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REVIEW 4 major objections 5 minor 1 cited by

WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian Splatting

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that preprocessing multi-view images by removing dense weather particles and masking lens occlusions lets 3D Gaussian Splatting reconstruct the underlying clean scene, and verifies this on new synthetic and real-world…

desk verdict WeatherGS is a plausible, well-scoped recipe for rain/snow 3D reconstruction whose load-bearing assumption—diffusion-preprocessed images are faithful enough to supervise 3DGS—is never actually tested. read the letter →

arxiv 2412.18862 v3 pith:77F4A6TB submitted 2024-12-25 cs.CV cs.AI

Chenghao Qian , Yuhu Guo , Wenjing Li , Gustav Markkula This is my paper · ORCID
classification cs.CVcs.AI
keywords 3DGaussianSplattingadverseweatherrainremovalsnowlensocclusiondiffusionmodelscenereconstructionbenchmark
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's central claim is that 3D Gaussian Splatting fails under rain and snow because it treats weather artifacts as part of the scene, and that a preprocessing stage can fix this without changing the rendering backend. WeatherGS sorts the artifacts into dense small particles (snowflakes, raindrops in the air) and sparse larger occlusions (precipitation on the lens), removes the first with a diffusion-based Atmospheric Effect Filter and detects the second with a Lens Effect Detector, then trains 3D Gaussians on the cleaned images while masking occluded pixels out of the loss. On a new benchmark of synthetic scenes and real-world footage, the paper reports that this pipeline beats vanilla NeRF, DerainNeRF, 3DGS, and GS-W on average PSNR and LPIPS, at a fraction of the training and rendering cost of a NeRF backend. If right, the contribution is a practical preprocessing recipe that makes real-time 3D reconstruction usable outdoors in bad weather.

What carries the argument

The dense-to-sparse preprocessing pipeline is the load-bearing mechanism: the Atmospheric Effect Filter (AEF) removes dense weather particles with a text-guided diffusion model, and then the Lens Effect Detector (LED) thresholds a confidence map to produce binary masks $M$. These masks enter the 3DGS training loss as a multiplication by $(1-M)$ in both the L1 term $L_1 = \sum_t |\hat{I}(t)-I(t)| \circ (1-M)$ and the D-SSIM term, so pixels judged as lens occlusions do not supervise the Gaussians. The AEF also helps the LED, because removing the dense particles makes the sparser lens occlusions easier to localize. Everything else is standard 3DGS, including initialization from structure-from-motion points, $\alpha$-compositing rendering, and the masked L1/D-SSIM objective.

What would settle it

Take a fixed scene, render it clean, then add synthetic snow or rain over it; run WeatherGS on the degraded views and compare the reconstruction to the clean render. If the restored images or reconstructed geometry are not closer to the clean ground truth than the unprocessed weather-corrupted input in non-occluded regions, the AEF is adding distortion rather than removing weather.

Watch

Extended reading notes

Core claim

WeatherGS's discovery, on its own terms, is that the two weather artifact types have opposite statistics and should be treated asymmetrically. Dense weather particles are small but numerous and vary across views, so 3DGS currently averages them into blur or reconstructs them as solid content; lens occlusions are sparser but larger and are stable enough across views to be modeled as scene geometry. The proposed remedy is to remove the dense particles first with an Atmospheric Effect Filter (AEF), a text-guided diffusion model that selects a derain or desnow task plugin by comparing a text prompt with image features, and then to generate a per-image occlusion mask with a Lens Effect Detector (LED). Training 3DGS with the L1 and D-SSIM losses masked by $(1-M)$ makes the occluded lens regions irrelevant to the optimization, so the Gaussians model the clear scene. The paper reports that this consistently improves average PSNR and LPIPS over the baselines, while noting that SSIM sometimes favors vanilla 3DGS because the baselines train on the original, unprocessed images.

Load-bearing premise

The entire pipeline hinges on the Atmospheric Effect Filter removing weather particles while leaving the underlying scene untouched, because if the diffusion model distorts or invents content, 3DGS is trained on corrupted targets and the 'clean' reconstruction is not the real scene.

Editorial extensions

If this is right

  • WeatherGS makes 3DGS usable in outdoor rain and snow without architectural changes to the splatting itself, so existing 3DGS tools and pipelines can adopt weather robustness as a preprocessing step.
  • With a 3DGS backend the pipeline renders a frame in about 0.02 seconds after roughly 28 minutes of training on one RTX 3090, whereas the same preprocessing with a NeRF backend takes hundreds of minutes to train and over 30 seconds per render; the paper presents this as making real-time applications feasible.
  • The benchmark introduces three synthetic scenes (Tanabata, Factory, Pool) in snowy and rainy versions plus two real-world video-derived scenes, giving the community a common testbed for weather-degraded 3D reconstruction.
  • The ablation results imply that neither component alone suffices: the AEF alone leaves lens occlusions, the LED alone leaves dense particles, and the combination gives the best LPIPS.

Reading between the lines

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

  • An implication the paper leaves implicit is that the text-driven plugin selection in the AEF generalizes beyond rain and snow: the same cosine-similarity switch could route fog, haze, dust, or spray to their own task plugins, making the pipeline a single framework for many weather types.
  • A testable extension is to validate against true clean geometry: capture or render the same scene both clear and weather-degraded, run WeatherGS on the degraded views, and compare the reconstructed 3D geometry (not just rendered images) to the clear capture; if the diffusion filter hallucinates detail, image metrics can look good while geometry drifts.
  • The masked-loss idea also suggests a pipeline-level improvement: instead of accepting the LED mask as fixed, the masks could be refined jointly with the 3DGS optimization, since occluded pixels are exactly those where the rendered clear scene disagrees with the input in a spatially coherent way.
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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 / 5 minor

Summary. The paper proposes WeatherGS, a 3D Gaussian Splatting framework for reconstructing clean 3D scenes from multi-view images captured under rainy or snowy conditions. The method decomposes weather artifacts into dense particles and sparse lens occlusions, removes particles with a diffusion-based Atmospheric Effect Filter (AEF) using Diff-Plugin task plugins, detects occlusion masks with a Lens Effect Detector (LED) based on the AttGAN detector, and trains 3DGS on the preprocessed images while masking out occluded regions. The authors introduce a synthetic benchmark derived from Deblur-NeRF scenes with ground truth, and report quantitative comparisons against NeRF, DerainNeRF, 3DGS, and GS-W, plus qualitative results on two real-world scenes. The main claims are that WeatherGS outperforms existing methods in PSNR and LPIPS on average and recovers the underlying clear scene.

Significance. If validated, WeatherGS would offer a practical and modular recipe for extending 3DGS to adverse weather by leveraging off-the-shelf 2D restoration and detection models. The synthetic benchmark with ground truth is a useful resource for future work, and the dense-to-sparse decomposition is a sensible design principle. The paper is not circular: the external pretrained components (Diff-Plugin, Stable Diffusion, AttGAN) are used as black boxes, and the only self-citation [28] is in related work, not in the method. However, the central claim that the AEF outputs faithfully preserve the underlying scene is not directly validated, and the SSIM regressions in the snowy scenes weaken the 'consistently outperforming' statement. The significance is therefore conditional on additional evidence about the fidelity and multi-view consistency of the preprocessed images.

major comments (4)
  1. [Section III-B, Section IV-C, Table I] The load-bearing step of the pipeline is the AEF-produced image i' used as the 3DGS training target; for the final radiance field to represent the true clear scene, i' must remove weather particles without altering scene geometry or appearance and must be consistent across views. The paper does not measure the fidelity of i' against the known clean ground truth available in the synthetic benchmark, nor does it measure cross-view consistency. Section IV-C concedes that integrating both components 'may introduce minor impacts to structural details due to the diffusion process,' and the SSIM results in Table I (e.g., Factory-snow: WeatherGS 0.700 vs 3DGS 0.768; average snowy: 0.787 vs 0.821) are consistent with structural loss in the preprocessed targets. Please add a direct evaluation of i' against the clean images (PSNR/SSIM/LPIPS) and a cross-view consistency metric, or the claim of 'accurately recover the underlying clear scene' is not supported.
  2. [Tables I and II, Abstract] The abstract states that WeatherGS 'consistently produces high-quality, clean scenes across various weather scenarios, outperforming existing state-of-the-art methods,' but the quantitative results are more mixed. In Table I, WeatherGS has lower SSIM than vanilla 3DGS on all snowy scenes (average 0.787 vs 0.821), and in Table II it is lower on Pool-rain (0.805 vs 0.817) while higher on Tanabata-rain and Factory-rain. The explanation in Section IV-B that sparse weather artifacts 'do not significantly impact the SSIM metric' does not address the systematic SSIM drop on snowy scenes. The claim should either be tempered or supported by additional evidence (e.g., a perceptual user study, or a discussion of which metric matters for the target application).
  3. [Section III-B/C, Eqns (4), (5), (8)] The method has at least three free parameters that are not reported: the plugin-selection threshold θ in Eq. (4), the mask binarization threshold t in Eq. (5), and the loss balancing weight λ in Eq. (8). No sensitivity analysis or error bars are provided for any experiment. Since the final quality likely depends on t (which determines how much of the lens occlusion is masked) and λ, the absence of these values and of a robustness study prevents reproduction and makes it hard to assess whether the reported PSNR/LPIPS gains are stable.
  4. [Section IV-A] The real-world evaluation is purely qualitative because ground truth is unavailable; this is an acceptable limitation, but it means the claim of generalization to 'various weather scenarios' is not quantitatively supported for real data. Please either state this limitation more explicitly in the conclusions or add a quantitative proxy such as a user study, no-reference metrics, or evaluation on a publicly available real dataset with known 3D scans.
minor comments (5)
  1. [General] Section III-A contains the typo 'Prelimineries' (should be 'Preliminaries'), and Section IV-B contains 'dense-to-spare' (should be 'dense-to-sparse').
  2. [Eqns (2), (6)] The equations are poorly typeset: Eq. (2) shows '|N |X' instead of a summation, and Eq. (6) has garbled notation that appears to mix L1 and L2 norms. Please correct the mathematical formatting throughout.
  3. [References] References [6] and [41] are both the original NeRF paper and should be merged; the reference list also contains incomplete URLs (e.g., [39], [40] are only 'Video link.' without working hyperlinks in the text).
  4. [Table III] In Table III, the full method (Ours) has lower SSIM (0.7395) than w/o AEF (0.7452); this is related to the major comment about AEF fidelity and should be discussed in the ablation section.
  5. [Section IV-A] The description of the real-world dataset extraction is incomplete: the paper should state how many keyframes were used, how camera poses were obtained (e.g., SfM), and whether the scenes are static or contain dynamic content.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: WeatherGS relies on external pretrained modules and standard 3DGS; the only author self-citation appears in related work and is not load-bearing.

full rationale

The claimed derivation chain is: multi-view weather images -> AEF (Diff-Plugin [32], Stable Diffusion [31], CLIP [33]) removes dense particles -> LED (AttGAN detector [24]) masks lens occlusions -> masked 3DGS [7] training. None of these components is fitted to the benchmark or defined in terms of WeatherGS's output. The AEF and LED are pretrained external modules, with text-prompt threshold selection in Eq. 4, and the 3DGS backend is the standard algorithm. The final clean scene is supervised by the preprocessed images, so the result is only as faithful as the preprocessing, but that is a correctness/robustness limitation, not a circular reduction. The paper itself flags this risk: 'directly applying diffusion models can lead to content distortion' (Section III-B) and 'integrating both components may introduce minor impacts to structural details due to the diffusion process' (Section IV-C). The only author-overlapping citation is [28] AllWeather-Net in the related-work survey of unified 2D architectures; it is not used in the method, experiments, or ablations, so it does not carry the argument. No equation in the paper is equivalent by construction to another, and no fitted parameter is renamed as a prediction. The score of 2 reflects a minor, non-load-bearing self-citation rather than any circularity of the central method.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper contains no first-principles derivation. It relies on external pretrained components and a synthetic benchmark. The ledger lists the hand-set thresholds and the transfer assumptions behind the preprocessing modules; no invented entities are introduced.

free parameters (3)
  • Plugin selection threshold theta
    Equation (4) chooses Dderain vs Ddesnow via cosine similarity; theta is never specified in text.
  • Mask binarization threshold t
    Equation (5) converts LED confidence map into a binary mask; t is never specified.
  • Loss balancing weight lambda
    Equation (8) blends L1 and D-SSIM; lambda is never specified.
assumptions (5)
  • standard math 3DGS alpha-compositing model (Eqs 1-3, 6-8) correctly models scene radiance under processed images.
    The method relies on the standard 3DGS differentiable rendering and photometric losses.
  • domain assumption Pretrained Diff-Plugin task plugins remove rain and snow from arbitrary new images while preserving content.
    Section III-B uses Diff-Plugin with CLIP-selected plugins; no fine-tuning on the WeatherGS benchmark is described beyond plugin selection.
  • domain assumption AttGAN-based Lens Effect Detector, trained on raindrop images, generalizes to snow and rain lens occlusions after AEF.
    Section III-B 'Lens Effect Detector' reuses the AttGAN detection module and Section IV-A trains it on Qian et al.'s dataset; transfer to the benchmark scenes is assumed.
  • domain assumption The synthetic Blender scenes with particle systems and OpenCV enhancements are representative of real adverse weather.
    Section IV-A builds the synthetic benchmark this way and uses it for all quantitative claims; real scenes are only qualitatively evaluated.
  • ad hoc to paper Diffusion restoration preserves the true clean scene rather than hallucinating plausible content.
    Section III-B notes diffusion can distort content; Section IV-C concedes the integration 'may introduce minor impacts to structural details.'

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Cite this review

Pith. "Pith review of WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian Splatting." pith.science (2026). https://pith.science/paper/77F4A6TB

@misc{pith2026241218862,
  author       = {Pith},
  title        = {Pith review of: WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian Splatting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/77F4A6TB}},
  note         = {Machine review of arXiv:2412.18862}
}
read the original abstract

3D Gaussian Splatting (3DGS) has gained significant attention for 3D scene reconstruction, but still suffers from complex outdoor environments, especially under adverse weather. This is because 3DGS treats the artifacts caused by adverse weather as part of the scene and will directly reconstruct them, largely reducing the clarity of the reconstructed scene. To address this challenge, we propose WeatherGS, a 3DGS-based framework for reconstructing clear scenes from multi-view images under different weather conditions. Specifically, we explicitly categorize the multi-weather artifacts into the dense particles and lens occlusions that have very different characters, in which the former are caused by snowflakes and raindrops in the air, and the latter are raised by the precipitation on the camera lens. In light of this, we propose a dense-to-sparse preprocess strategy, which sequentially removes the dense particles by an Atmospheric Effect Filter (AEF) and then extracts the relatively sparse occlusion masks with a Lens Effect Detector (LED). Finally, we train a set of 3D Gaussians by the processed images and generated masks for excluding occluded areas, and accurately recover the underlying clear scene by Gaussian splatting. We conduct a diverse and challenging benchmark to facilitate the evaluation of 3D reconstruction under complex weather scenarios. Extensive experiments on this benchmark demonstrate that our WeatherGS consistently produces high-quality, clean scenes across various weather scenarios, outperforming existing state-of-the-art methods. See project page:https://jumponthemoon.github.io/weather-gs.

Figures

Figures reproduced from arXiv: 2412.18862 by the authors.

Figure 1
Figure 1. Rendering examples under adverse weather conditions. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The overview of WeatherGS. The WeatherGS preprocesses multi-view images by removing weather-related visual artifacts. An [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The pipeline of atmospheric effect filter. A snowy scene [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Qualitative results on synthetic and real-world datasets under snowy and rainy conditions. NeRF-based methods show blurring [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: The comparison results of using NeRF and 3DGS backend. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.