator design: (1) analogue filtering of signals from light intensity to events, and (2) a cutoff frequency that is independent of video frame rate. Experimental results on two relevant tasks, including semantic segmentation and image reconstruction, validate the reliability of simulated event data, even in high-contrast scenes. This demonstrates that deep neural networks exhibit strong generalization from simulated to real event data, confirming that the synthetic events generated by the proposed method are both realistic and well-suited for effective training.

Figures

Figures reproduced from arXiv: 2411.12250 by the authors.

Figure 1
Figure 1. The framework of the proposed ADV2E simulator. Different from v2e, the proposed simulator models brightness changes [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Continuity Sampling. The frame rate is significantly in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Qualitative comparison of events generated by different [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Qualitative comparison of semantic segmentation across event simulators. Each column represents a separate scene. From top to [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison of image reconstruction on datasets introduced in [ [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation

    cs.CV 2025-11 conditional novelty 6.0 of 10

    A unified, calibrated noise model for hybrid event-frame sensors is implemented in H-ESIM, a simulator that generates realistic RAW frames and events and improves downstream frame interpolation and deblurring on real ...

  2. Towards a physically realistic computationally efficient DVS pixel model

    eess.IV 2025-05 conditional novelty 6.0 of 10

    A DVS pixel model combining circuit-derived differential equations with first-passage-time stochastic event generation predicts noise event rates accurately with timesteps over 1000 times larger than prior methods.

  3. How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection

    cs.CV 2025-06 reject novelty 4.0 of 10

    Training with more real event data monotonically improves detection on real eTram test scenes, while CARLA DVS synthetic training transfers poorly, but the paper's synthetic-heavy test claim is not directly measured.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.