RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction

Brett Seymour; Guoxi Huang; Haoran Wang; Nantheera Anantrasirichai; Zhuodong Jiang

REVIEW 5 major objections 5 minor 31 references

RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction

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

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Pith's one-line read RUSplatting claims sparse-view underwater 3D reconstruction improves by coupling per-channel water-attenuation physics into Gaussian splatting, yielding PSNR gains up to 1.90 dB.

desk verdict Solid specialized contribution with a new deep-sea dataset, but the headline PSNR gain is not backed by per-scene statistics and the method leans heavily on pseudo-depths that the authors admit are off-domain. read the letter →

arxiv 2505.15737 v2 pith:HRFHYJBF submitted 2025-05-21 cs.CV

Zhuodong Jiang , Haoran Wang , Guoxi Huang , Brett Seymour , Nantheera Anantrasirichai This is my paper · ORCID

classification cs.CV

keywords underwater3DreconstructionGaussiansplattingsparse-viewnovelviewsynthesisimageformationmodelframeinterpolationadaptiveweightingedge-awaresmoothnesslossdeep-seadataset

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 tries to establish that 3D Gaussian Splatting can reconstruct deep underwater scenes from sparse views if the water's wavelength-dependent absorption and scattering are modeled per color channel and if training is enriched with interpolated frames and edge-aware noise suppression. This matters because marine archaeology, biology, and robotics rely on reconstructing sites from limited imagery, where existing methods assume clear air or dense, temporally coherent input. The authors claim consistent gains over state-of-the-art baselines on three datasets, including their new deep-sea Submerged3D set, with best PSNR gain of 1.90 dB.

What carries the argument

The central mechanism is per-channel medium-parameter prediction: an MLP takes position-encoded depth and viewpoint and outputs medium parameters for each Gaussian and each color channel, producing the corrected color $c^m_{i,\text{ch}} = T^D_{i,\text{ch}} \cdot c_{i,\text{ch}} + (1 - T^B_{i,\text{ch}}) \cdot b_{i,\text{ch}}$. Supporting this, Intermediate Frame Interpolation (IFI) uses RIFE to synthesize frames between sparse inputs, Adaptive Frame Weighting (AFW) tunes each interpolated frame's loss weight via a learnable uncertainty parameter, and an Edge-Aware Smoothness Loss (ESL) uses depth discontinuities from pseudo-depth maps as edge weights to smooth noise while preserving structure.

What would settle it

Train RUSplatting on an underwater scene where ground-truth depth is available (for example, a synthetic water tank or a capture with a depth sensor) and compare against the pseudo-depth variant; if replacing Depth-Anything-V2 depths with true depths does not improve or preserves the reported gains, the claimed coupling between depth accuracy and medium estimation is not supported.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that decoupling the underwater image formation physics per RGB channel, with attenuation $T^D_{\text{ch}}$ and backscatter $T^B_{\text{ch}}$ varying with distance $z$, inside an affine color transform of each Gaussian, combined with interpolated intermediate frames and a depth-guided edge-aware smoothness loss, makes sparse-view underwater novel-view synthesis robust. The method builds directly on 3D Gaussian Splatting and replaces a shallow MLP with a five-layer MLP that predicts per-channel medium parameters $\{T^D, T^B, \beta^d, \beta^b, b\}$. The authors report that RUSplatting outperforms five baselines across PSNR, SSIM, and LPIPS on SeaThru-NeRF, S-UW, and Submerged3D.

Load-bearing premise

The single load-bearing assumption is that pseudo-depth maps from Depth-Anything-V2 are accurate enough in deep-sea, low-light, high-turbidity images to supervise geometry and to set edge weights in the smoothness loss; if they are not, the decoupled physics and depth losses push the optimization with incorrect depth values.

Editorial extensions

If this is right

Underwater scenes with roughly 20 training views per sequence can be rendered at higher fidelity than previous underwater NeRF and Gaussian Splatting methods, supporting interactive onshore inspection of shipwrecks and marine sites.
Per-channel affine color correction restores color in high-turbidity deep-sea footage without a separate image-enhancement network, reducing color cast during reconstruction itself.
Interpolated frames combined with learnable weighting make the pipeline more tolerant of wide baselines and limited overlap between input views.
The release of Submerged3D provides a real deep-sea benchmark with low light, severe turbidity, and shipwreck scenes, enabling direct comparisons for future underwater reconstruction work.

Reading between the lines

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

If the per-channel physics decoupling is the true source of gain, the same affine model could transfer to other turbid media such as fog or murky rivers, where wavelength-dependent attenuation is weaker; a direct comparison on fog data would test that transfer.
The method's reliance on Depth-Anything-V2 and RIFE suggests that fine-tuning these estimators on underwater imagery, or substituting physics-based depth estimates, would further improve robustness — a limitation the authors themselves note.
A stronger diagnostic would be rendering only the training views rather than held-out views, to see whether the PSNR gain reflects improved geometry or mainly improved color post-processing.
The depth-guiding role of ESL means the method's noise suppression quality is coupled to monocular depth accuracy; comparing against a variant using edge weights from the rendered image itself would isolate that dependency.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Worth a careful look. The genuinely new pieces are the RGB-decoupled medium estimation with a deeper MLP, the interpolated-frame pipeline with uncertainty-based adaptive weighting, the edge-aware smoothness loss, and the Submerged3D dataset of deep-sea wrecks. Code and data are public, which is more than many papers in this niche provide. The ablation study is cleanly structured and each component earns its keep; the IFI and AFW ablations in particular show large, plausible effects.

The soft spots are the ones the reader flagged. Table 1 reports only dataset-level averages, and the text claims gains over baselines that don't match the abstract's 1.90 dB figure — the table shows 2.8–3.8 dB over the best baseline, so the abstract actually undersells it, but the lack of per-scene numbers and error bars makes "consistently outperforms" hard to verify. Hyperparameters (λs, λb, α, deeper-MLP choice) are selected via grid search without a clear validation split, so some of the gain may be tuning. More fundamentally, pseudo-depth from Depth-Anything-V2 is used in three load-bearing places: depth supervision, edge weights in ESL, and the z in the per-channel transmission model. The authors themselves concede in Section 6 that RIFE and Depth-Anything-V2 aren't designed for underwater. If pseudo-depth is biased in deep-sea turbidity, the attenuation decoupling and depth loss push in the same wrong direction. That doesn't sink the paper — the method still works better than baselines in the reported conditions — but it means the robustness claim is narrower than the title suggests. A sensitivity analysis on pseudo-depth quality (e.g., scaling or perturbing D) would address this directly.

On balance, this is a legitimate extension-level contribution with a useful dataset and public code. The central empirical claim is plausible, not airtight. I'd send it to peer review and ask for per-scene results, error bars, and a pseudo-depth sensitivity study in the revision. The stress-test note overstates the 1.90 dB issue slightly — that number appears to come from a different comparison — but the underlying concern about verification is fair.

Referee Report

5 major / 5 minor

Summary. The manuscript proposes RUSplatting, a 3D Gaussian Splatting framework for sparse-view underwater scene reconstruction. The method combines four contributions: per-RGB-channel decoupling of underwater attenuation and backscatter parameters estimated by a deeper MLP, an intermediate frame interpolation (IFI) mechanism with adaptive per-frame uncertainty weighting (AFW), an edge-aware smoothness loss (ESL) that uses pseudo-depth discontinuities to denoise while preserving structure, and a newly collected deep-sea dataset, Submerged3D. Experiments are reported on SeaThru-NeRF, S-UW, and Submerged3D against five baselines, with the claim that RUSplatting consistently outperforms all baselines across PSNR, SSIM, and LPIPS.

Significance. If the empirical claims hold, the paper offers a practical and well-motivated recipe for reconstructing sparse-view, low-light underwater scenes, and the release of a new deep-sea dataset and public code is a useful community contribution. The core priors (Depth-Anything-V2, RIFE, R-UIFM) are external and not produced by the authors, so circularity risk is low. However, the current evidence does not yet substantiate the headline claims: there are unresolved quantitative inconsistencies between the abstract, Section 5.2, Table 1, and the ablation study, and the reported robustness of the method is not supported by per-scene results, error bars, or a sensitivity analysis of the pseudo-depth dependency.

major comments (5)

[Abstract and Section 5.2, Table 1] The abstract's claim of 'PSNR gains up to 1.90dB' is not supported by Table 1. The largest improvement over the best baseline in Table 1 is 1.22 dB (Submerged3D, RUSplatting 25.7990 vs. WaterSplatting 24.5832); the 2.83, 3.01, and 3.84 dB figures quoted in Section 5.2 correspond to the average improvement over all five baselines, not the improvement over the best baseline, and this should be stated explicitly. The authors should either reconcile the 1.90 dB figure with a specific scene-level result or remove it.
[Section 4.3, Section 4.4, Section 5.1] Depth-Anything-V2 pseudo-depth is used simultaneously as the depth z in the per-channel transmission model (Eq. 5), as the supervision target for LDepth (Eq. 10), and as the edge-weight source in ESL (Eq. 9). Because Section 6 concedes that Depth-Anything-V2 is not designed for underwater conditions, systematic errors in pseudo-depth could propagate into both the attenuation decoupling and the geometry supervision. The paper provides no sensitivity analysis, no comparison with alternative depth sources, and no depth-quality diagnostics. A concrete test, such as perturbing pseudo-depth or evaluating on a scene with known depth, is needed to establish that the reported gains are robust to this dependency.
[Table 1 and Section 5.2] The claim that RUSplatting 'consistently outperforms' all baselines across all metrics is based solely on dataset-level averages over four scenes per dataset. No per-scene numbers, standard deviations, or significance tests are reported. This is particularly important because the Submerged3D SSIM margin over UW-GS is only 0.0001 (0.7724 vs. 0.7723), and the LPIPS differences are small. Per-scene tables and error bars are needed to verify the across-scene robustness that the word 'consistently' implies.
[Section 5.3] The ablation study contains a numerical inconsistency with Table 1. The text reports that the full RUSplatting model achieves an average PSNR of 25.7874 across all datasets, but the mean of the three dataset-averaged PSNR values in Table 1 is 26.8958. The ablation also reports M5 at 25.03 PSNR, implying an AFW gain of 0.76 dB, yet no per-dataset ablation breakdown is given. The authors should clarify the exact subset and weighting used in the ablation averages and provide per-scene ablation results.
[Sections 4.2–4.3 and Section 5.1] Hyperparameters such as alpha, lambda_b, lambda_s, the loss weights, and the choice of a 5-layer MLP are selected via grid search or hyperparameter sensitivity analysis, but no validation split is described. If these choices are tuned on the test scenes used for evaluation, part of the reported improvement could reflect favorable hyperparameter selection rather than the proposed components. The validation protocol should be stated, and a fixed validation split or nested selection procedure should be used.

minor comments (5)

[Abstract and Section 1] The abstract claims improved 'geometric accuracy,' but no quantitative geometric evaluation is reported; the paper relies on pseudo-depth only as weak supervision. Either add depth evaluation metrics or temper the geometric-accuracy claim.
[Section 5.3, Figure 4] The ablation configuration table marks both 'IFI w/o AFW' and 'IFI w/ AFW' as enabled for the final RUSplatting column, which is confusing. The rows should clearly indicate that the final model uses IFI with AFW only.
[Eq. (7) and Eq. (11)] The gamma notation is inconsistent: Eq. (7) uses gamma_f for each interpolated frame, while Eq. (11) writes only gamma. The subscript should be restored in Eq. (11) for clarity.
[Section 1] The listed percentage improvements '21.37%, 4.99%, and 5.09%' relative to SeeThru-NeRF, WaterSplatting, and UW-GS do not have their baseline definitions stated; specify whether these are relative PSNR improvements and against which exact baseline configurations.
[Table 1] The table caption says red bold denotes the best result and yellow the second best, but no yellow highlighting is visible in the provided text; ensure the final rendering shows both annotations or revise the caption.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: central claims are empirical comparisons with external priors and baselines.

full rationale

The paper's central claims (Table 1, ablations) are empirical, not derived from its own fitted parameters. The underwater color model in Eqs. 5-6 is a per-channel application of the externally established R-UIFM [1] and is not used to fabricate a prediction. Pseudo-depth from Depth-Anything-V2 and interpolated frames from RIFE are external, pre-trained components, not outputs of RUSplatting; using the same pseudo-depth in Eq. 5, LDepth, and ESL weights is a shared external input, not a self-referential derivation. The AFW uncertainty weights and medium parameters are optimized on training views and evaluated on held-out views, so gains are measured rather than forced by construction. Self-citations ([10] review, [27] UW-GS, [19]) appear only as motivation or baselines; [27] is compared via its official implementation, and the ablation is internal. The Section 6 concession that RIFE and Depth-Anything-V2 are not designed for underwater conditions is a robustness or transfer risk, not circularity. No equation reduces by definition to another, and no fitted parameter is renamed as a prediction.

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

The method's contributions rest on two pretrained external models (Depth-Anything-V2 and RIFE) whose validity in the deep-sea domain is assumed rather than demonstrated, plus the R-UIFM physics model. Most degrees of freedom are learned within the network, but the weighting hyperparameters are tuned on the same data used for final evaluation.

free parameters (5)

lambda_s (smoothness loss weight) = 0.2
Set by hand in Section 4.4; balances ESL against reconstruction and depth losses. No ablation over this weight is shown.
lambda_r, lambda_d, lambda_ca (loss weights) = 0.8, 0.1, 1.0
Fixed in Section 5.1; no sensitivity analysis reported in the main text.
alpha (AFW regularization) = grid-searched in [0,1], exact value deferred to supplementary
Controls uncertainty weight in Eq. 7; selected by grid search, and because no validation split is described, selection may use test performance.
lambda_b (edge sensitivity) = grid-searched in [0,5], exact value deferred to supplementary
Controls depth-discontinuity weighting in ESL Eq. 9; selected via grid search on the evaluation datasets.
per-frame uncertainty gamma_f (AFW) = learned during training
Each interpolated frame gets a learnable scalar in Eq. 7; it is optimized jointly with the model, effectively a per-example weight.

assumptions (4)

domain assumption R-UIFM (Eq. 3) is an adequate model of underwater image formation in the tested deep-sea scenes.
The whole color-decoupling pipeline is built on this model from Akkaynak and Treibitz [1]; deviations (e.g., non-homogeneous water, artificial illumination) are not modeled.
domain assumption Depth-Anything-V2 pseudo-depth is accurate enough in deep-sea, low-light, high-turbidity imagery to supervise rendered depth and compute ESL edge weights (Section 4.3).
No ground-truth depth exists in any dataset; the paper acknowledges in Section 6 that Depth-Anything-V2 is not designed for underwater conditions.
domain assumption RIFE interpolated frames are geometrically plausible additions to the training set.
The adaptive weighting (AFW) mitigates but cannot eliminate artifacts from synthetic frames; the method assumes more views is beneficial overall.
domain assumption The grey-world assumption that mean channel intensity should be 0.5 improves color restoration.
Lg loss (Section 4.4) assumes natural scenes average to mid-gray; deep-sea scenes dominated by blue may violate this, and the term may bias colors.

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Cite this review

Pith. "Pith review of RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction." pith.science (2026). https://pith.science/paper/HRFHYJBF

@misc{pith2026250515737,
  author       = {Pith},
  title        = {Pith review of: RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HRFHYJBF}},
  note         = {Machine review of arXiv:2505.15737}
}

read the original abstract

Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aquatic environments. This paper presents an enhanced Gaussian Splatting-based framework that improves both the visual quality and geometric accuracy of deep underwater rendering. We propose decoupled learning for RGB channels, guided by the physics of underwater attenuation, to enable more accurate colour restoration. To address sparse-view limitations and improve view consistency, we introduce a frame interpolation strategy with a novel adaptive weighting scheme. Additionally, we introduce a new loss function aimed at reducing noise while preserving edges, which is essential for deep-sea content. We also release a newly collected dataset, Submerged3D, captured specifically in deep-sea environments. Experimental results demonstrate that our framework consistently outperforms state-of-the-art methods with PSNR gains up to 1.90dB, delivering superior perceptual quality and robustness, and offering promising directions for marine robotics and underwater visual analytics. The code of RUSplatting is available at https://github.com/theflash987/RUSplatting and the dataset Submerged3D can be downloaded at https://zenodo.org/records/15482420.

Figures

Figures reproduced from arXiv: 2505.15737 by the authors.

**Figure 2.** Sample images from Submerged3D dataset. (described in Section 4.3), with a weight λs of 0.2. Additionally, we integrate the adaptive frame weighting for interpolated frames as described in Section 4.2. Consequently, the final loss function is in the form of, Lf inal = LRec +LDepth +Lg +λsLSmooth, (10) for the original frame, while for the interpolated frame, the loss function turns to, L ′ f inal = 1 2 · γ ·Lf inal … view at source ↗

**Figure 3.** Novel view rendering comparison on the Submerged3D, SeaThru-NeRF and S [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Left: Ablation study configurations. Right: Average results of all scenes. We [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

A revised underwater image formation model

Derya Akkaynak and Tali Treibitz. A revised underwater image formation model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6723–6732, 2018

work page 2018

[2] [2]

A survey on 3d gaussian splatting

Guikun Chen and Wenguan Wang. A survey on 3d gaussian splatting. arXiv preprint arXiv:2401.03890, 2024

arXiv 2024

[3] [3]

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John Y Chiang and Ying-Ching Chen. Underwater image enhancement by wavelength compensation and dehazing. IEEE transactions on image processing , 21(4):1756– 1769, 2011

work page 2011

[4] [4]

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Paulo LJ Drews, Erickson R Nascimento, Silvia SC Botelho, and Mario Fernando Mon- tenegro Campos. Underwater depth estimation and image restoration based on single images. IEEE computer graphics and applications, 36(2):24–35, 2016

work page 2016

[5] [5]

Y . Du, Z. Zhang, P. Zhang, F. Sun, and X. Lv. UDR-GS: Enhancing Underwater Dy- namic Scene Reconstruction with Depth Regularization. Symmetry, 16(8):1010, 2024. doi: 10.3390/sym16081010

work page doi:10.3390/sym16081010 2024

[6] [6]

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Zhenqi Fu, Huangxing Lin, Yan Yang, Shu Chai, Liyan Sun, Yue Huang, and Xinghao Ding. Unsupervised underwater image restoration: From a homology perspective. In Proceedings of the AAAI conference on artificial intelligence , volume 36, pages 643– 651, 2022

work page 2022

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Salma P González-Sabbagh and Antonio Robles-Kelly. A survey on underwater com- puter vision. ACM Computing Surveys, 55(13s):1–39, 2023

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AquaNeRF: Neural Radiance Fields in Underwater Media with Distractor Removal

Luca Gough, Adrian Azzarelli, Fan Zhang, and Nantheera Anantrasirichai. AquaNeRF: Neural Radiance Fields in Underwater Media with Distractor Removal. InProceedings of the 2025 IEEE International Symposium on Circuits and Systems (ISCAS) . IEEE, 2025

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