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REVIEW 5 major objections 4 minor 1 cited by

GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation

T0 review · 5 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that reconstructing static scenes with 3D Gaussian Splatting and then simulating events through a stochastic DVS voltage model produces large-scale, geometry-consistent event streams that transfer to real event-based 3D…

desk verdict The dataset idea is new and worth building on, but an unresolved contrast-threshold contradiction and missing quantitative sim-to-real evidence currently undercut the paper's central claims. read the letter →

arxiv 2505.15287 v1 pith:NMB4SAQ4 submitted 2025-05-21 cs.CV

Yuchen Li , Chaoran Feng , Zhenyu Tang , Kaiyuan Deng , Wangbo Yu , Yonghong Tian , Li Yuan This is my paper
classification cs.CV
keywords eventcamera3DGaussianSplattingsyntheticdatasetevent-basedreconstructioncontrastthresholdsim-to-realtransfernovelviewsynthesisDVS-Voltmeter
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

GS2E claims that a static 3D Gaussian Splatting reconstruction of a real scene is enough of a foundation to generate large-scale, geometry-consistent event-camera data. The pipeline reconstructs each scene from sparse multi-view RGB images, renders dense image sequences along velocity-controlled virtual camera trajectories, and feeds them to the DVS-Voltmeter stochastic event simulator. A contrast threshold is chosen to match real sensor behavior while suppressing rendering artifacts. The paper argues that the resulting dataset of over 1,150 scenes provides the viewpoint diversity and temporal density that video-driven synthetic datasets lack, and that models trained on it transfer to real event data for 3D reconstruction. If this holds, high-quality event-vision training data can be produced from ordinary multi-view photo collections instead of specialized hardware or dense video.

What carries the argument

The load-bearing mechanism is the pairing of 3D Gaussian Splatting with a stochastic DVS voltage model. 3DGS represents the scene as a set of anisotropic 3D Gaussians with optimized opacity and radiance, rendered by differentiable rasterization, which turns sparse RGB views into a photorealistic radiance field that can be re-rendered from arbitrary poses. DVS-Voltmeter then treats each pixel's voltage as $\Delta V_d(t) = \mu \Delta t + \sigma W(\Delta t)$ and fires an event when the stochastic process crosses ON or OFF thresholds. The two are joined by a contrast threshold $c$: the paper sweeps $c \in [0.25, 1.5]$ and settles on $c = 1$, a value that preserves event detail while hiding 3DGS floater artifacts. Velocity-controlled cubic B-spline trajectory interpolation provides the motion that turns rendered frames into temporally dense event streams.

What would settle it

Record a static real scene with a real DVS event camera while the camera follows a known trajectory, reconstruct the same scene with 3DGS from synchronized RGB frames, and run the paper's pipeline to simulate events along that trajectory. Compare the simulated stream with the real one, through event-rate statistics, contrast-threshold histograms, or by training a downstream 3D reconstruction model on GS2E and testing it on this real capture. If the modeled events are no better at predicting the real events or the resulting reconstruction than events from a standard video-driven simulator, the central sim-to-real claim fails.

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Extended reading notes

Core claim

The central claim is that events rendered from 3DGS-reconstructed scenes, rather than from dense RGB video or graphics-engine assets, combine photorealistic appearance with strict geometric consistency. Starting from sparse multi-view images with known poses, GS2E trains a 3D Gaussian Splatting radiance field, then uses cubic B-spline interpolation with a velocity profile to create dense camera trajectories, including novel-view mini-trajectories. The rendered frames drive DVS-Voltmeter, which models each pixel's photovoltage as Brownian motion with drift and emits ON/OFF events when contrast thresholds are crossed, with inverse-Gaussian timestamp sampling. The paper reports that this setup yields event streams that align with scene structure, support non-uniform motion, and, when used to train event-based 3D reconstruction, deblurring, and video reconstruction models, generalize to real event data better than streams synthesized from dense RGB video.

Load-bearing premise

The benchmark value rests on the assumption that events generated from 3DGS-rendered images by DVS-Voltmeter with a manually chosen contrast threshold $c = 1$ are realistic enough to transfer to real DVS sensors; the paper's appendices concede that 3DGS does not model overexposure, underexposure, or high dynamic range, and that $c = 1$ was chosen in part to hide floater artifacts rather than calibrated against real sensor data.

Editorial extensions

If this is right

  • Training event-based 3D reconstruction models on GS2E should yield better generalization to real event streams than training on events synthesized from dense RGB video, as the paper's DSEC comparisons and reconstruction experiments indicate.
  • Because each 3DGS scene can be re-rendered along any smooth trajectory, GS2E can generate multiple multi-view event streams per scene without new hardware capture, supporting controlled evaluations under different motion speeds.
  • The dataset's pairing of blurry RGB frames, sharp poses, and temporally dense events makes it usable for event-based deblurring and video reconstruction, not only 3D reconstruction.
  • The velocity-controlled interpolation lets researchers test models under mild, medium, and strong camera motion while holding the scene geometry fixed, isolating motion effects from scene effects.

Reading between the lines

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

  • If 3DGS truly decouples scene geometry from sensor simulation, the same reconstructed scenes could be reused to generate other sensor modalities, such as optical flow, depth, or alternative event-camera noise models, making 3DGS a general synthetic-data generator.
  • The paper chooses $c = 1$ partly to hide Gaussian floater artifacts rather than from direct sensor calibration; a natural extension is per-scene or per-sensor contrast-threshold distributions measured from real DVS recordings, which could close more of the sim-to-real gap.
  • The static-scene assumption is the clearest boundary of the method; extending the pipeline to dynamic scenes would require a deformable or 4D Gaussian representation, a step the paper leaves for future work.
  • If quantitative event-realism metrics such as EQS become publicly available, the paper's qualitative DSEC comparison could be replaced by a direct numerical check of simulated event realism.
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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

5 major / 4 minor

Summary. The paper proposes GS2E, a synthetic event-stream data generator. It reconstructs static scenes from sparse multi-view RGB images using 3D Gaussian Splatting, generates dense camera trajectories via velocity-aware B-spline interpolation, renders image sequences along those trajectories, and feeds them into the DVS-Voltmeter stochastic event simulator. The stated contributions are a large-scale dataset (the paper variously says 1050, 1150, or 1900 scenes), a physically-informed contrast-threshold model, and superior generalization of models trained on GS2E for event-based 3D reconstruction, deblurring, and video reconstruction. The experiments report reconstruction metrics, a qualitative comparison against DSEC real-world event data, downstream task evaluations, and an ablation of trajectory interpolation. The appendix discloses limitations including the inability to model overexposure, underexposure, or high dynamic range.

Significance. If the synthetic-to-real transfer claim were quantitatively established, GS2E would address a real bottleneck in event-vision research: the scarcity of large, multi-view-consistent event datasets with aligned RGB frames. The pipeline design is plausible and the choice to build on 3DGS plus a stochastic event simulator is well motivated. The paper also makes a concrete dataset release, benchmarks several downstream tasks, and provides a useful comparison table of existing event-based 3D datasets. However, the current evidence does not support the central claim of superior generalization: the contrast threshold, which is the key physical parameter, is specified inconsistently and is chosen to hide rendering artifacts rather than calibrated to real sensors; the synthetic-to-real evaluation is only qualitative; and the experiments lack comparisons against prior synthetic event data generators or error bars. Significance is therefore conditional on substantial additional validation.

major comments (5)
  1. [§3.6, Appendix B, §4 (Implementation details)] The contrast threshold, the key physical parameter of event simulation, is specified inconsistently. Section 3.6 states that 'we adopt c ∈ [0.2, 0.5]' after an empirical sweep, Appendix B states that 'the GS2E dataset was simulated with the parameter setting c = 1' because lower thresholds expose 3DGS floater artifacts, and Section 4 states 'ΘON = ΘOFF = 1 as default.' These settings are mutually incompatible, and neither is calibrated against real DVS sensor data; c = 1 is explicitly chosen to suppress rendering artifacts. Because the paper's claimed contribution of 'physically-consistent contrast threshold modeling' depends on this parameter, please reconcile the statements, report which threshold was actually used for the released scenes, and provide a quantitative sensitivity analysis or a calibration against real sensor statistics (for example, the contrast-threshold distributions in Stoffregen et al. or DSEC event rates).
  2. [§4.2] The claim of 'superior generalization capabilities' is not supported by quantitative evidence. Section 4.2 states that the Event Quality Score is not publicly available and that the synthetic-to-real evaluation is limited to 'qualitative evaluations' and 'visual comparisons' against DSEC. No quantitative domain-gap metric is reported anywhere in the paper. Please add measurable evidence of sim-to-real transfer, for example: event-rate and contrast-histogram statistics on real versus synthetic streams, background-activity and noise comparisons, or a transfer experiment in which models trained on GS2E are evaluated on real event data (DSEC or MVSEC) and compared with models trained on prior synthetic data.
  3. [Tables 1-2] The experiments do not compare GS2E against existing synthetic event data generators. Tables 1 and 2 report downstream task metrics for methods trained or evaluated on GS2E, but there is no head-to-head comparison with events generated by ESIM, v2e, Vid2E, or DVS-Voltmeter on ordinary rendered videos. Without such baselines, the abstract's claim that GS2E overcomes the limitations of prior event datasets is untestable. In addition, all table entries are single point estimates without error bars; please report means and standard deviations across scenes or seeds, and add a data-source ablation.
  4. [Abstract, §3.3, §4, Appendix C, Table 3] The reported dataset scale is internally inconsistent: the Abstract and Table 3 say 'over 1150 scenes'; Section 3.3 selects 1,000 MVImgNet scenes and 50 DL3DV scenes (1050 total); Section 4 and Appendix C say 'we choose and render 1.8k scenes from MVImageNet and 100 scenes from DL3DV' (1900 total). Since dataset scale is one of the stated contributions, please correct the numbers and make the scene-selection protocol precisely match the released dataset.
  5. [Abstract vs Appendix E] The Abstract claims 'diverse motion and lighting conditions,' but Appendix E explicitly concedes that the pipeline 'inherits the photometric constraints of 3D Gaussian Splatting' and 'may not faithfully replicate extreme illumination conditions such as overexposure or underexposure,' including low-light and high-dynamic-range scenes. Because event cameras are most advantageous exactly in these regimes, this limitation is directly relevant to the benchmark's fidelity claim. Please either add experiments or rendering modifications that cover HDR or overexposure cases, or explicitly scope the dataset claims to moderate illumination.
minor comments (4)
  1. [§4, Appendix C, §3.1, §3.3] There are several typos and inconsistent terms that should be fixed: 'interplementation verision' in Section 4 and Appendix C; 'ourdoor' in Section 3.3; 'off-the-shell' in Section 3.1; and inconsistent spelling of 'DVS-V oltmeter' versus 'DVS-Voltmeter.'
  2. [§4.4, Figure 6] The ablation of trajectory interpolation methods is presented only qualitatively; please add quantitative metrics (for example, trajectory smoothness or downstream reconstruction PSNR) or explicitly state that the comparison is qualitative.
  3. [References and Table 3] References [52] and [53] appear to be the same paper (DA4Event); the duplicate should be removed. Also, Table 3 is difficult to read in the provided version because of merged columns, missing spaces, and layout artifacts such as 'GS2E SubmissionSynthetic'; please reformat it.
  4. [Appendix A] In the speed-list interface, the notation ΔT is used but T is never defined; please define all variables used in the speed-curve construction.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the dataset-generation chain uses external components (3DGS, DVS-Voltmeter) and is benchmarked by independent downstream methods; contrast-threshold settings are free parameters, not fitted predictions.

full rationale

GS2E's data-generation derivation is self-contained in the relevant sense: real multi-view images are reconstructed by 3D Gaussian Splatting [32], rendered along interpolated trajectories, and passed to the DVS-Voltmeter simulator [39] to produce events. None of the quantities claimed as outputs (event streams, dataset realism, benchmark utility) is defined in terms of the paper's conclusions, and no fitted parameter is renamed as a prediction. The load-bearing components are third-party external works, while the authors' own prior papers (e.g., EvaGaussian [87], AE-NeRF [10]) appear only in contextual citations and dataset tables, not as justification for the core derivation. The evaluation uses independent downstream methods (E-NeRF, Event-3DGS, D2Net, EFNet, E2VID, TimeLens++) with standard metrics against rendered ground truth, so the benchmark claim does not reduce to the pipeline's own assumptions. The most significant weaknesses are non-circular: Sec. 3.6 says 'we adopt c ∈ [0.2, 0.5]' but Appendix B states 'the GS2E dataset was simulated with the parameter setting c = 1' and Sec. 4 sets 'ΘON = ΘOFF = 1 as default', an internal inconsistency; and the sim-to-real evaluation is only qualitative because 'the EQS implementation is not publicly available.' Appendix E also concedes that 3DGS 'may not faithfully replicate extreme illumination conditions such as overexposure or underexposure.' These are calibration/domain-gap limitations that weaken the generalization claim, but they are not circularity: the contrast threshold is a manually chosen input parameter, not a conclusion derived from itself. Accordingly the circularity score is 1, reflecting only non-load-bearing self-citations.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. The pipeline relies on hand-set trajectory and threshold parameters; the contrast threshold is the most consequential, and the paper gives contradictory settings for it. No quantitative calibration of these parameters against real sensor data is provided.

free parameters (8)
  • Contrast threshold c = 1 in Appendix B/C; 0.2-0.5 in Section 3.6
    Controls event density and noise. The paper gives contradictory settings and chooses c=1 to suppress 3DGS floater artifacts rather than calibrating to real sensor behavior.
  • Velocity profile v(t) = 0.25 sin(t) + 1.1
    Hand-chosen analytic speed profile used to reparameterize camera trajectory; affects temporal distribution of rendered frames and events.
  • Interpolation multiplier gamma = 5
    Determines temporal density of the rendered trajectory via M = ceil(gamma * N).
  • Pose smoothing window w = 2
    Half-width for local temporal averaging of camera poses in Eq. 7 and Eq. 8.
  • Displacement weights alpha, beta = not specified
    Weights in Eq. 9 balancing rotation and translation in the path-length metric; values are not reported.
  • Novel-view augmentation G, K, F = G=3, K=5, F=150
    Control the number and length of mini-trajectories used to augment camera motion diversity.
  • 3DGS input view count N = 30 for MVImgNet, 100 for DL3DV
    Number of sparse multi-view images used to reconstruct each scene; affects reconstruction fidelity and downstream event realism.
  • DVS-Voltmeter sensor parameters k1-k6 = k1=0.5, k2=1e-3, k3=0.1, k4=0.01, k5=0.1, k6=1e-5
    Inherited from DVS-Voltmeter [39] and treated as fixed; they determine drift and variance of the stochastic voltage process and hence event timing and polarity.
assumptions (5)
  • domain assumption DVS-Voltmeter's Brownian motion with drift model faithfully represents real DVS event generation.
    Eq. 6 models photovoltage as Delta V = mu Delta t + sigma W(Delta t); the realism of all synthesized events depends on this physical model being adequate.
  • domain assumption 3DGS reconstruction from sparse multi-view images is photorealistic and geometrically accurate enough for event synthesis.
    Section 3.4 uses 3DGS with N=30 or 100 views; Appendix E concedes 3DGS does not model extreme illumination or high dynamic range.
  • ad hoc to paper A fixed contrast threshold of c=1 (or the range 0.2-0.5 in Section 3.6) produces event streams matching the target real-sensor distribution.
    Appendix B states c=1 was chosen to hide 3DGS floater artifacts, while Section 3.6 claims the range 0.2-0.5 was chosen by matching MVImgNet/DL3DV characteristics; no quantitative sensor calibration is provided.
  • domain assumption Scenes are static with no dynamic object motion.
    Appendix E explicitly states dynamic object motion is not modeled, which limits applicability to dynamic event vision.
  • domain assumption Camera poses from MVImgNet and DL3DV are accurate enough for 3DGS training and trajectory interpolation.
    Section 3.4 treats poses from structure-from-motion or dataset metadata as ground truth; pose errors would propagate into rendered sequences and events.

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Cite this review

Pith. "Pith review of GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation." pith.science (2026). https://pith.science/paper/NMB4SAQ4

@misc{pith2026250515287,
  author       = {Pith},
  title        = {Pith review of: GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NMB4SAQ4}},
  note         = {Machine review of arXiv:2505.15287}
}
read the original abstract

We introduce GS2E (Gaussian Splatting to Event), a large-scale synthetic event dataset for high-fidelity event vision tasks, captured from real-world sparse multi-view RGB images. Existing event datasets are often synthesized from dense RGB videos, which typically lack viewpoint diversity and geometric consistency, or depend on expensive, difficult-to-scale hardware setups. GS2E overcomes these limitations by first reconstructing photorealistic static scenes using 3D Gaussian Splatting, and subsequently employing a novel, physically-informed event simulation pipeline. This pipeline generally integrates adaptive trajectory interpolation with physically-consistent event contrast threshold modeling. Such an approach yields temporally dense and geometrically consistent event streams under diverse motion and lighting conditions, while ensuring strong alignment with underlying scene structures. Experimental results on event-based 3D reconstruction demonstrate GS2E's superior generalization capabilities and its practical value as a benchmark for advancing event vision research.

Figures

Figures reproduced from arXiv: 2505.15287 by the authors.

Figure 1
Figure 1. We propose GS2E, a high-fidelity synthetic dataset designed for 3D event-based vision, comprising over 1150 scenes. GS2E examples of RGB frames and event streams are shown above. Abstract We introduce (GS2E) (GAUSSIAN SPLATTING TO EVENT GENERATION), a large-scale synthetic event dataset for high-fidelity event vision tasks, captured from real-world sparse multi-view RGB images. Existing event datasets are often synt… view at source ↗
Figure 2
Figure 2. Overview and comparison of event-based 3D dataset construction methods. We compare (1) real-world capture, (2) video-driven synthesis, and (3) simulation via computer graphics engines in terms of commonly used methods, strengths, and drawbacks. preserves the geometric fidelity of the original scene. The controllable virtual setup enables diverse motion patterns and blur levels, supporting the training of robust even… view at source ↗
Figure 3
Figure 3. Overview of the proposed GS2E pipeline. Starting from sparse multi-view RGB images and known camera poses, we reconstruct high-fidelity scene representations using 3D Gaussian Splatting. Virtual camera trajectories are then synthesized via velocity-aware reparameterization and interpolation. The rendered image sequences are passed to a volumetric event simulator to generate temporally coherent and geometrically cons… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of synthesized event distributions using GS2E versus traditional [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Application to Multiple Tasks. We benchmark it across event-vision tasks: 3D reconstruc [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Comparison of different interpolation methods shows that our method is smoother and has [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Selecting the same viewpoint and time window(1000 us), visualize events simulated from [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

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Forward citations

Cited by 1 Pith paper

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

  1. E-4DGS: High-Fidelity Dynamic Reconstruction from the Multi-view Event Cameras

    cs.CV 2025-08 conditional novelty 6.0 of 10

    E-4DGS is a deformable 3D Gaussian Splatting method that reconstructs dynamic scenes directly from multi-view event camera streams, outperforming event-to-image baseline approaches.

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