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SaENeRF: Suppressing Artifacts in Event-based Neural Radiance Fields

T0 review · 2 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Two new losses let event streams alone train clean, photorealistic neural radiance fields.

desk verdict A modest, clearly specified loss-design contribution for event-based NeRF that deserves a real referee, but the objective has a scale-invariance gap the paper never acknowledges and the empirical support is thin. read the letter →

arxiv 2504.16389 v1 pith:V4PODQOY submitted 2025-04-23 cs.CV

Yuanjian Wang , Yufei Deng , Rong Xiao , Jiahao Fan , Chenwei Tang , Deng Xiong , Jiancheng Lv This is my paper · ORCID
classification cs.CV
keywords eventcameraneuralradiancefieldsevent-based3Dreconstructionnovelviewsynthesisself-supervisedlearningartifactsuppressionvolumetricrendering
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 the artifacts in event-based NeRF reconstruction come from two fixable sources: early aggressive learning of brightness changes before geometry is settled, and noise at pixels that trigger no events. SaENeRF replaces the raw event-supervision loss with a positive-only normalization between predicted radiance changes and accumulated event polarities, plus two zero-event regularization terms that suppress artifacts without flattening contrast. If correct, a static scene can be reconstructed from an event camera alone, without RGB frames, at quality close to frame-based methods, with cleaner backgrounds and sharper edges than EventNeRF and E-NeRF on the reported dataset. This matters because event cameras offer low latency, high dynamic range, and low power, and this is a step toward using them on their own for photorealistic 3D reconstruction.

What carries the argument

The load-bearing mechanism is the loss family built on the Event Generation Model, where accumulated event polarities over a time window, scaled by a fixed threshold, act as the supervision signal for rendered brightness change. The first component is positive-only normalization: both the predicted radiance difference and the accumulated polarity are divided by their magnitudes computed only over pixels whose predicted sign matches the event polarity, so zero-event pixels cannot inject noise into the normalization and geometry is learned before large brightness variations. The second component is zero-event regularization: one term penalizes predicted brightness change at zero-event pixels, and a second term divides that penalty by the positive-pixel variation so artifact suppression does not wash out image contrast. Two monitoring quantities, the Temporal Average of Predicted Event Thresholds and the Proportion of Appropriate Pixels, select the consistent pixels used in the normalized loss.

What would settle it

Record a static scene with a real event camera and a synchronized high-frame-rate intensity camera, then compare per-pixel accumulated event polarity times the fixed threshold against the actual log-intensity difference over the same window. If the mismatch is large precisely where SaENeRF still renders artifacts, or if training on synthetic events that satisfy Eq. (3) exactly removes all artifacts while training on real events does not, the paper's linear event model is the simplification that fails.

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Extended reading notes

Core claim

The paper's central claim is that event-only NeRF training can be made artifact-resistant by reshaping the supervision signal. Instead of matching raw rendered brightness changes to raw accumulated event polarity, SaENeRF normalizes both quantities by their magnitude computed only over positive, photometrically consistent pixels, then adds penalties that drive predicted brightness change toward zero on zero-event pixels while dividing that penalty by the variation on positive pixels to preserve contrast. The authors report that this removes the aggressive early brightness learning that distorts geometry and suppresses the background artifacts caused by event noise, yielding higher PSNR and SSIM and lower LPIPS than EventNeRF and E-NeRF on synthetic scenes and visibly cleaner reconstructions on real low-light sequences.

Load-bearing premise

The whole training signal assumes that each pixel's accumulated event polarity times one fixed threshold equals the true log-brightness change, while real event sensors add per-event threshold noise, refractory behavior, and asynchronous triggering.

Editorial extensions

If this is right

  • If the paper is right, event-only NeRF training no longer needs RGB frames to suppress background artifacts, so the low latency and high dynamic range of event cameras can be used directly for photorealistic novel view synthesis.
  • The reported real-time variant converges in about one minute on a single 3090 GPU, suggesting event-based scene reconstruction could become an interactive capture-and-model tool rather than an offline process.
  • Because the losses work at a low zero-event sampling ratio, they reduce artifact counts and sampling cost at the same time.
  • The normalization changes the order of learning: geometry converges before large brightness variations are fitted, which is the mechanism the paper credits for suppressing early-training artifacts.

Reading between the lines

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

  • The linear event model in Eq. (3) is the obvious stress point; if per-event threshold noise matters, a natural extension is to estimate a per-pixel or time-varying threshold (the paper's Temporal Average of Predicted Event Thresholds could seed such an estimate) instead of treating the threshold as a fixed constant.
  • The same normalization and zero-event penalties should transfer to other event-supervised inverse rendering losses, such as event-based 3D Gaussian Splatting or event-based SLAM, since those also supervise from accumulated polarities and should inherit the artifact suppression.
  • A stronger test than the reported low-light static scenes would be fast camera motion or high scene contrast, where longer windows accumulate more polarity and the linear approximation in Eq. (3) is most strained; artifacts in those regimes would show where the method's premise ends.
  • A confidence-weighted version of the positive-only normalization, replacing the hard sign-consistency mask, might recover signal from small noisy polarities that the current selection discards.
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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 / 5 minor

Summary. The paper proposes SaENeRF, a self-supervised method for training a Neural Radiance Field (NeRF) from event streams alone. The core ideas are a normalization loss (Eq. 8) that restricts the L1 normalization to pixels with non-zero accumulated event polarity, and two zero-events regularization losses (Eqs. 9-10) that suppress photometric changes in no-event regions while promoting contrast in event regions. The method is evaluated on the EventNeRF dataset for novel view synthesis, reporting PSNR/SSIM/LPIPS improvements over EventNeRF and an E-NeRF reimplementation, together with a real-time Instant-NGP variant. The code is publicly available.

Significance. The proposed losses are simple, clearly motivated, and easy to implement; the paper also ships code, which is a strength. If the identifiability concern discussed below were resolved, the method would constitute an incremental but potentially useful improvement for event-based NeRF training. However, the central quantitative claim of 'photorealistic' reconstruction is undermined by the scale-invariance of the loss, and the reported gains over the strongest baseline are small and lack statistical backing. As it stands, the evidence does not support the abstract's claim of 'significantly reduces artifacts'.

major comments (2)
  1. [Sec. III-C/D, Eqs. (8)-(12)] The loss function is invariant under a global per-channel multiplicative rescaling of the rendered radiance I_c -> s_c I_c, because every loss term depends only on log-intensity differences ΔL_hat(u) = log I(u,t) - log I(u,t0), and the normalization in Eqs. (8) and (10) divides by L1 norms that also scale. Consequently, the absolute brightness and color of the reconstruction are unconstrained by the event supervision. The reported PSNR/SSIM/LPIPS values against target views therefore do not measure the method's ability to recover photometry; they reflect the MLP's initialization, bias, and activation range. This directly affects the central claim of 'photorealistic' reconstruction in the Abstract and Sec. I, and it limits the interpretation of Tables I and II. Please (i) acknowledge this identifiability issue explicitly, (ii) demonstrate that the metrics are stable under changes to the color MLP's initialization/biasing (e.g., multiple seeds, adding a constant offset to the output color, or including a scale parameter), or (iii) add a constraint that fixes the absolute scale (e.g., a gray-world prior or a penalty on the mean rendered color). Without such evidence, the quantitative comparison is not well grounded.
  2. [Sec. IV-A, Tables I and II] The claim that SaENeRF 'significantly reduces artifacts and achieves superior reconstruction quality' is not supported by the reported statistics. The comparison is based on a single run per method with no error bars or multiple seeds. The average PSNR gain over E-NeRF† is only 0.24 dB (30.39 vs 30.15), and SaENeRF is worse than E-NeRF† on three of the seven synthetic scenes (Lego, Ficus, Materials). The ablation study in Table III is performed on a single scene (Drums, misspelled as 'Drams' in Sec. IV-C), and the difference between norm+&zero+ and norm-&zero+ is 0.02 dB PSNR, which is well within typical run-to-run variation for NeRF training. I recommend reporting multiple seeds with standard deviations, or at least softening the language from 'significantly' to 'marginally' or 'on the tested sequence'.
minor comments (5)
  1. [Abstract and Sec. I] There are typos: 'changes in logarithmic brightness changes' should be 'changes in logarithmic brightness'; 'threshould' in Sec. I should be 'threshold'.
  2. [Sec. III-B, Eq. (2)] The equation 'pC = log I(u,t) - log I(u,tprev)' is confusing because p is defined as the polarity in {+1,-1}; it would be clearer to write 'ΔL = pC' or 'each event contributes pC to the log-intensity difference'.
  3. [Sec. III-E] λ0 is set to 0 for SaENeRF, so the main model does not use Lzero-; the paper should justify this choice and state whether the results in Table I rely on Lzero+ alone. A sensitivity analysis for λ and λ0 would strengthen the claims.
  4. [Sec. IV-A] The comparison with EventNeRF NGP uses different negative sampling ratios (0.1 for EventNeRF NGP vs. 0.05 for the other methods), which the text acknowledges but still constitutes a confound. Please at least run EventNeRF NGP with the same ratio for a fairer comparison.
  5. [Sec. III-C/D, Eqs. (8) and (10)] The paper does not discuss the case where the positive-sample set is empty (i.e., no non-zero accumulated polarities in an event window), which would make the denominators in Eqs. (8) and (10) zero. Please clarify how this is handled in practice.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SaENeRF's losses are training-time heuristics evaluated on held-out novel views, not quantities fitted to the evaluation targets.

full rationale

The paper's central claim is that its normalization (Eq. 8) and zero-events regularization losses (Eqs. 9-10) suppress artifacts in event-based NeRF reconstruction. These losses are training objectives for a NeRF and are not derived from, or fitted to, the held-out target views used in the quantitative evaluations (Tables I and II). Equation (3) is a standard event generation model cited to external sources [1], [15], and the event-window formulation follows [11]; none of these citations are self-citations by the present authors. The L1 normalization removes the event threshold C from the supervision, so the method does not constrain absolute brightness from the event data; this is an identifiability or optimization concern, not a circularity, because the training objective is not equivalent to the evaluation metrics and no fitted parameter is renamed as a prediction. The ablations on the same dataset and hand-set hyperparameters are standard empirical tuning rather than circular derivation. The paper itself acknowledges limitations (artifacts are not completely eliminated and known poses are required), which further indicates the claims are not forced by construction. No circular step can be quoted from the paper's equations or cited results.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the Event Generation Model approximation and on hand-set hyperparameters; no new physical entities or conserved quantities are introduced.

free parameters (3)
  • lambda (weight of Lzero+) = 0.5 for SaENeRF, 1.0 for SaENeRF NGP
    Hand-set in Section III-E; controls the trade-off between artifact suppression and contrast, with no sensitivity analysis beyond a single ablation.
  • lambda0 (weight of Lzero-) = 0 for SaENeRF, 0.5 for SaENeRF NGP
    Hand-set in Section III-E; disabled in the main SaENeRF model, so the contribution of Lzero- is not demonstrated in the full configuration.
  • negative sampling ratio = 0.05 for SaENeRF NGP and reimplemented baselines; 0.1 for EventNeRF NGP
    Set in Section IV-A; affects artifact behavior, and the original baseline uses a different value, which is disclosed but not discussed as a confound.
assumptions (4)
  • domain assumption Event Generation Model: accumulated event polarities times threshold equal log-light differences (Eq. 3)
    Section III-B relies on this linear model as the supervision signal; real sensors have threshold noise and asynchronous triggering, making it approximate.
  • domain assumption Known camera intrinsics and poses
    Section III states K and P are known; the conclusion admits pose-unknown settings are outside the scope.
  • domain assumption Static scene assumption
    Section III restricts reconstruction to static scenes; moving objects are not handled.
  • domain assumption Event window aggregation
    Section III-B aggregates events over time windows, discarding fine temporal structure that may matter for fast motion.

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Pith. "Pith review of SaENeRF: Suppressing Artifacts in Event-based Neural Radiance Fields." pith.science (2026). https://pith.science/paper/V4PODQOY

@misc{pith2026250416389,
  author       = {Pith},
  title        = {Pith review of: SaENeRF: Suppressing Artifacts in Event-based Neural Radiance Fields},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V4PODQOY}},
  note         = {Machine review of arXiv:2504.16389}
}
read the original abstract

Event cameras are neuromorphic vision sensors that asynchronously capture changes in logarithmic brightness changes, offering significant advantages such as low latency, low power consumption, low bandwidth, and high dynamic range. While these characteristics make them ideal for high-speed scenarios, reconstructing geometrically consistent and photometrically accurate 3D representations from event data remains fundamentally challenging. Current event-based Neural Radiance Fields (NeRF) methods partially address these challenges but suffer from persistent artifacts caused by aggressive network learning in early stages and the inherent noise of event cameras. To overcome these limitations, we present SaENeRF, a novel self-supervised framework that effectively suppresses artifacts and enables 3D-consistent, dense, and photorealistic NeRF reconstruction of static scenes solely from event streams. Our approach normalizes predicted radiance variations based on accumulated event polarities, facilitating progressive and rapid learning for scene representation construction. Additionally, we introduce regularization losses specifically designed to suppress artifacts in regions where photometric changes fall below the event threshold and simultaneously enhance the light intensity difference of non-zero events, thereby improving the visual fidelity of the reconstructed scene. Extensive qualitative and quantitative experiments demonstrate that our method significantly reduces artifacts and achieves superior reconstruction quality compared to existing methods. The code is available at https://github.com/Mr-firework/SaENeRF.

Figures

Figures reproduced from arXiv: 2504.16389 by the authors.

Figure 1
Figure 1. The overview presents our progressive learning method. By normal [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. EventNeRF adopts an aggressive joint optimization strategy, simultaneously learning in geometric structure via PoAP and light difference via TAoPET, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison of our method with EventNeRF, E2VID+NeRF, E-NeRF [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Comparison of our method with EventNeRF and E-NeRF†, presented [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 6
Figure 6. Figure 6: Ablations of different normalizations in synthetic sequence. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Ablations of different normalizations and zero-events losses in real [PITH_FULL_IMAGE:figures/full_fig_p007_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. A Survey of 3D Reconstruction with Event Cameras

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A dedicated survey categorizes event-based 3D reconstruction methods by input setup and reconstruction strategy, and catalogs datasets, metrics, and open challenges.

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