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REVIEW 4 major objections 7 minor 1 cited by

Visual enhancement and 3D representation for underwater scenes: a review

T0 review · 4 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This review makes the case that underwater visual enhancement and underwater 3D reconstruction are one coupled problem, and reports that integrated physics-based methods such as UW-GS produce the clearest novel views in the tested scenes.

desk verdict A genuinely useful survey of underwater enhancement and 3D reconstruction, but the benchmark section overclaims: the abstract promises quantitative evaluation and Section 5 delivers only visual inspection on two scenes, using the authors' own methods. read the letter →

arxiv 2505.01869 v2 pith:NL5MFASL submitted 2025-05-03 cs.CV

Guoxi Huang , Haoran Wang , Brett Seymour , Evan Kovacs , John Ellerbroc , Dave Blackham , Nantheera Anantrasirichai This is my paper · ORCID
classification cs.CV
keywords underwaterimageenhancement3DreconstructionformationmodelneuralradiancefieldsGaussiansplattingscatteringmedianovelviewsynthesisbenchmarkdatasets
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 sets out to fill a gap it identifies in the literature: no previous review covers underwater visual enhancement and underwater 3D reconstruction together. It organizes the field into a unified taxonomy, spanning physics-based models, classical image processing, deep learning, and the newer neural rendering families, and then benchmarks three reconstruction routes on public underwater datasets: reconstruction without enhancement, a two-stage enhance-then-reconstruct pipeline, and integrated models that embed water physics into reconstruction. Its central empirical claim is that the integrated route, exemplified by UW-GS, produces the clearest novel views in the tested scenes, because modeling scattering and attenuation inside the renderer beats correcting images beforehand. A sympathetic reader would take this as evidence that enhancement and geometry estimation should be treated as a single problem rather than separate steps.

What carries the argument

The load-bearing object is the Jaffe–McGlamery underwater image formation model, which writes the observed image as the sum of direct transmission, forward scattering, and backscattering: $I(x)=I_d(x)+I_f(x)+I_b(x)$, usually simplified to $I(x)=J(x)T(x)+A(1-T(x))$. This model connects the two halves of the review: enhancement methods are categorized by how they estimate the transmission map $T$ and ambient light $A$, while reconstruction methods are categorized by whether they treat water as a medium inside the renderer or ignore it. The integrated pipelines highlighted in the benchmark, especially UW-GS, embed this model into a 3D Gaussian Splatting renderer so that scattering and attenuation are estimated per scene rather than removed in a preprocessing step.

What would settle it

Run a controlled comparison of UW-GS against a two-stage enhancement-plus-reconstruction pipeline on a larger, more diverse set of underwater scenes using quantitative metrics such as PSNR, SSIM, and LPIPS; if the two-stage pipeline matches or beats UW-GS on average, the paper's central ranking claim fails.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims to be the first systematic review to span both sides of the underwater vision problem: restoring what cameras see and reconstructing the 3D scene behind those images. It builds a taxonomy that ranges from histogram and Retinex methods through dark-channel priors and data-driven CNNs, transformers, Mamba, and diffusion models on the enhancement side, and from photogrammetry and visual SLAM through NeRF and 3D Gaussian Splatting on the reconstruction side. Its empirical section compares three pipelines: raw reconstruction, enhancement followed by reconstruction, and integrated physics-based reconstruction, using public datasets including NUSR, SeaThru, S-UW, UWBundle, and BVI-Coral. The reported outcome is that integrated models such as UW-GS render the sharpest, most color-correct novel views, that 3DGS captures fine texture better than NeRF where texture exists but blurs information-poor areas, and that dynamic NeRF variants struggle with high-frequency underwater detail. The paper frames this as evidence that the field is converging on embedding the physics of underwater light into the reconstruction itself.

Load-bearing premise

The benchmark conclusions assume the handful of public underwater 3D datasets used, including NUSR, SeaThru, S-UW, UWBundle, and BVI-Coral, represent the range of real underwater conditions, and that visual inspection of one scene pair is enough to rank methods like UW-GS.

Editorial extensions

If this is right

  • If the paper's conclusion holds, future underwater vision systems should couple enhancement with reconstruction rather than treating image restoration as an optional preprocessing step.
  • Reviews and taxonomies of underwater imaging should include both enhancement and 3D reconstruction in one framework, since the physical model that explains color loss also explains reconstruction failure.
  • The benchmark suggests that 3D Gaussian Splatting is the more practical base for static underwater scenes, with NeRF remaining preferable for dynamic scenes.
  • Public underwater 3D datasets are too scarce and small to support strong generalizations, so the field's next bottleneck is data collection, not algorithms.
  • New methods that compare against integrated physics-based models like UW-GS will need to report whether enhancement is embedded or done in advance, because that choice affects the outcome.

Reading between the lines

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

  • Beyond the paper: if integrated medium-aware rendering keeps winning, the default for underwater novel-view synthesis is likely to shift away from two-stage enhance-then-reconstruct pipelines, making enhancement a component of the renderer rather than a separate artifact.
  • Beyond the paper: the reliance on a handful of small public datasets means the reported ranking is fragile; a larger multi-condition benchmark could plausibly overturn UW-GS's top placement.
  • Beyond the paper: the same physics-based medium modeling could be tested on downstream tasks such as underwater depth estimation, object detection, or ROV navigation, where enhanced images may or may not help depending on the task.
  • A testable extension is synthetic data: render scenes under several known water types and turbidities with ground-truth geometry, then compare integrated versus two-stage reconstruction quantitatively under controlled conditions.
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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 / 7 minor

Summary. This manuscript presents a review of underwater visual enhancement (UVE) and underwater 3D reconstruction. It introduces the Jaffe–McGlamery model and simplified image formation models, organizes UVE methods into conventional, data-driven, and hybrid categories, and reviews reconstruction approaches ranging from photogrammetry and visual SLAM to NeRF and 3D Gaussian Splatting. The paper also reports a small benchmark of three pipelines: reconstruction without enhancement, two-stage enhancement-then-reconstruction, and integrated physics-based reconstruction, with a qualitative comparison on two scenes (Panama and Reef). The authors claim this is the first unified review covering both UVE and underwater 3D reconstruction.

Significance. If the promised quantitative evaluation were delivered, the benchmark would provide a useful reference point for practitioners. The paper's taxonomy is broad and current, including Mamba- and diffusion-based enhancement methods and recent underwater Gaussian splatting approaches, and it usefully compiles public datasets and discusses open challenges. However, the central empirical contribution currently rests on qualitative inspection of two scenes and on methods with overlapping authorship, which limits the independent value of the benchmark.

major comments (4)
  1. [Section 5 and Abstract; Figure 19] The abstract states that the paper conducts 'both quantitative and qualitative evaluations' of state-of-the-art UVE and underwater 3D reconstruction algorithms across multiple benchmark datasets, but Section 5 reports no quantitative metrics (no PSNR, SSIM, LPIPS, or error bars), and the conclusion that 'UW-GS appears to be the best' is drawn from visual inspection of two scenes. This gap bears directly on contribution (3) in Section 1.3. Please either add a quantitative protocol with per-scene metrics and multiple random seeds, or revise the abstract and Section 1.3 to describe Section 5 as a qualitative illustration only.
  2. [Sections 5.2 and 5.3; Huang et al. (2025) and Wang et al. (2025)] The two-stage pipeline uses the authors' unpublished enhancement method (Huang et al., 2025), and the integrated pipeline highlights UW-GS (Wang et al., 2025), which shares authors with this survey. No code, checkpoints, or detailed hyperparameters are provided, so an independent researcher cannot reproduce or verify the comparison. Please make the benchmark reproducible and include at least one independent baseline not affiliated with the authors, or clearly frame these results as self-reported demonstrations.
  3. [Section 1.3 and Abstract] The claim that 'a comprehensive and systematic review covering both UVE and underwater 3D reconstruction remains absent' is not supported by a comparison with existing surveys, such as Anwar and Li (2020) for underwater image enhancement or Diamanti and Ødegård (2024) for marine 3D documentation. Please add a survey-comparison table or qualify the novelty claim.
  4. [Section 5 and Table 7] Table 7 shows that public underwater 3D datasets are scarce and small, and the headline comparison in Figure 19 uses only two scenes. The paper's own observation that current public data are limited means the external validity of any ranking from this benchmark is weak. Please state this limitation explicitly in Section 5 and temper the conclusion accordingly.
minor comments (7)
  1. [Section 3.1.1] The sentence 'CLAHE is a typical baseline in this category' appears twice; please delete one occurrence.
  2. [Sections 1 and 2.4] Section 1 contains an unfinished sentence, 'underwater exploration and analysis remain hampered. .', and Section 2.4 has the typo 'serval works' for 'several works'.
  3. [Section 3.4] The attribution of the UIQM metric to Wang et al. (2021a) is inaccurate; UIQM was introduced by Panetta et al. and should be cited to its original source.
  4. [Table 3] Table 3 uses reference placeholders such as 'Multi-Exposure Fusion (?)' and 'Hybrid Dehazing + White Balance (?)'; these should be replaced with actual citations.
  5. [Figure 10 caption] In the caption, 'UCDP+HE' should read 'UDCP+HE' for consistency with the main text.
  6. [Sections 5.1-5.3] Terminology is inconsistent: 'InstanceNGP' and 'Instant-NGP' are both used, and Figure 18 refers to 'UW-3DGS' while the text and Figure 19 use 'UW-GS'; please unify the names.
  7. [Section 5.3, Figure 19] The note that the images in the first row of Figure 19 'have been enhanced for better visibility' is ambiguous; please clarify whether this enhancement is part of the compared pipeline or only for presentation, since it directly affects the fair comparison of the shown methods.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the survey content is independent, and Section 5's self-authored qualitative benchmark is an evidentiary limitation rather than a definitional or fitted-input circularity.

full rationale

The paper's actual derivation chain is confined to Section 2, where Eq. (7) is explicitly obtained from the Jaffe-McGlamery model by neglecting forward scattering, assuming a constant backscatter phase function, and integrating the simplified backscatter term; this is a stated approximation, not a result equivalent to its input. Sections 3 and 4 are literature surveys of external works, so no target claim is used to define its own evidence. The only self-referential element is Section 5: the two-stage pipeline uses 'the algorithm described in (Huang et al., 2025)' and the integrated comparison concludes 'Overall, UW-GS appears to be the best' from Figure 19, where UW-GS is the same group's method (Wang et al., 2025) and one test scene (Reef) comes from S-UW, the same work. This is a self-evaluation with no quantitative metrics, and it conflicts with the abstract's promise of 'quantitative and qualitative evaluations.' However, these are problems of evidence strength and independence, not circularity: no parameter is fitted to data and then renamed as a prediction, and no equation or definition makes the conclusion equivalent to its input. The paper itself concedes the limitation: 'publicly accessible underwater 3D scene datasets are quite rare... potentially limiting the thoroughness of evaluations for reconstruction methods.' Therefore no circular step is exhibited; the weakness belongs to correctness and rigor rather than circularity.

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

The paper introduces no new physical quantities or fitted parameters. Its dependencies are the accepted image formation models and the reliability of the cited literature. The only quantitative elements are the illustrative benchmark runs, which use off-the-shelf or prior-art methods without releasing code.

assumptions (3)
  • domain assumption The Beer-Lambert law and the Jaffe-McGlammery image formation model accurately describe underwater image degradation.
    Section 2 uses these models as the physical foundation for the entire taxonomy of enhancement and reconstruction methods.
  • domain assumption Simplified image formation models, with constant attenuation and ambient light, remain valid for moderate clarity and shallow underwater scenes.
    Section 2.4 derives Eq. (7) and Eq. (8) under homogeneity assumptions, and the benchmarked methods such as SeaThru-NeRF incorporate these simplifications.
  • domain assumption The results reported by the survey's cited papers are accurate and correctly represented.
    The review synthesizes claims from many external papers without re-running or independently verifying each experiment, which is standard for a survey but a load-bearing premise for its usefulness.

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Cite this review

Pith. "Pith review of Visual enhancement and 3D representation for underwater scenes: a review." pith.science (2026). https://pith.science/paper/NL5MFASL

@misc{pith2026250501869,
  author       = {Pith},
  title        = {Pith review of: Visual enhancement and 3D representation for underwater scenes: a review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NL5MFASL}},
  note         = {Machine review of arXiv:2505.01869}
}
read the original abstract

Underwater visual enhancement (UVE) and underwater 3D reconstruction pose significant challenges in computer vision and AI-based tasks due to complex imaging conditions in aquatic environments. Despite the development of numerous enhancement algorithms, a comprehensive and systematic review covering both UVE and underwater 3D reconstruction remains absent. To advance research in these areas, we present an in-depth review from multiple perspectives. First, we introduce the fundamental physical models, highlighting the peculiarities that challenge conventional techniques. We survey advanced methods for visual enhancement and 3D reconstruction specifically designed for underwater scenarios. The paper assesses various approaches from non-learning methods to advanced data-driven techniques, including Neural Radiance Fields and 3D Gaussian Splatting, discussing their effectiveness in handling underwater distortions. Finally, we conduct both quantitative and qualitative evaluations of state-of-the-art UVE and underwater 3D reconstruction algorithms across multiple benchmark datasets. Finally, we highlight key research directions for future advancements in underwater vision.

Figures

Figures reproduced from arXiv: 2505.01869 by the authors.

Figure 1
Figure 1. Examples of underwater images exhibiting wavelength-dependent color casts and veiling effects ( [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Example underwater images with non-uniform lighting: the top row shows images from the UIEB [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Examples of underwater images with dynamic illumination conditions ( [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: Examples of underwater images with marine snow ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Jaffe–McGlamery underwater IFM, depicting light absorption and the selective attenuation of underwater [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Taxonomy of selected key underwater image and video enhancement papers into various categories, [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Comparison of different histogram equalization techniques applied to the underwater image. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Enhanced underwater images using MIP (Carlevaris-Bianco et al., 2010). Images (a) and (c) are the input images with and without red color, respectively, while images (b) and (d) are their corresponding enhanced results. MIP fails in images with blue-green dominant ligh…
Figure 9
Figure 9. Figure 9: Illustration of the integrated framework proposed by [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Comparative results of various underwater image enhancement methods. (a) Original images, (b) [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: Illustration of NeRF (Mildenhall et al., 2020) and its differentiable rendering process. It involves sampling 5D coordinates (position and direction) along camera rays (a), using an MLP to produce color and density (b), and rendering these into an image (c). The diffe…
Figure 12
Figure 12. Figure 12: Result of K-Planes, a dynamic NeRF method proposed by [PITH_FULL_IMAGE:figures/full_fig_p030_12.png]
Figure 13
Figure 13. Figure 13: Visualization of input images (a) and their corresponding depth map (b) and normal map estimated with [PITH_FULL_IMAGE:figures/full_fig_p036_13.png]
Figure 14
Figure 14. Figure 14: Snapshots of the sparse point cloud (a), dense point cloud (b), Poisson surface reconstruction (c) and [PITH_FULL_IMAGE:figures/full_fig_p037_14.png]
Figure 15
Figure 15. Figure 15: Snapshots of reconstructed underwater scene using InstanceNGP ( [PITH_FULL_IMAGE:figures/full_fig_p038_15.png]
Figure 16
Figure 16. Figure 16: Input images before and after enhancement. [PITH_FULL_IMAGE:figures/full_fig_p039_16.png]
Figure 17
Figure 17. Figure 17: Snapshots of the reconstructed underwater scene using InstanceNGP, with image enhancement using the [PITH_FULL_IMAGE:figures/full_fig_p039_17.png]
Figure 18
Figure 18. Figure 18: Visualization of rendered images (top) and the estimated clean images (bottom) using UW-3DGS ( [PITH_FULL_IMAGE:figures/full_fig_p040_18.png]
Figure 19
Figure 19. Figure 19: Novel view rendering comparison for Panama from the Seathru-NeRF dataset ( [PITH_FULL_IMAGE:figures/full_fig_p041_19.png]

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.