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REVIEW 5 major objections 4 minor 1 cited by

PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining

T0 review · 5 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read PRE-Mamba claims that event-camera deraining can be done directly on raw event points, reporting 0.95 signal retention, 0.91 noise removal, and 0.4 seconds per million events on the new EventRain-27K benchmark.

desk verdict A promising first point-based event deraining framework with a new benchmark, but the headline numbers rest on unvalidated synthetic labels and the SOTA comparison is not fully controlled. read the letter →

arxiv 2505.05307 v2 pith:Q52KKLG4 submitted 2025-05-08 cs.CV

Ciyu Ruan , Ruishan Guo , Zihang Gong , Jingao Xu , Wenhan Yang , Xinlei Chen This is my paper · ORCID
classification cs.CV
keywords eventcameraderainingpoint-basedprocessingstatespacemodel4Dcloudspatio-temporaldecouplingandfusionfrequency-domainregularizationrainnoiseremovaldataset
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

The paper is trying to establish that rain noise in event cameras can be removed without sacrificing the sensor's native temporal precision. Its claim is that a point-based network operating on raw events outdoes frame- or voxel-based deraining and denoising methods on a new benchmark, with only 0.26M parameters and linear-time processing. A sympathetic reading: if true, event cameras become usable in rain without giving up the microsecond-resolution, asynchronous advantages that make them attractive for high-speed robotics and autonomous driving. The method's three load-bearing ideas are a 4D event-cloud representation that keeps two temporal scales, a spatiotemporal decoupling and fusion module, and a multi-scale state-space backbone, regularized in the frequency domain.

What carries the argument

The central object is the 4D Event Cloud, $(x,y,z,T_n,p)$, which serializes raw events along z-order and Hilbert curves so a selective state-space model can ingest them while preserving both intra-window (microsecond) and inter-window (global) temporal structure. Around it, the Spatio-Temporal Decoupling and Fusion module (STDF) extracts spatial features and modulates them by intra- and inter-window temporal features; the Multi-Scale State Space Model (MS3M) runs parallel appearance and motion branches, adds a multi-scale spatial pathway, and applies a cross-product fusion before the SSM scan. The frequency-domain regularizer $L_{\text{fft}}$ aligns the FFT amplitude and phase of predicted and ground-truth event labels, enforcing physically consistent rain patterns. Together these carry the claim by showing that each component contributes to the final DA score in ablations.

What would settle it

Take a held-out set of real rainy events recorded with a synchronized high-speed camera, manually label every event as rain or background by checking the optical ground truth, and run PRE-Mamba on it. If its SR/NR/DA falls to near chance or well below the reported 0.95/0.91 while the same labels look correct to human inspectors, the central claim is falsified.

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Extended reading notes

Core claim

PRE-Mamba treats event deraining as per-event binary classification: each event is labeled rain or background, rather than reconstructing a clean frame. The central discovery is that this classification works better when each event is represented as a point in a 4D cloud $(x,y,z,T_n,p)$, where $z$ is time normalized inside a fixed window and $T_n$ is the window index, and processed by a state-space model with separate intra-window and inter-window branches. The paper reports average SR/NR/DA of 0.95/0.91/0.93 on its EventRain-27K test sets, processes 0.4 seconds per million events with 0.26M parameters, and shows qualitative generalization across rain intensities, viewpoints, and snow.

Load-bearing premise

The whole comparison depends on the ground-truth labels that say which events are rain and which are background; if those labels are wrong or systematically biased, the reported accuracy numbers do not mean what they appear to mean.

Editorial extensions

If this is right

  • Rain removal can be done event-by-event on raw streams, so downstream high-speed vision consumes cleaned events without frame conversion.
  • At 0.26M parameters and 0.4 seconds per million events, the approach fits resource-constrained and near-real-time settings.
  • Because performance degrades gracefully from 5 to 150 mm/h rain and transfers to snow, a single model may cover varied weather rather than requiring per-intensity retraining.
  • The linear-complexity backbone avoids the quadratic attention bottleneck that blocks point transformers at high event rates.
  • EventRain-27K supplies labeled synthetic, artificial, and real sequences, giving subsequent point-based deraining methods a common training and evaluation ground.

Reading between the lines

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

  • Beyond the paper: if the per-event labels are reliable, the same 4D-cloud-plus-state-space recipe should transfer to other sparse weather noise such as hail, spray, or dust, where the spatiotemporal signature differs from ordinary sensor noise.
  • Beyond the paper: the dual-temporal representation may be useful outside deraining, for example as a general input format for event-based segmentation or tracking where intra-window and inter-window motion both matter.
  • Beyond the paper: a strong practical test would be measuring downstream task performance (tracking, SLAM, or object detection) on derained real rainy events, since SR/NR/DA on labeled synthetic data may not capture true utility.
  • Beyond the paper: the frequency-domain loss on event labels could be adapted to any sparse point-classification problem with imbalanced positive/negative ratios, not just rain.
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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 PRE-Mamba, a point-based event-camera deraining framework that operates directly on raw event streams via a 4D event cloud representation, a Spatio-Temporal Decoupling and Fusion module (STDF), a Multi-Scale State Space Model (MS3M), and a frequency-domain regularization loss. The authors construct EventRain-27K, a dataset with synthetic, self-recorded artificial, and real-world rain event sequences, and report state-of-the-art per-event classification accuracy (average SR/NR/DA of 0.95/0.91/0.93) with low parameter count and high inference speed. The paper also claims generalization to snow. The central claim is empirical: the proposed architecture outperforms adapted event-denoising baselines on the newly introduced dataset.

Significance. If the results hold, the paper is a useful contribution: it is the first point-based event deraining framework, introduces a public dataset and code, and demonstrates that a selective state-space model can process raw event streams at scale with linear complexity. The architecture is described in sufficient detail to be reimplemented, and the ablations isolate the contribution of each proposed component. The efficiency numbers are attractive for embedded and real-time applications. The main significance is contingent on the reliability of the EventRain-27K ground-truth labels, because the headline SR/NR/DA metrics and the SOTA comparison are computed directly against those labels; the current manuscript does not provide enough evidence that the labels are trustworthy.

major comments (5)
  1. [Section 4, Synthetic Dataset] The labeling protocol for the synthetic dataset is unspecified. The text states only that clean KITTI/SPAC videos were made rainy and then passed through Vid2E, but it never states how per-event rain/background labels are derived. If the labels are obtained by differencing the event streams of rainy and clean videos, the procedure is invalid because rain alters the effective scene contrast, shifting or suppressing background events; the resulting labels would systematically misclassify occlusion and contrast-change events as rain, directly inflating SR and NR. The authors must specify the exact label-generation rule, and if differencing is used, they must either justify it with a physical model or replace it with a valid procedure.
  2. [Section 4, Self-recorded Artificial Dataset] The KNN-based spatiotemporal alignment used to label background events in the self-recorded artificial data is not validated. Since the authors deliberately applied slight camera vibration during capture, many background events in the rainy take will have no close spatiotemporal neighbor in the rain-free take and will be labeled as rain; foreground droplets also occlude background, adding further label noise. Because Table 1 reports per-event accuracy against these labels, systematic mislabeling would distort the absolute numbers and, more importantly, the comparison with baselines. Please provide a quantitative label-quality assessment (e.g., human-annotated subset, consistency analysis, or an alternative alignment method) and discuss how label noise affects the reported SR/NR/DA.
  3. [Abstract and Section 4] The dataset size is internally inconsistent. The abstract and introduction state that EventRain-27K comprises 18K labeled synthetic and 9K unlabeled real-world sequences, totaling 27K. Section 4 instead says the dataset contains over 7K self-generated synthetic samples, over 7K self-recorded artificial samples, and over 9K real rain samples, totaling approximately 23K. This discrepancy must be resolved, as the reader cannot tell how many labeled sequences were actually used for training and evaluation.
  4. [Section 5.2, Table 2 and Abstract] The efficiency claim is numerically inconsistent. The abstract reports 0.4s/M events, and the text says the model scales to 1M events in 0.398s, but Table 2 reports 0.0987s per 100K events, which is 0.987s per 1M events. Moreover, the 'Relative speed' column is confusing: EDnCNN is assigned 1.0x while TS, which is 155x faster than EDnCNN by inference time, is also assigned 1.0x. Please correct these numbers and define the reference method clearly.
  5. [Section 5.2, Baselines] The comparison protocol is under-specified. The paper says only that EDnCNN was retrained with the authors' ground-truth labels; it does not state whether AEDNet and EDformer were likewise retrained on EventRain-27K or used with their pretrained weights. If the latter, the comparison is not a controlled evaluation of deraining architecture quality, because the baselines were trained on different label distributions and may simply be mismatched to the test set. Please report the exact training protocol for every learning-based baseline and, ideally, provide per-method confidence intervals across multiple runs.
minor comments (4)
  1. [Equation (6)] The frequency loss divides by max(|F(P)-F(Y)|, epsilon) but it is not clear what happens when the predicted and ground-truth spectra are both zero at a particular frequency; please clarify the epsilon handling and the choice of the L1/L2 normalization.
  2. [Table 2] The 'Relative speed' column should state the reference method and whether the ratios are derived from the reported inference times; currently the entries appear mutually inconsistent.
  3. [Figure 7 caption] The caption contains a typo: 'isualizes' should be 'visualizes'.
  4. [Section 5.4] The snow generalization claim is supported only by qualitative examples. While this is acceptable as a supplementary demonstration, the text should explicitly say that no quantitative snow evaluation was performed, so readers are not misled.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: PRE-Mamba's claims are empirical, evaluated on held-out labeled data; the only overlapping-author citations are not load-bearing.

full rationale

This paper makes no theoretical derivation; its central claims are empirical accuracy and efficiency on the EventRain-27K benchmark. The training objective (L = Lce + λLfft) uses ground-truth labels Y as supervision, and the reported SR/NR/DA numbers are computed on held-out test events, not on the training fit. The dataset labels are produced by an external pipeline (KITTI/SPAC videos, rain rendering, Vid2E simulation, and KNN spatiotemporal alignment for self-recorded data), but the model neither embeds the KNN labeling rule nor evaluates with a metric that restates those labels; the model is a separate learned classifier. The KNN alignment and unspecified synthetic-label derivation are data-quality and validity risks, but they are not circularity: there is no equation in the paper that reduces a reported prediction to its own input label or to a fitted parameter. The only self-citation with author overlap, DistillNet [11], is used as a qualitative baseline and as prior voxel-based work; it is not invoked to justify any component of PRE-Mamba, to establish uniqueness, or to forbid alternative architectures. Mamba [32] and MSSM [70], the two load-bearing architectural references, are external works. Accordingly, no self-definitional, fitted-input-called-prediction, or self-citation-load-bearing step is present. Score 1 reflects a minor overlapping-author citation that is not load-bearing and the external-label dependence of the SOTA comparison, which is a correctness concern rather than circularity.

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

The central claim rests on the quality of the training data and labels, which are generated with domain-specific assumptions (Vid2E simulated events, KNN alignment). The method itself is a standard learned classifier with hand-chosen hyperparameters; no new physical entities are introduced.

free parameters (4)
  • frequency loss weight lambda = not reported
    Loss in Sec. 3.4 uses L = Lce + lambda Lfft; the value of lambda is not given in the paper and is not ablated, yet it affects the reported performance.
  • time window duration T = 0.1 seconds
    Chosen in Sec. 5.1; no ablation of T is shown, though it defines the intra-window temporal scale.
  • number of time windows = 5
    Ablated in Table 4 (3, 5, 8); 5 chosen as trade-off between DA and inference time.
  • multi-scale kernel sizes = (1, 3, 5)
    Kernel sizes for MS3M local pathway, stated in Sec. 5.1; chosen by hand.
assumptions (5)
  • domain assumption Rain events can be separated from scene events by per-event binary classification
    The framework defines deraining as event-by-event classification (Sec. 3); if rain and scene events are not separable in the 4D cloud space, the method cannot succeed.
  • domain assumption Synthetic event sequences generated by Vid2E from rain-rendered videos faithfully represent real event camera rain responses
    The synthetic labeled dataset (Sec. 4) is the primary quantitative benchmark; if the simulation is unrealistic, the reported scores do not transfer to real sensors.
  • domain assumption KNN spatiotemporal alignment between rainy and rain-free self-recorded captures yields correct background/rain labels
    Used to label the self-recorded artificial dataset (Sec. 4); no validation of label accuracy is reported.
  • domain assumption Frequency-domain distribution of rain labels correlates with rainfall intensity and frequency loss improves generalization
    Motivates Lfft in Sec. 3.4 via Figure 3; ablation support is limited to one small improvement in DA.
  • domain assumption State space models retain linear complexity and long-range modeling when applied to event point clouds
    Assumed from prior Mamba literature (Sec. 2) and architectural claims in Sec. 3.3; not independently verified for the 4D event cloud.

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Cite this review

Pith. "Pith review of PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining." pith.science (2026). https://pith.science/paper/Q52KKLG4

@misc{pith2026250505307,
  author       = {Pith},
  title        = {Pith review of: PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q52KKLG4}},
  note         = {Machine review of arXiv:2505.05307}
}
read the original abstract

Event cameras excel in high temporal resolution and dynamic range but suffer from dense noise in rainy conditions. Existing event deraining methods face trade-offs between temporal precision, deraining effectiveness, and computational efficiency. In this paper, we propose PRE-Mamba, a novel point-based event camera deraining framework that fully exploits the spatiotemporal characteristics of raw event and rain. Our framework introduces a 4D event cloud representation that integrates dual temporal scales to preserve high temporal precision, a Spatio-Temporal Decoupling and Fusion module (STDF) that enhances deraining capability by enabling shallow decoupling and interaction of temporal and spatial information, and a Multi-Scale State Space Model (MS3M) that captures deeper rain dynamics across dual-temporal and multi-spatial scales with linear computational complexity. Enhanced by frequency-domain regularization, PRE-Mamba achieves superior performance (0.95 SR, 0.91 NR, and 0.4s/M events) with only 0.26M parameters on EventRain-27K, a comprehensive dataset with labeled synthetic and real-world sequences. Moreover, our method generalizes well across varying rain intensities, viewpoints, and even snowy conditions.

Figures

Figures reproduced from arXiv: 2505.05307 by the authors.

Figure 1
Figure 1. (a) Rain severely degrades event camera data compared to conventional cameras. (b) Unlike frame-based aggregation methods, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of Our Proposed PRE-Mamba. The model takes n temporal windows as input to generate a 4D event cloud, serialized via four scanning modes. The STDF module decouples and enhances spatiotemporal features across dual temporal scales, which flow through a U-Net style encoder-decoder with multiple blocks. Each block employs MS3M as its core, integrating intra- and inter￾window branches to capture appearance and mo… view at source ↗
Figure 3
Figure 3. Power Spectrum Heatmap and Segment Length Dis￾tribution for Rainfall Intensities (5, 20, 60 mm). Top: Heatmaps show higher frequencies at peak power for lower densities, reflect￾ing sparse rain. Bottom: Distributions of continuous signals reveal longer segments at higher densities, indicating sustained rain. Our training objective combines a standard binary cross￾entropy loss (Lce) with a frequency-domain regulariza… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: EventRain-27K: The First Point-based Event Derain￾ing Dataset. Additional dataset visualizations with varying rain￾fall intensities and scenarios are included in the Appendix. 4. Dataset To support model training and testing, we published EventRain-27K, the first point…
Figure 5
Figure 5. Figure 5: Our fixed-point & speed data acquisition system. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Qualitative Comparison on Synthetic and Artificial Datasets. PRE-Mamba demonstrates superior deraining capabilities across diverse structural contours and complex textures with enhanced detail preservation. Please zoom in for details. GFLOPs Parameters Inference time (…
Figure 7
Figure 7. Figure 7: Qualitative Comparison on Real-World Datasets. The top four rows highlight our method’s superior deraining performance on real rain datasets, while the last two rows showcase its generalization to snow scenes. Please zoom in for details. Window Number Num = 3 Num = 5 N…

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Forward citations

Cited by 1 Pith paper

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  1. EDmamba: Rethinking Efficient Event Denoising with Spatiotemporal Decoupled SSMs

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    A decoupled spatial-temporal Mamba architecture denoises event camera streams with 88.9K parameters and state-of-the-art accuracy on four benchmarks.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.