Title: Fix False Transparency by Noise Guided Splatting
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Gaussian Splatting

Computer Graphics
Abstract: Opaque objects reconstructed by 3D Gaussian Splatting (3DGS) often exhibit a falsely transparent surface, leading to inconsistent background and internal patterns under camera motion in interactive viewing.
This issue stems from the ill-posed optimization in 3DGS. During training, background and foreground Gaussians are blended via $\alpha$-compositing and optimized solely against the input RGB images using a photometric loss. 
As this process lacks an explicit constraint on surface opacity, the optimization may incorrectly assign transparency to opaque regions, resulting in view-inconsistent and falsely transparent output.
This issue is difficult to detect in standard evaluation settings (i.e., rendering static images), but becomes particularly evident in object-centric reconstructions under interactive viewing.
Although other causes of view-inconsistency, such as popping artifacts, have been explored previously, false transparency has not been explicitly identified. 
To the best of our knowledge, we are the first to quantify, characterize, and develop solutions for this "false transparency" artifact, an under-reported artifact in 3DGS.
Our strategy, Noise Guided Splatting (NGS), encourages surface Gaussians to adopt higher opacity by injecting opaque noise Gaussians in the object volume during training, requiring only minimal modifications to the existing splatting process.
To quantitatively evaluate false transparency in static renderings, we propose a novel transmittance-based metric that measures the severity of this artifact. 
In addition, we introduce a customized, high-quality object-centric scan dataset exhibiting pronounced transparency issues, and we augment popular existing datasets (e.g., DTU) with complementary infill noise specifically designed to assess the robustness of 3D reconstruction methods to false transparency.
Experiments across multiple datasets show that NGS substantially reduces false transparency while maintaining competitive performance on standard rendering metrics (e.g., PSNR), demonstrating its overall effectiveness.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the false transparency problem in 3D Gaussian Splatting by proposing a method called Noise Guided Splatting (NGS). The approach introduces (1) an alpha-consistency loss to suppress background opacity and (2) random noise Gaussians injected beneath the object’s surface during training to guide opacity optimization. Additionally, the paper presents a novel evaluation method for detecting transparency artifacts—often hard to perceive in static images—by infilling the interior with high-saturation colors for visualization and quantification.\n\nSTRENGTHS AND WEAKNESSES:\n## Strength: \n- **Quantitative evaluation of false transparency** The paper introduces a valuable method for visualizing and quantifying false transparency artifacts, which are difficult to detect in static renderings. Using high-saturation infill colors provides a clear and intuitive way to expose these issues during evaluation.\n\n## Weakness:\n- **Fragmented content structure:** The paper is difficult to follow due to its fragmented presentation. For example, Section 3.3 on Noise Guided Splatting (NGS) is divided into multiple loosely connected subsections. Some, such as the “Infill” subsection, focus on failed boundary estimation methods, which add little value. A more cohesive and step-by-step description of the full NGS pipeline would improve clarity.\n\n- **Incomplete method description:** Key details are missing from the main method section. For instance, the learning rate reset is never introduced as part of the core pipeline, yet the ablation study later reveals it is essential for the method’s score improvement. Important training mechanisms should be clearly described up front in the method section.\n\n- **Lack of distinction from floater artifacts:** The false transparency artifact appears closely related to the floater artifact. However, the paper does not clearly distinguish the two or cite recent related work on the floater (e.g., StableGS (arXiv 25.03), Optimize the Unseen (arXiv 24.12), Nerfbusters (ICCV 2023)). Without this clarification, the originality and scope of the contribution remain ambiguous.\n\n- **Significance concerns**:  The method is limited to object-centric scenarios, which reduces its general applicability. Similar works on the floater artifact are compatible with general scenes, making them potentially more attractive to researchers seeking broader solutions.\n\nQUESTIONS:\n## 1. Is your false transparency problem the same as the floater artifact?\nThe false transparency issue described in your paper appears similar to the floater artifact, where surfaces are reconstructed at incorrect depths. Could you clarify whether your definition of false transparency is fundamentally different from floaters? If they are the same or closely related, please explain why your method should be considered novel in this context.\n\n## 2. Could you re-express your method as an algorithm?\nThe current presentation is somewhat difficult to follow. A concise “Algorithm” box or pseudocode format would help readers clearly understand the overall training pipeline.\nTo confirm my understanding, here's my interpretation of your pipeline. Please correct or confirm:\n\n```\nFor iteration in range(30000):\n\tIf iteration == 6000: \n\t\tcompute_convex_hull()\n\t\tprune_gaussians_not_farthest_in_depth()\n\tIf iteration > 6000:\n\t\tassign_random_noise_color(RGBCMY)\n\tIf iteration == 15k:\n\t\t_3DGS.require_grad = False\n\t\t_3DGS.opacity.require_grad = True \n\tUpdate_3DGS()\n```\nIs this an accurate reflection of your training process?\n\n ## 3. Is the pruning method potentially flawed?\nFrom your description, it seems that pruning is done by retaining only the farthest Gaussian along each ray during depth testing. Wouldn’t this incorrectly remove valid front-surface points while keeping back-facing or internal points? \n\nFor example, given the scene setup as shown in [the picture](https://i.ibb.co/W47Sb3v0/image.png) that we try to reconstruct a scene of a ball. Let's say you try to prune the image of the ball. The ray hit 3 times. The floater, the front of the ball (close to the camera), and the back of the ball (far from the camera). But you only keep the farthest depth. The floater (shown as orange) will be removed as per your intention. But the surface on the front of the ball (shown as red) will be removed as well, and keep the back of the ball (shown as green), but in this case, you want to keep red.\n\n![Image for question 3](https://i.ibb.co/W47Sb3v0/image.png)\n\nCould you clarify or justify this pruning strategy?\n\n## 4.  Is the Surface Opacity Score (SOS) perceptually aligned?\n\nCould you provide visual comparisons illustrating what different SOS values (e.g., 0.95 vs 1.0) look like? In your ablation study, it seems that you favor a lower SOS (e.g., 0.95) over a perfect 1.0, which seems counterintuitive.\n\nAdditionally, since your method allows injecting infill into other models as shown in Figure 5, SOS can be applied to all baselines. Therefore, it would be helpful to include the SOS scores for all methods in Table 1 for a fairer comparison.\n\n## 5.  Why limit noise colors to RGBCMY?\nWhy are noise Gaussians assigned only one of six fixed colors (Red, Green, Blue, Cyan, Magenta, Yellow) instead of using fully random RGB values? If this design choice is intentional, please explain the reason behind the design. It would also be helpful to include an experiment comparing RGBCMY vs. random RGB to demonstrate its impact, if any.\n## 6. How is the 360° object case handled, given the limitations of alpha-consistency loss?\nYou mention that the alpha-consistency loss has limitations when applied to full 360° captures. However, both DTU and OmniObject3D are 360° datasets. Do you apply any adjustments or additional constraints in these cases? If not, how do you mitigate the failure of the loss in such scenarios?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper spotlights a “false-transparency” artefact in 3DGS that causes opaque surfaces to appear semi-transparent when front- and back-layer Gaussians blend. It proposes Noise-Guided Splatting (NGS), which injects interior noise Gaussians so the optimiser is forced to sharpen surface opacity. A new Surface Opacity Score (SOS) and the stone dataset are introduced to quantify improvements. NGS roughly doubles SOS while keeping PSNR/SSIM stable.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. The paper tackles a genuine and under-explored artifact, spurious transparency, in 3DGS, giving the topic practical relevance.\n2. The proposed Noise-Guided Splatting (NGS) maintains parity with baselines on standard image metrics while markedly outperforming them on a dedicated transparency score.\n\n### Weaknesses\n1. The solution is essentially an empirical “noise fill-in” heuristic; the manuscript offers no theoretical rationale or analysis to explain why it should eliminate false transparency or under what conditions it might fail.\n2. NGS yields only parity on NVS quality, yet incurs ~50% extra memory and additional training time. The authors argue that better opacity should benefit novel-view synthesis and reconstruction, but provide no downstream reconstruction metrics to substantiate this claim.\n3. The newly introduced “Stone” dataset consists of niche, single-material objects, limiting its representativeness; thus, the generality of the transparency findings remains unclear.\n\nQUESTIONS:\n1. Please provide quantitative evidence that eliminating false transparency truly improves 3D reconstruction. \n2. The current draft convincingly shows that Noise-Guided Splatting raises opacity, but it remains unclear whether this benefit translates into a leaner and more surface-aligned Gaussian representation. I strongly encourage you to include an explicit Gaussian-count vs. quality analysis. If NGS achieves the same reconstruction quality with fewer Gaussians (excluding noisy Gaussians), this would demonstrate a tangible efficiency upside.\n3. What is the metric \"FTS\" in Table 1? It's not mentioned in the paper.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the problem of false transparency artifacts in 3D Gaussian Splatting, where opaque surfaces appear incorrectly semi-transparent due to ill-posed alpha blending. A method called Noise Guided Splatting (NGS) is proposed to solve the problem. NGS injects internal noise Gaussians during training to enforce correct surface opacity. The paper further introduces a transmittance-based metric and a new diagnostic dataset. Experiments on multiple object-centric datasets demonstrate that NGS can effectively reduce false transparency without degrading rendering quality.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe paper presents a practical and easy-to-implement approach to mitigate false transparency artifacts in 3D Gaussian Splatting. The method can be integrated seamlessly into existing pipelines.  It is systematically evaluated with the help of a new dataset. The introduction of a quantitative metric and dataset also provides a useful tool for analyzing the artifact in future research.\n\n\nWeaknesses:\n\nThe main issue with the paper is that the authors do not clearly identify or exemplify the false transparency problem before introducing their solution. Although they point out that the problem is more apparent in dynamic renderings, the paper does not provide clear dynamic or temporal results to support the claim. The effectiveness of the proposed method on standard datasets is not convincingly demonstrated and lacks comparison with strong baselines.\n\nQUESTIONS:\n(1). I am curious about how much the reported false transparency issue affects rendering quality, especially in interactive or dynamic scenarios. The problem of false transparency should be defined more precisely, with clearer examples illustrating how it degrades rendering quality in static and interactive scenes.\n\n(2). Additional qualitative visualizations on standard open datasets (e.g., DTU and OmniObject3D) would better support the claims and show the method’s effectiveness.\n\n(3). The authors should clarify whether the injected noise infill would introduce new artifacts or unintended occlusion, and how the method avoids damaging fine surface details.\n\n(4). The generalization of the proposed method to more complex scenes should be discussed, especially regarding how the noise infill is initialized and managed in such cases. Current result visualization only focuses on single object-centric scenarios.\n\n(5). There are some typos and figure errors that need to be corrected.\n\nLIMITATIONS:\nThe paper has discussed the main limitations of the proposed method.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper provides a plug and play solution to mistakes in transparency estimation of gaussian splatting in an object oriented setup. 
Given a segmentation mask of the object surface, the method proposes to add noisy opaque gaussians inside the object to break the line of sight between different viewpoints and force the optimization to focus on the surface.
For the initialization they run a convex hull mesh algorithm on the initial gaussians and the prune it based on the depth ordering of the gaussians. 
They provide a method for better evaluation of the transparency issue in 3dgs by doing green infill and show the noise guided splatting improves significantly upon the baselines and similar methods.

STRENGTHS AND WEAKNESSES:
Strengths
The method is novel with a small amount of overhead and easily applicable to different gaussian splatting methods. 
The experiments are conclusive over 3 datasets and ablations cover the different elements of the method.
The paper is well written.

Weaknesses:
Based on the ablation it seems that pruning is not helpful so the justification for keeping it is unclear.

QUESTIONS:
Why is the pruning kept in the method?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03