Title: Plenodium: Underwater 3D Scene Reconstruction with Plenoptic Medium Representation
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Gaussian Splatting

Underwater Imaging

Novel View Synthesis

Spherical Harmonics

Depth Prior

and Underwater Image Restoration.
Abstract: We present *Plenodium* (*plenoptic medium*),  an effective and efficient 3D representation framework capable of jointly modeling both objects and the participating medium.
In contrast to existing medium representations that rely solely on view-dependent modeling, our novel plenoptic medium representation incorporates both directional and positional information through spherical harmonics encoding, enabling highly accurate underwater scene reconstruction.
To address the initialization challenge in degraded underwater environments, we propose the pseudo-depth Gaussian complementation to augment COLMAP-derived point clouds with robust depth priors.
In addition, a depth ranking regularized loss is developed to optimize the geometry of the scene and improve the ordinal consistency of the depth maps.
Extensive experiments on real-world underwater datasets demonstrate that our method achieves significant improvements in 3D reconstruction.
Furthermore, we construct a simulated dataset with GT and the controllable scattering medium to demonstrate the restoration capability of our method in underwater scenarios.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel-view synthesis method for participating media based on 3D Gaussian Splatting (3DGS). The primary contribution lies in modeling spatially-varying and directionally-dependent scattering coefficients using spherical harmonics (SH) encoding (Plenodium), which enables the representation of heterogeneous participating media. Compared to previous MLP-based approaches for participating media, Plenodium achieves superior scene reconstruction quality and faster rendering speed.\n\nSTRENGTHS AND WEAKNESSES:\n[Strengths]\n\n- The main technical contribution lies in leveraging spherical harmonics (SH) encoding without relying on MLPs to model spatially-varying and directionally-dependent scattering effects in heterogeneous participating media. This design choice is simple yet well-motivated, given the established effectiveness of SH representations in prior works such as Plenoxels [CVPR’22] and TensoRF [ECCV’22]. (Originality, Quality)\n- The proposed method outperforms SeaThru-NeRF and WaterSplatting--state-of-the-art approaches for underwater novel-view synthesis--in both reconstruction quality and rendering efficiency. Since underwater imaging remains a challenging, these results may encourage further research and adoption in real-world aquatic vision systems.\n(Quality, Significance)\n\n[Weaknesses]\n- The primary weakness of the paper lies in the lack of detailed analysis to support its central motivation. While the use of spatially-varying SH encoding is intended to represent heterogeneity in participating media, the evaluation focuses solely on novel-view quality. Prior studies have already demonstrated that SH encoding improves scene representation compared to MLPs in natural scenes. As a result, it remains unclear whether the improvements are indeed due to modeling heterogeneous media or merely the general advantages of SH. To support the motivation more convincingly, further experiments--such as synthesizing clearly heterogeneous media (the current simulation dataset appears to depict homogeneous media) and visualizing the learned SH coefficients--would be valuable. (Quality, Originality)\n- The proposed pseudo-depth Gaussian Complementation (PDGC) in Sec. 4.2 is technically unclear. As shown in Fig. 2, PDGC initially increases Gaussian primitives derived from COLMAP based on monocular depth from DepthAnything. However, to compensate for the scale ambiguity, the scene depth should be computed as in Eq. (8), which requires Gaussian primitives with learned depth and opacity--not merely initial estimates--contradicting the inputs illustrated in Fig. 2. If the proposed method requires initial Gaussian estimates, does the reported training time include the entire training process? Additionally, the expression “COLMAP-initialized Gaussian primitives” (e.g., Line 69) is confusing. I recommend using “Gaussian primitives estimated from COLMAP-initialized point clouds.” It is also unclear what actual parameter values are assigned to the Gaussians inserted by PDGC. (Clarity, Quality)\n- Although the paper introduces PDGC and a depth-ranking loss, their novelty appears limited. The issue of insufficient initial SfM points has been addressed in the context of sparse-view reconstruction; to claim technical novelty, comparisons with existing approaches such as the graph-based densification in FSGS [ECCV’24] are necessary. Additionally, the proposed depth-ranking loss is nearly identical to that used in SparseNeRF [ICCV’23]. (Originality, Significance)\n- Minor errors\n    - Line 128 refers to (x,y,z) as the observer’s spatial coordinates, while Line 134 uses it to denote the backscatter position.\n    - In Fig. 3, “Depth Any Model” should be corrected to “Depth Anything Model.”\n\nQUESTIONS:\n- I suggest visualizing the learned SH coefficients, especially on synthesized heterogeneous media, to strengthen the claim that the method effectively models heterogeneous participating media.\n- Please clarify the input to PDGC. Does it require initial Gaussian primitives to be estimated? If so, does the reported training time include the time needed for this initial estimation?\n- If there are any novel aspects or design elements specific to underwater scenarios in PDGC or the proposed depth-ranking loss compared to prior densification techniques for sparse-view scenarios or existing depth-ranking losses (e.g., in FSGS and SparseNeRF), please clarify them. If not, I recommend refraining from presenting them as technical contributions (e.g, Line 67: \"To improve ..., we introduce a pseudo-depth Gaussian complementation ... and a depth ranking loss ...\").\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel view synthesis algorithm for underwater scenes. Compared to prior work, it uses a grid-based spherical harmonic spatial discretization (as opposed to an implicit neural network) to store medium information, encodes positional as well as directional dependences, and improves the initialization process. It is shown to improve or match the reconstruction accuracy of previously existing methods while cutting the runtime by a significant amount.\n\nSTRENGTHS AND WEAKNESSES:\nIt seems to be like the main strength of the paper is the spherical harmonic discretization of the medium parameters, as well as the performance improvements that result from it. Together with the produced dataset, I am confident that this work is an interesting contribution to this research problem that may inspire future work. In general, I think exploring differentiable discretizations different from implicit MLPs that encode problem priors to improve efficiency and accuracy is a worthwhile pursuit in many applications, and I am encouraged to see it applied here. It is possible that the initialization strategy is a fundamental novel contribution as well; unfortunately, I was not able to understand it very well (see “Questions”).\n\nThe main weakness of this manuscript is in its comparison to WaterSplatting, which has similar runtime requirements and very similar performance (in Table 1, one often has to go to the fourth significant digit to see an accuracy difference). There is a chance I misunderstood this and the result in Table 1 is of WaterSplatting combined with the SH discretization (Sec 7 says “a 47% speedup relative to WaterSplatting”, but it seems to me like this refers to rendering only, not training. Could one train WaterSplatting and then save the information on a grid to render efficiently?), and I will gladly listen to the authors’ response to see if this is the case.\n\nQUESTIONS:\n- As someone who has not worked on this problem before, I was surprised to hear that that scattering effects were dependent on the viewing position as well as the direction (Sec. 1 and 4.1). I do not doubt that this is the case, but I would love to see (here and in the paper) a citation justifying this!\n- I would have appreciated more evaluation of the grid discretization. What is the grid size used in the results? Is it always a regular grid? In cases where the medium changes drastically and irregularly, can this method suffer due to not being able to concentrate resolutive power the same way that an MLP can?\n- This is probably my own limitation, but I was not able to follow Sec. 4.2. If this is about how the Gaussians are initialized, what does it mean to “render the pixel-wise depth”? If we are in the initialization step, isn’t alpha_i and z_i not defined yet? Also, if \\tilde{D} is estimated using Depth Anything, why does it depend on (x,y) in Eq. 10? I am sure I am missing something obvious, but I would really appreciate if this section was rewritten to be easier to follow.\n- In Fig. 1, what is the difference between reconstruction and restoration? Is one 3D and the other 2D? The figure is otherwise clear and a very helpful visualization, but those two words confused me a bit.\n\nLIMITATIONS:\nSee “weaknesses”. Strangely, limitations are not discussed anywhere in the manuscript.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript presents an underwater 3D scene reconstruction method that models objects using a 3D Gaussian Splatting (3DGS) representation, while encoding view-dependent scattering properties of the medium on a voxel grid as opposed to WaterSplatting which represents the medium as an MLP. This grid is interpolated to enable continuous estimation of the medium’s spatio-angular optical characteristics. To initialize the 3DGS representation, the method leverages both COLMAP and monocular depth estimation techniques. The manuscript claims all these tricks enable faster and more accurate reconstructions compared to state-of-the-art methods such as SeaThru-NeRF and WaterSplatting. However, the fairness of the comparisons is questionable.\n\nSTRENGTHS AND WEAKNESSES:\n**strengths** \n\n1. The authors shared code in the supplementary. While I didn't try it out myself, I believe that they code runs fine. Assuming that the authors make the code publicly available, the technique is replicable. \n2. Quantitatively, the proposed technique is faster and results in better reconstructions.\n3. Ablation studies show that all three contributions: (i) Plenoptic medium representation (ii) monocular depth initialization (iii) depth regularized loss helps quantitatively. \n\n**weaknesses**\n1. Line 143-8: As monocular depth estimates are trained on clean images (non-underwater images), won't they get affected by degradation as well? So, I am unsure if it is fair to state that degradation affects colmap but not this technique. Instead, I guess that more independent estimates (one from colmap and another from depth priors) is helping this technique. As such, if one were to try this on non-underwater (non-degraded images), they probably will have better initialization and hence, better results as well. Not a negative point to the manuscript but the claim is probably not accurate and can be more generic. \n2. Eq. 9: Are these k and b voxel or Gaussian dependent? Given that underwater degradation varies from depth plane to depth plane, does it make sense to have this correction for each depth plane? If k and b are global, what are typical k and b values. Are they significantly different from 1 and 0?\n3. Fig 3: The depth map comparison is super unfair. Of course the proposed technique will result in better results given that the proposed technique uses depth anything for initialization, it has scaled it and the manuscript is using the same model for validation.A fairer comparison is to do an affine alignment between GT and the past methods (assuming that k and b are constant for all depths for Plenodium) and see if the error is just in affine alignment\n4. Fig 4: \"Plenodium generates results with more reasonable exposure and accurate colors.\"  I am unsure how the manuscript is claiming that without ground truth.- Also, I don't buy that Plenodium generates better results and natural color fidelity (Line 255). There is a standard color checkerboard in this scene. One can retrieve and check if the colors are accurate. The proposed Plenodium actually has poorly retrieved colors in the color checkerboard. \n5. Line 227-228: how is it simulated? Is multipath considered? If not there is no model mismatch and of course this technique works the best. \n6. In Fig 5. what does \"easy-->hard\" mean? Poor illumination? Is Poisson noise added when the illumination is reduced. \nLine 256-259: can't we do simple color and exposure correction before comparing? Also white balancing. \n\ntypos: Line 260: \"Additionally, it and retains\" --> \"Additionally, it retains\"\n\nQUESTIONS:\nPlease answer the questions asked in the weakness section.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe work proposes to reconstruct 3D-GS representation from multi-view underwater images. Different from prior work, which usually exploit MLP to represent the medium, they exploit spherical harmonics encoding for better performance. The new medium representation incorporates both directional and positional information for more effective and efficient 3D representations, compared to MLP. They also exploit pseudo-depth Gaussian complementation for more robust depth priors. The experimental results demonstrate improved performance compared to prior baselines.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths ## \n1. The paper is well written and easy to follow. \n2. The proposed method is rational and the experimental results demonstrate its effectiveness.\n\n## Weaknesses ##\n1. Parts of the technical details can be further improved:\n* the paper mentions that they exploit a medium volume field for SH coefficient interpolation, what is the size of the grid? It is selected based on which criteria? \n* eq (12), why the denominator is N^4, how many i, j pairs are chosen? \n* eq (11), what is the motivation to emphasize dark regions during optimization? Could the authors provide any ablation evaluations on its effectiveness? \n\n2. eq (12), the manuscript mentions that they downsampled the depth image to 16x16 pixels low-resolution from 512x512/900x1400 pixels. The reviewer wonders the necessity of the depth loss. It is also verified from the ablation studies, i.e. the loss only improves the PSNR by 0.099 dB, 0.124 dB, 0.111 dB and 0.001 dB, which could be considered a very marginal improvements since you might also get similar performance by repeat the training several times.  \n\n3. Line 2: participating media -> participating medium;\n\nQUESTIONS:\nPlease see weakness section.\n\nLIMITATIONS:\nDoes the proposed method could also be applied to other similar problems, such as dehaze etc.?", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}