Fix False Transparency by Noise Guided Splatting
Authors: Aly El Hakie
Yiren Lu
Yu Yin
Michael W. Jenkins
Yehe Liu
Venue: NeurIPS 2025 poster ()
Keywords: 3D Gaussian Splatting
Computer Graphics
Primary area: applications
TL;DR: We identify and solve the "false transparency" problem in 3D Gaussian Splatting by injecting opaque noise Gaussians, reducing surface transmittance while maintaining rendering quality.

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.

Fix False Transparency by Noise Guided Splatting
Aly El Hakie';
Yiren Lu Yu Yin", Michael Jenkins 1.2
Yehe Liu
OOssiClear LLC
Case Western Reserve University [aly, y clear............ com [yiren. lu, yu. mw aw]5] edu
https://opsicli github. 1o/ngs/
Abstract
Opaque objects reconstructed by 3D Gaussian Splatting (DGSS) often exhibit 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 a-compositing and optimized solely against the input RGB images using photometric loss. As this process lacks an explicit constraint on surface opacity, the optimization may incorrectly assign transparency opaque regions, resulting in iew-inconsistent and falsely transparent output. This issue is difficult 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 our knowledge, we are the first to quantify, characterize, and develop solu- tions for this "false transparency" artifact, an under-reported artifact in (DGSS) 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 novel transmittance- baseed metric that measures the severity of this artifact. In addition, introduce customized, high-quality object-centric hibiting pronounced transparency issues, and we augment popular existing datasets (e.g., DTU) with complementary infill noise specifically designed assess the robustness of 3D reconstruction methods to false transparency. Experiments across multiple datasets show that INSS substantially reduces false transparency while maintaining competitive performance on standard rendering metrics (e.g., PSNR), demonstrating its overall effectiveness.
dataset
Introduction
3D Gaussian Splatting 3DGS 1 is emerging neural rendering technique offering unprecedented real-time performance and fidelity through explicit scene representation. However, due to its un- constrained optimization and the a-blending process, opaque surface Gaussians can be incorrectly learned as transparent. We define this largely unacknowledged artifact as false transparency.
False transparency causes opaque surfaces to incorrectly appear semi cemi-transparent, especially observ- able during interactive viewing and undetectable in individual frames and with standard Image Quality Assessment (QA) metrics. During camera movement, objects exhibit disturbing "see-throw "heethhough"
These authors contribute equally.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Original 3DGS
Jointly optimize w/ noise gaussians
3DGS with noise infill
Injected noise gaussions sizellocation. random color, and trainablc opacity pruning
Fixed surface gaussians during optimization
d
f
Infill recolored
Vanilla 3DGS w/ green infill
NGS trained 3DGS green infill
green
(for evaluation)
(a) Object-centric 3DGS render of a stone. (b) Noise Gaussians are introduced to the training process. (c) Only visible noise Gaussians are removed during optimization, leaving subsurface noise Gaussians filling the object. (d) Noise infill can be recolored and saved for highly transparent
Figure 1: Overview of the NGS
transparency evaluation. (e) Recolored infill inserted the 3ani regions on the surface. (f) Recolored infill does not leak through the NGS trained surface.
quality where internal and background Gaussian structures become visible through surfaces that should be opaque. These internal structures move out of alignment with the surface under changes in camera pose, creating an illusion reminiscent of frosted glass.
This false transparency artifact occurs because 3DGS is supervised primarily by 2D photometric loss between rendered and ground-truth images. Such supervision creates ambiguity regarding true surface opacity, the optimization can jointly refine foreground and inner background elements in discrete views, with their combined rendering still satisfying the 2D constraints. is most commonly observed in regions lacking adequate visual cues differentiate between opaque surface and a semi-trans transparntp surface backed by another surface, specifically in areas presenting low texture, repeating patterns, specular highlights, geometric complexity.
beCause3DGS)
The problem also more pronounced in object-centric reconstruction. scene-level reconstruction, many objects are not imaged from all angles and lack information about their posterior surfaces, resulting in less ambiguity. However, in 360 object-centric settings, the mean depth between opposing surfaces is small. At every angle, front-facing surface of the object is paired and jointly optimized with back surface along the same ray. Beyond visual artifacts, false transparency affects downstream applications such surface extraction, physics simulations, and volumetric analysis. Many of such applications rely on accurate opacity to delineate object boundaries. An ambiguous alpha causes invalid unreliable results.
In practice, evaluating the severity of this false transparency challenging during both training and post-training analysis, as conventional metrics generally rely comparing static renderings with a 2D reference. Some recent studies 2 5, 6 have indirectly mitigated this issue. Although these studies advanced approximation techniques in the splatting process, for example by refining depth-ordering and a-blending, their main goal was to improve view-consistency in iDCS As such, they did not directly address the underlying mechanisms responsible for false transparency.
This paper introduces Noise Guided Splatting NGGS (Fig. D. novel strategy to address the opacity ambiguity during optimization by injecting persistent internal noise Gaussian structures within an object's volume, effectively enforcing surface opacity. NGS allocates high-opacity noise Gaussians with continuously randomized coloration within the object's volume. The infill creates an effective occlusion barrier between opposing surfaces, preventing the optimization process from integrating back surfaces into the front-facine rendering. Additionally, the noise points be extracted and

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used as a diagnostic tool to support evaluating false transparency when assessing any splatting-base rendering methods.
In summary, we make the following contributions:
1. A new technique that places interior noise Gaussians to distinguish between the interior and exterior space the rendered object, guiding optimization toward proper surface opacity. The method is plug-and-play and requires minimal modifications to existing frameworks.
2. A new approach to visualize and quantify the false transparency in static 3DGS renderings.
3. A noise Gaussian infill dataset, including infill add-ons to existing datasets (e.g.. DTU) and a customized high-resolution object-centric scan dataset, to facilitate benchmarking.
Related works
through neural implicit representations for 3D scenes, achieving high visual quality. However, NeRFis computational demands led to the development of faster alternatives like sparse voxel grids { and hash encoding [9]. 3DGS [T] marked a major advancement through explicit trainable primitives,
Novel View Synthesis NVS Neural Radiance Fields NeRF
revolutionized NVS
enabling real-time rendering with high fidelity. Building upon 3DGS) numerous works have enhanced its capabilities in numerous directions, including more efficient training strategies [TO] M. better
densification heuristics
[2] anti-aliasing
3
and reduced dependency on initialization
NVSJartifacts There are several well recognized NVS artifacts that should not be confused with false transparency artifacts. A common one is floater artifacts, which manifest sparse features reconstructed at incorrect depths above the surface [1 [6]. These artifacts do not appear in training be mitigated using depth consistency constraints [1.5] 17] and specialized priors [16, 18] 19]. Another category is view-inconsistency artifacts, which cause surfaces to exhibit unnatural changes during viewpoint transitions. A well- known example is the 'popping artifact' [6]. caused by sorting discontinuities between adjacent views. Hierarchical sorting [6], order-independent transparency (2). anti-aliasing filtering 4. and hybrid transparency S have been introduced to address these problems. Finally,
views but become obvious in novel views. Floaters
InDG BDGS also suffers from poor reconstruction of certain details, which has been addressed using specialized loss functions 20 and diffusion- based post-processing enhancements (21.
ensuring rendering fidelity and smooth transitions across viewing angles. Most research on 3DGS transparency focuses accurately reconstructing inherently transparent objects [22 23 However, the phe-
Transparency. Blending semi-transparent primitives is essential feature of 3DGS
nomenon of false transparency, where surfaces intended to be opaque incorrectly appear transparent, remains largely unexplored the literature. This oversight is significant because false transparency contributes substantially to view inconsistency artifacts through different mechanism from popping.
Object-centric reconstruction. Scene reconstructions mostly reconstruct the front side of some objects. contrast, object-centric techniques scan around the target object, and often have the object
isolated from the scene. Some methods use object masks for targeted reconstruction 25 26 27),
while others employ semantic segmentation or prompt-based interaction 288 30
31 These
advancements have enabled object-aware representations, manipulation
32
33 and improved 3D)
asset editing (34 35). However, accurately defining object boundaries in complex scenes with
occlusions, transparencies, and intricate geometries remains challenging.
3 Theories & Methods
3.1 False-transparency mechanism
Background. In the original 3DGS| pipeline (1) each pixel value is obtained by front-to-back a-blending of the depth-sorted splats whose projection cover that pixel:
C(p)
Σ a; C; II (1-aa))
(EN(p)
jEi
where o, E 1] and c; E RR3 are the opacity and color of splat i and NV()) is the front-to-back list along the ray going through pixel p. Training minimizes purely image-space objective with

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Holes and false fransparency
Back inner surface
b
Injected noise
gaussions
Rendering combines both back inner surface
surface foroad to opacity
Front surface
Front surface gaussians
Back surface
Back inner
surface blocked
Figure 2: Mechanisms false transparency and NGS) (a) A semi-transparent front surface and inner back surface can jointly render to mimic ground-truth image, causing the false transparency artifact. (b) NGS) fills interior volume with colored noise Gaussians, promoting the surface opacity.
compound LI and SSIM loss:
Cphoto (1 (1-
Σ - + CDSSIM: P
1
(2)
with respect all Gaussian parameters O The photometric loss (Eq. (2) constrains only the final RGB value and its local structure. It is indifferent to how the color is distributed along the ray.
Where the transparency ambiguity arises. Let be the index of the first intersected surface splat and denote all deeper splats by B. Decomposing ( at s yields:
C(p) = ag Ce
(1-ag) Σ C, II (1-a)). $<j<i
(3)
Front surface
iEB
background that leaks if <1
For every semi-transparent surface (a,, < 1) there exists a set of background opacities/colors that reproduces the exact pixel color fully opaque surface, giving the same loss Eq. (2). Gradient descent therefore can accept trivial minima with low ag. In turn, the surface becomes translucent even though it should be opaque (Fig.
3.2 Object-centric reconstruction
Alpha-consistency loss. Given a pre-computed binary mask we can suppress any opacity that spills outside the object. Let P {1..... w) be the set of pixel indices, A, [0, 1] the rendered alpha and M, E {0, 1} the mask. Following [2] we define the mackground-suppresttie C, = P ΣiEp EiE A, M.), which drives A, + wherever the mask is zero.
loss
Most 3DGS) variants employ strategies that restrict opacity to reduce over-reconstruution. For instance, the original BDGS resets opacity values (I, Markov Chain Monte Carlo (MCMC) densifies the point cloud using sampling from Gaussian opacity distribution 14], and Revised Adaptive Density Control (ACC) implements explicit opacity regularization I2 All of these approaches unintentionally promote transparency in scenarios where internal background colors match object surface colors, because there no photometric incentive to maintain surface consistency when the optimization can minimize loss by simply reducing opacity. Adapting the Revised AD to the object mask yields the complementary foreground-opacity loss Lj [P] Eigr(1 A;) M,, which rewards pixels inside the mask for reaching full opacity. Summing the two gives a single alpha-consistency loss La, which is simply the L1 distance because A,, M, [0, 1]:
= Lg
Σ A, IEP
-M].
P
Limitations in 360 captures. For reconstructions from incomplete views of the object, L, cleanly separates object and background. In full 360 seans each camera ray almost always intersects with multiple surfaces. Gaussians behind the first surface are 'background' for that ray, but lie inside the mask for other views. Without an additional bias toward the front-most Gaussians, the optimizer can still converge to semi-tree ami-trrassparent surfaces.

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3DGS (ganeric workflow)
Initialization
Growth (-15k itors
Fine-tuning
SFM Inttial training
Dansity: prune & reset opacity
Stop density control & opacity reset
NGS
Noise Initiation
Noise optimization (@) -6k iter)
Noise Finalization
(as an add on)
Frezze surface points
Randomize color every iteration
Freeze noise
Add mulli-scale, subsurface noise
Train noise opacity and prune transparent noise
Continue regular training
Figure 3: 3DGS training schedule with NGS NGS
is an add-on to the standard 3DGS pipeline.
3.3 Noise Guided Splatting NGGS
NGS addresses false transparency by employing an alpha consistency loss and strategically placing noise Gaussians within the object's volume to obstruct direct lines of sight between front and back surfaces, as illustrated in Fig. [ thereby forcing the optimization to prioritize an opaque foreground (Fig. (2. The pipeline begins by initializing set of noise Gaussians within a coarse voxelized convex hull of the object. The color of the noise Gaussians are randomized in each iteration prevent overfitting. These noise Gaussians are then pruned based on depth, ensuring only noise Gaussians inside the object remain. This initialization and pruning sequence is repeated in multi-scale manner across increasing voxel resolutions to accurately fill complex geometries while remaining memory efficient. Afterwards, we conduct brief fine tuning phase where the surface Gaussians are frozen, and the noise Gaussians opacities are trained and pruned. Finally, the noise Gaussians are frozen, the surface Gaussians are unfrozen and training proceeds normally with reset learning rate.
Initialization. We first compute a convex hull mesh from the means of existing Gaussian primitives using Quickhull [36). The mesh provides an approximation of the object's volume that is then converted into coarse occupancy volume. Voxels located inside the mesh are marked as occupied. We map each occupied voxel in this coarse voxel grid sparse noise point, and set it a random color. The dense occupancy grid is always synced to the sparse point in downstream operations.
Pruning.
The convex hull approximation includes regions outside the actual object. Therefore, for each rendered pixel in each view in the capture, we identify and remove noise Gaussians that appear in front of surface Gaussians using 3DGS depth ordering [1. This carves away incorrectly placed noise Gaussians that would otherwise interfere with surface reconstruction. After depth pruning, we further eroded the occupancy grid to establish a buffer distance between the Gaussians of noise and the object surface. This guarantees a minimal thickness for the surface Gaussians to optimize without noise interference.
Multi-scale noise injection. For a balanced setup to minimize computational overhead and maximize the coverage of fine geometrical features, we initiate noise Gaussians at multiple-voxe grid resolution. The initialization is repeated in a coarse-to- fine manner, where we only initialize new noise Gaussians in occupied voxels that do not already contain noise Gaussians from previous resolution levels. This allows us to fit complex geometry and finer features while maintaining low noise count.
Fine tuning. As a final refinement step, we freeze the surface Gaussians and conduct training pass where only the opacity values of noise Gaussians are made trainable. During this phase and for the remainder of training, we randomly set the color of each noise Gaussian from Red Green Blue Cyan Magenta Yellow (RGCCMY) at each iteration. This prevents the noise opacities from overfitting to the surface's 's color, ensuring they are optimized based on occlusion alone. IRGBCMY is chosen because these six primary and secondary colors offer maximum contrast against most natural surface colors in RGB and HSV space. The opacity of incorrectly placed noise Gaussians (e.g., too close to the surface) decreases until they are removed through pruning. The fine tuning is a final step to guarantee noise Gaussians interact negatively with the surface Gaussians.
Guided Surface training. Following the fine-tunie tne-ttnnng phase, the interior noise structure is considered final. We freeze all parameters of the noise Gaussians for the remainder of training. The surface Gaussians are then unfrozen, and we reset the learning rate for their means. This reset allows the surface parameters to adapt effectively to the new optimization landscape defined by the internal noise barrier. Training then proceeds as normal, with the continued randomization of the noise Gaussians' colors preventing the surface from learning to complement static internal pattern.
5

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Use recolored noise Gaussians evallate transparency
Acquire inner Gaussians through
Recolor noise infill wi high saturation color
Recolor both infill and surface points
Figure 4: Noise Gaussian primitives from NGS as an infill to evaluate surface transmittance. (a) Generated and extracted noise Gaussians from NGS] training. (b) Recolored noise Gaussians for transparency visualization. (c) Recolored surface and noise Gaussians for higher visual contrast and quantitative analysis. (d) Noise Gaussians inserted into models trained with other methods to characterize false transparency.
3.4 Transparency benchmark
Standard quality metries (e.g,, Peak Signal-to-Noise Ratio PSNR) Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) do not quantify view inconsistency, because they are designed to work on pairs of static images. In scenarios where reconstructions exhibit false transparency. the metrics can still report high scores. Using noise Gaussian primitives created by NGS] we can create a dense point cloud placed inside the object volume that breaks the line of sight and obscures the background Gaussians during evaluation. creates a false transparency diagnostic tool to visualize and measure the false transparency.
Visualization. For transparency visualization, we simply insert the pre-trained noise into a trained BDGS) asset and render them together using the rasterizer (Fig. We can then visualize and measure the surface transparency in static renderings without interactively manipulating the 3D model to spot view inconsistency. The infill can be used to ravel transparency in the original model, or we could recolor the surface into a complementary color for better visual contrast (Fig.
Quantification. To quantify the transparency, we set the infill and surface Gaussians into two separate channels (e.g., green and red). We then render the image using the standard rasterizer, where the infill color channel the render is the surface transmittance map 7; for viewpoint i. The mean pixel values of the infill color channel are computed for all foreground pixels. If a segmentation mask M; available, we apply the mask to further refine the target area. We could also use the rendering a or recolored surface Gaussian as M; when the segmentation mask is not available (e.g., in novel views). We define results normalized in log scale as Surface Opacity Score (SOS):
SOS, log Iog M. +
(5)
log(e)
where is set IE-10 ensure numerical stability. We expect SOS; for fully opaque surfaces, and SOS; x 0 for fully transparent surfaces.
4
Experiments & results
4.1 Experimental settings
Dataset. We used public available object-centric datasets, DTU [37 and OmniOhject3D 38 to
evaluate NGS] To support research on high-resolution macro 3D scanning, we also captured a novel high-resolution dataset, herein referred to as the Stone Dataset. This dataset comprises over 100 distinct stone samples, selected for their complex geometry and surface textures, test the robustness and detail-capturing capabilities of our approach. For the Stone Dataset, we acquired 240 images per sample. Each stone was positioned on a rotating turntable within softbox. Images were captured from 6 latitudinal and 40 longitudinal angles, covering the entire upper hemisphere of the samples. Images have 3000 x 4000 pixels, and 16-bit raw Bayer data was preserved to retain maximum image information. Camera pose estimation for this custom dataset was performed using COLMAP (39]. For all datasets employed our study (DTU, OmniOhject3D, and the Stone Dataset), we also produced a high-quality foreground segmentation for each image using MVAnet [4)]. Example data

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is shown in Fig. S3 All data created for this study will be made available to the community. This release includes the Stone Dataset, foreground segmentation masks, the generated noise infills, and supplementary dataset featuring a mixture of everyday objects (Fig. SS4p.
Training. We conducted all experiments on NVIDIA L40SS GPUs using the GSplat framework [1]. Unless explicitly stated, our base implementation of NNGS used the default variant of Gsplat. Trainings typically required no more than 8GB of VRAM. Average training time for base methods (without noise) was 16 min for DTU 37 11 min for OmniObject3D 38), and 18 min for the new Stone dataset (15k adaptive density control + 15k refinement). Adding noise Gaussians increased memory consumption by about 50% due to the additional primitives required to fill the object interior, and increased training time by 1 min. If the memory consumption becomes limiting factor, reducing the number of finer noise Gaussians can substantially reduce the memory consumption.
Baselines. We conducted
transparency evaluation protocol (Sect. 3.4 on several 3DGS vari- ants, Gaussian Opacity Fields (GOF 42 and StopThePop (,, across all three datasets (DTU,
OmniObject3D. and our Stone dataset)
Benchmarking. A standard 7:1 train-test split was used for all datasets, with the test set forming the basis for all quantitative metrics. We first assessed the quality of our result and baselines using PSNR ISIM and our proposed SSOS] To analyze the perceptual impact of any surface transparency. the standard [NVS metries were re-evaluated with pre-trained, recolored noise infill included in the rendered scene. Comparing these infill-conditooned metries (denoted with an asterisk against the baseline NVS NNSS scores highlights the extent which the infill visually "leaks" " through the object's surface.
in Table. 1 e.g., (PSRR )
e.g.,PSSNR
4.2 Implementation details
NGS settings. The L. loss was added to the photometric loss from Lphoto to enforce a-consistency, Noise Gaussians were introduced at iteration 6,000 during adaptive density control, allowing sufficient time for the initial surface reconstruction to establish before applying our transparency guidance strategy (Fig. 3). We refined the noise Gaussians for 1,000 iterations. The surface Gaussians were frozen during noise refinement. After the noise refinement, the noise Gaussians' means, opacity, scale randomized
and rotations were frozen. Until the end of training, each noise Gaussian's 's color from RGBCMY at each iteration, preventing the surface Gaussians from fitting a fixed noise pattern. We reset the learning rate of Gaussian means to compensate for the sudden change to the blending. The rest of training follows the default GSplat parameters.
4.3 Object-centric 3DGS Reconstruction Evaluation
Novel view synthesis metries and limitations. NVS metrics PSNR SSIM LPIPS) effectively measure the overall visual fidelity of reconstructions but fail to specifically identify or quantify false transparency artifacts. These artifacts stem from their foreground-background ambiguity, where background elements visible through transparent surfaces contribute to the render score. This is particularly problematic for object-centric reconstruction where internal Gaussians can be optimized to match the appearance of surface Gaussians. To address this limitation, we employ our transparency evaluation protocol described in Section
Quantitative results. Our quantitative evaluation (Table [ demonstrates that NGS consistently improves surface opacity across tested methods while maintaining or slightly improving standard rendering metrics. When comparing transparency scores between baseline methods and their noise- enhanced counterparts, we observe an average reduction in surface transmittance by a factor of two, confirming the effecctiveness of our approach in addressing false transparency. Notably, the introduction of noise Gaussians does not adversely affect rendering quality as measured by standard metrics, with several cases showing modest improvements in PSNS and SSIM This suggests that by resolving the foreground bawgunddddbkkkoudddd optimization ambiguity. NGS not only reduces transparency but also helps the optimization process converge to more accurate reconstructions.
Quality comparison on challenging cases. Our evaluation demonstrates varying degrees of ef- fectiveness across different datasets. On our Stone dataset, NGS almost completely eliminates false transparency artifacts (Fig-5), resulting fully opaque surface reconstructions. This superior performance can be attributed to the controlled capture environment with consistent, diffuse light
7

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Table 1: Average NVS and SOS results on DTU (Table. S2
OmniObject3D (Table. S3 and our novel dataset (Table. STI Metrics denoted by were acquired when using green infill, as shown in Fig.
and +a denotes use of La.
Method
PSNRT
PSNR*
SSIM
SSIM
LPIPS
LPIPS
SOST
3DGS
25.575
22.967
0.891
0.874
0.180
0.250
0.147
GOF
25.648
21.109
0.880
0.816
0.209
0.273
0.179
DTU
Thopoh 22.817
18.885
0.852
0.780
0.213
0.315
0.135
GSplat+o
25.433
25.263
0.884
0.883
0.183
0.186
0.598
NGS
25.428
25.427
0.881
0.881
0.192
0.192
0.749
3DGS
34,610
27.551
0.949
0.909
0.055
0.222
0.140
GOF 31.469
21,998
0.893
0.780
0,186
0.324
0.126
Stone
StopThePop
32.457
23.047
0.945
0.853
0.078
0.223
0.168
GSplat+a
33.832
33.823
0,948
0.948
0.062
0.062
0.891
NGS
34.148
34.148
0.951
0.951
0.053
0.053
0.922
3DGS
29.300
27.456
0.940
0,929
0.069
0.116
0.215
GOF 32.259
24.931
0.970
0.898
0.062
0.122
0.208
OmniObject
StopThePop
32.274
25.095
0.970
0,900
0.050
0.113
0.265
GSplat+a
33.575
33.350
0.973
0.972
0.060
0.064
0.642
NGS
33.619
33.578
0.972
0.972
0.060
0.060
0.736
d
Figure 5: Renders with green infill revealing transparency (top) and corresponding transmittance maps (bottom) for (a) 3DGS (b) GOF (c) StopthePop. (d) Gsplat+a and (c) NGS
from softbox, which reduces view-dependent effects and provides uniform illumination across all viewpoints. In contrast, while still showing significant improvement, NGS achieves more modest transparency reduction on the DTU and OnniObject3D datasets (Fig. S5 S6 S7
S These datasets feature more variable lighting conditions with stronger directional components, creating view-dependent effects that can be misinterpreted as transparency during optimization. The inconsis- tent shadows and highlights across different viewpoints make it more challenging to establish clear distinction between surface appearance variation and actual transparency. These results highlight the important relationship between lighting consistency and false transparency in BBGS suggesting that controlled capture conditions can substantially enhance the effectiveness of transparency reduction.
4.4 Ablation Study
To thoroughly evaluate the impact of each component in our NGS method, conducted series of ablation experiments using our stone dataset to isolate the effects of individual design choices on both rendering quality and transparency reduction. One at time, removed the following

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Table 2: Mean NVS and [SOS] scores over the Stone Dataset. Metrics denoted by were acquired when using an infill. The current NGS) setting provides a good balance between SOS and NVS metries. Renderings selected stone is shown in Fig.
Method
PSNR1
PSNR*1
SSIM
SSIM#
LPIPS
L
SOST
Ours
32.201
32.201
0.934
0.934
0.145
0.145
0.969
w/o Erosion
30.785
30.785
0.914
0.914
0.225
0.225
1.000
w/o Pruning
31.496
31.489
0.927
0.927
0.151
0.151
0.952
w/o L
31.252
30.930
0.928
0.927
0.126
0.135
0.379
w/o LR reset
26.136
26.133
0.830
0,830
0.416
0,416
0.545
Random Bg 30.205
30.149
0.923
0.923
0.136
0.139
0,467
w/o Color reset
31.442
31.436
0.926
0.926
0.154
0.154
0.962
components from the proposed NGGS pipeline: binary erosion, pruning, Ly (from Eq. 4 and learning rate reset at noise initialization. We also substitute L, for random background. Our results 2) demonstrate that the default NGS configuration achieves optimal balance between rendering quality and transparency reduction. Among the tested variations, two components proved particularly crucial for the method's effectiveness, learning rate reset and L.y-
resuusults (Table
Learning rafe reset. Resetting the learning rate decay for Gaussian means after noise introduction substantially improved the system'ss ability to adapt the optimization landscape. Without this reset, we observed that surface Gaussians struggled to properly adjust their parameters in response to the presence of internal noise Gaussians, resulting in degraded reconstruction quality and higher transparency scores. Ly outperformed alternative background regularization techniques. While random background approaches provided some mitigation of transparency issues, our foreground loss directly incentivizes surface opacity, resulting in a greater reduction transparency scores.
Noise voxel grid erosion. We also note that although not eroding the voxel grid for noise Gaussians achieves better transparency scores, it hinders the visual quality the renders.
This study confirms that NGS)s effectiveness stems from the synergistic combination of appropriate noise guidance and targeted optimization adjustments, with the learning rate reset and foreground loss serving as the most significant contributors to its performance.
Discussion
Findings. Our results demonstrate that Noise Guided Splatting effectively addresses the false transparency problem in object-centric 3D Gaussian Splatting while maintaining or improving NYS quality. By injecting noise Gaussians within object volumes, we successfully force the optimization process to prioritize surface opacity, resulting in more accurate and view-consistent reconstructions. The results align with theoretical understanding of the transparency problem as optimization ambiguity between foreground and background elements.
Standard photometric losses alone cannot distinguish between properly opaque surface and partially transparent surface with solid surface sitting behind. By breaking the line of sight between front and back surfaces with noise infill, we eliminate this ambiguity and guide the optimization toward solutions with appropriate surface opacity. The reduction in SOS across different methods and datasets confirms the generalizability of our approach. Importantly, this improvement does not come at the cost of rendering quality and even improves PSNR and SSSM in some cases. demonstrates that resolving false transparency is not merely a visual enhancement but a fundamental improvement to the reconstruction process.
Limitations. Despite its effectiveness, NGGS has several limitations worth acknowledging. First, our method benefits from high-quality segmentation masks to guide the object-centric reconstruction. Inaccurate segmentation can lead to incorrect noise Gaussian placement, potentially compromising reconstruction quality. This limitation particularly relevant objects with fine details complex boundaries. Second, NGS assumes the material is fully opaque. For inherently transparent or

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translucent objects (e.g,, glass, certain plastics), our approach may incorrectly enforce opacity where transparency is actually desired. Third, our approach requires reasonable Gaussian point cloud to define a convex hull encompassing the entirety of the object. Fourth, while we have shown NGGSs effectiveness for object-centric reconstruction, requires further investigation. The current noise initialization and pruning strategies will require adaptation for scenes with multiple objects and complex spatial relationships. Finally, this method less effective for thin structures where injecting noise is difficult.
application to large-scale scene reconstruction
Computational Considerations. NGS introduces some computational overhead relative standard BDGS) primarily in the form of increased memory requirements and rendering time during training. The number of additional noise Gaussians depends on object geometry complexity, typically increas- ing the total Gaussian count by 30-50%. This translates to proportional increases in memory usage and rendering time. However, several optimizations could mitigate these costs. Our multi-resolution initialization approach already reduces the number of required noise Gaussians compared to uniform voxel filling. Further improvements include reducing the parameters for noise Gaussians down to spheres with variable opacity and color, removing spherical harmonics, quaternions and scales.
Broader Applications. The improved surface consistency provided by NGS has significant im- plications for several downstream applications. In AR/VR asset creation, where accurate object representation is critical for immersive experiences, our method produces more reliable reconstruc- tions with well-defined boundaries. The reduced transparency particularly benefits applications requiring watertight meshes. such as physies simulations or 3D printing. By providing clearer sur- face definitions, [GGS reduces the need for manual cleanup of extracted meshes, streamlining the transition from digital reconstruction efSeeffectvely addresses the false transparency problem, future research may develop alternative approaches to resolve this issue. Nevertheless, the infill assets generated by NGS) can still serve valuable purpose. They provide robust basis for the [SO)] benchmark, thereby facilitating the evaluation and comparison of subsequent methods aimed at mitigating false transparency, regardless of their underlying technique.
physical reproduction. It is also worth noting that while
Future Directions. logical extension of this work involves applying it to more complex scenes containing multiple objects using segmentation methods like Segment Any 3D Gaussians [3] to isolate each object and apply our NGS pipeline individually. Another promising research direction to emerge from this work is to use the noise infill as learnable interior Gaussians and predict the internal structures of the model. For example, FruitNinja 44 used infill particles together with a diffusion model to generate the internal structures of 3D models. The noise infill provide more robust way initializing these method.
10

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13

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A
Appendix Acronyms
3DGS
3D Gaussian Splatting
NGS
Noise Guided Splatting
PSNR
Peak Signal-to-Noiss Ratio
SSIM
Structural Similarity Index
LPIPS
Learned Perceptual Image Patch Similarity
RGBCMY
Red Green Blue Cyan Magenta Yellow
NeRF
Neural Radiance Fields
NVS
Novel View Synthesis
505
Surface Opacity Score
MCMC
Markov Chain Monte Carlo
GOF
Gaussian Opacity Fields
ADC
Adaptive Density Control
QQA
mmge Quality Assessment
14

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B Appendix Evaluation Results
Table S1: Full NVS and [SO
results on the Stone Dataset. Metrics denoted by were acquired
when using infill, shown in Fig. ald +a denotes of L. and No Gaussians denotes the number of Gaussians.
Index
Method
PSNR
PSNR*
SSIM
SSIM
LPIPS
LPIPS*
SOS
N Gaussians
NGS
32.563
32.563
0.947
0.947
0,062
0.062
1.000
585856
Gsplat+o
32.260
32.260
0.943
0.943
0.069
0.069
1.000
551437
NGS
36.229
36,229
0.956
0.956
0.043
0.043
1.000
575300
2
Gsplat+a 35.968
35.968
0.954
0.954
0.050
0.050
1.000
479522
NGS 33.418
33.418
0.945
0.945
0.071
0.071
0.926
591907
3
Gsplat+a 33.168
33.168
0,942
0,942
0.081
0.081
0.835
476246
NGS
36.244
36.244
0.962
0.962
0.046
0.046
0.953
554600
4
Gsplat+a 35.814
35.814
0.957
0.957
0.055
0.055
0.949
453943
NGS 32.871
32,871
0.953
0.953
0.053
0.053
0.958
542696
5
Gsplat+a 32.703
32,703
0.951
0.951
0.061
0.061
0.927
442430
NGS 33,302
33,302
0.940
0.940
0.057
0.057
0.626
615372
Gsplat+a
32,996
32,992
0.937
0.937
0.062
0.063
0.495
484779
NGS
32.993
32,993
0.947
0,947
0.053
0.053
0.913
540734
7
Gsplat+a
32.785
32.785
0.944
0.944
0.061
0.061
0.912
423754
NGS 33.153
33.153
0.948
0.948
0.042
0.042
0.995
571105
8
Gsplat+a 32.826
32.826
0.945
0.945
0.049
0.049
0.975
428108
NGS 36.517
36,517
0.960
0.960
0.048
0.048
0.961
585541
9
Gsplat+a 35,973
35.972
0.956
0.956
0.058
0.058
0.950
502017
NGS 34.193
38,186
0.951
0.951
0.057
0.057
0.890
564590
10
Gsplat+a
33,823
33.739
0,947
0,947
0.070
0.071
0.872
435205
NGS
34.148
34.148
0.951
0.951
0.053
0.053
0.922
572770
Mean
Gsplat+a
33,832
33.823
0.948
0.948
0,062
0.062
0.891
467744
NGS
1.565
1.565
0.007
0.007
0.009
0.009
0.111
23220
Std.
Gsplat+a
1.493
1.494
0.007
0.007
0.010
0.010
0.149
39548
Scan indices correspond the following files: (1) scan_ 20250416
093245, (2) scan 20250422221466110 0000004166140444550 (3) scan 2et_22222016614420 143850, (4)
scan 202506026616111. 101101161111881,, (5) scan 20250416. 161137 (6) 20250402 165122,, (7)
(8) sean 12250417_112354,
202504117 009250417_1503811 159900 20250417, 15336112
15

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Table S2: Full NVS and SOS
results DTU. Metries denoted by * acquired when using and +a denotes use of La. and No Gaussians denotes the number of
green infill, as shown in Fig. Gaussians.
Method
PSNR
PSNR*
SSIM
SSIM
LPIPS
LPIPS*
SOS
Gaussians
NGS
24.359
24.359
0.903
0.903
0.097
0.097
0.888
1461500
Gsplat+a
24.372
24.365
0.905
0.905
0.092
0.093
0.773
2113100
NGS
22.896
22.894
0,888
0.888
0.227
0.227
0.585
404102
2
Gsplat+a
22.787
22,283
0.890
0.887
0,220
0.229
0.418
566632
NGS
24.450
24.450
0.880
0.880
0.234
0.234
0.475
495216
3
Gsplat+a 24.390
24.280
0.880
0.880
0,228
0.231
0.318
692157
NGS 27,541
27.541
0,905
0.905
0,148
0.148
0.953
782621
4
Gsplat+a 27.679
27.679
0.909
0.909
0.139
0.139
0.978
1249151
NGS 25.682
25.682
0.877
0.877
0,196
0.196
0.791
567251
5
Gsplat+a 25.609
25.515
0.878
0.878
0.187
0.189
0.367
859701
NGS 28.532
28.529
0.894
0.894
0.187
0.188
0.552
642402
6
Gsplat+a 28.525
28.036
0.897
0.894
0.177
0.185
0.443
1007084
NGS 24.532
24.532
0.821
0.821
0.253
0.253
0.999
633185
7
Gsplat+a
24.681
24.681
0.827
0.827
0,238
0.238
0.890
839702
NGS
25.428
25.427
0.881
0.881
0.192
0.192
0.749
712325
Mean
Gsplat+a
25.435
25.263
0.884
0.883
0.183
0.186
0.598
1046790
NGS
1.978
1.978
0.029
0.029
0.054
0.054
0.211
351317
Std.
Gsplat+a
2.017
2.024
0.027
0.027
0.053
0.054
0.273
518612
Scan indiees conrepoond the following files: (1) scan55, (2) scan65, (3) scan69, (4) scan106, (5) scan 14, (6) scanl (7) scan122
16

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Table S3: Full and SOS results on OmniObject3D. Metrics denoted by were acquired when using a green infill, as shown in Fig. [ and denotes use of La and No Gaussians denotes the
number of Gaussians.
Method
PSNR
PSNR*
SSIM
SSIM
LPIPS
LPIPS*
SOS
N" Gaussians
NGS
35.848
35.608
0.975
0.975
0.098
0.100
0.555
191628
Gsplat+a
35.825
35.157
0.976
0.975
0.095
0.105
0.402
250896
NGS 31.690
31,690
0.978
0.978
0,039
0.039
0.806
223903
2
Gsplat+a 31,637
31,598
0,978
0.978
0.040
0.041
0.605
279212
NGS 35.682
35.671
0.980
0.980
0.020
0.020
0.817
123827
3
Gsplat+a 35.673
35.663
0.981
0.981
0.020
0.020
0.837
169815
NGS 37.958
37.958
0.972
0.972
0.083
0.083
1.000
231004
4
Gsplat+a 37.894
37.894
0.972
0.972
0.082
0.082
1.000
300919
NGS 33.284
33.283
0.977
0.977
0.045
0.045
0.439
274345
Gsplat+a 33.212
32.431
0.978
0.976
0.044
0.055
0.324
343927
NGS 32.178
32,178
0.972
0.972
0.048
0.048
0.834
223486
6
Gsplat+a 32.124
32.124
0.972
0.972
0.048
0.048
0.825
272385
NGS 28.693
28.658
0.953
0.953
0.085
0.088
0.699
268095
7
Gsplat+a 28.663
28.583
0.954
0.954
0.089
0.096
0.504
332044
NGS
33.619
33,578
0.972
0.972
0.060
0.060
0.736
219469.7
Mean
Gsplat+o
33.575
33.350
0.973
0.972
0.060
0.064
0.642
278456.857
NGS
3.115
3.096
0.009
0.009
0.029
0.030
0.188
50772.745
Std.
Gsplat+a
3.115
3.093
0.009
0.009
0.029
0.031
0.251
58112.765
Scan indises correspond to the following files: (1) antique_ 004, 007. (7) dinosaur_
004, (2) dinosaur 004, (3) dinosmur_000 (4) antigue_005, (5) dinosaur, 0006, (6)
17

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C
Appendix Supplementary Figures
20250426 095404
20250416 134048
20230437 111032
20250416 172001
20250416 113008
20250416 134601
scon 20204416
scan 2025042022100000 1709877
scon 202500224
SCM 20250417 10
scnn scm_202500 20250416 14035
SCML 201.22220666600000 156830
SCMO 20250416, 163026
163326
stan, 202500166666 185825
111904171161877777 161817
Ion 200_2059666 135320
scer 20750426
Figure S1: Stone dataset with noise augmentations
18

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scan65
Figure S2: DTU with noise augmentation
19

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anbique 015
018
010
020
artique 021
doosaurr001
diosauu_002
dinosaur.03
dinosuurr00 000
dinosuur_oo
dinesaur
dinosaur
Figure S3: OnniObject3D with noise augmentation
20

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Sum
Figure FiggueSSte S4: Object dataset with noise a |agmentationn
21

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Figure S5: DTU renders with green infill revealing transparency (top) and corresponding transmittance
maps (bottom) for (a) BDGS (b) GOF
(c) StopthePop, (d) Gsplat+a and (e) NGS
22

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a
b
Figure S6: DTU renders with green infill revealing transparency (top) and corresponding transmittance
maps (bottom) for (a) 3DGS (b) GOF (c) StopthePop. (d) Gsplat+a and (c) NGS
23

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Figure S7: OmniObject3D renders with green infill revealing transparency (top) and corresponding
transmittance maps (bottom) for (a) 3DGS (b) GOF (c) StopthePop, (d) Gsplat+a and NG)
24

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Figure S8: OmniOhject3D renders with green infill revealing transparency (top) and corresponding
transmittance maps (bottom) for (a) 3DGS (b) GOF
(c) StopthePop, (d) Gsplat+a and (e) NGS
25

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NVS
w/o Erosion
w/o Pruning
w/o Learning Rate reset
Random Background
w/o 4
Figure S9: A sample from the Stone Dataset used in the ablation study. The reconstructions are filled with green noise. Reset the learning rate affects the quality of final rendering the most significantly. Stopping erosion also reduces the surface quality. Most of the results do show visible false transparency, but Can be quantified and compared.
26

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NeurIPS Paper Checklist
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[Yes]
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We discussed the limitations fo the work in the discussion section.
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Reviews:
[{"review_id": "UCtca5FRrj", "reviewer": "Reviewer_faeg", "summary": "This paper provides a plug and play solution to mistakes in transparency estimation of gaussian splatting in an object oriented setup. \nGiven 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.\nFor 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. \nThey 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.", "questions": "Why is the pruning kept in the method?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\nThe method is novel with a small amount of overhead and easily applicable to different gaussian splatting methods. \nThe experiments are conclusive over 3 datasets and ablations cover the different elements of the method.\nThe paper is well written.\n\nWeaknesses:\nBased on the ablation it seems that pruning is not helpful so the justification for keeping it is unclear.", "was_revised": true, "final_justification": "I have read the other reviewer's concerns regarding the impact or importance of these false transparencies. In my experience they are quite important if you are to use 3dgs as a true 3d model rather than just a video of camera trajectory generator, such as for VR and Gaming. \n\nIt is unfortunate that currently we don't have evaluation metrics that is more comprehensive regarding the full 3D model of a scene, which should be a point of further investigation and benchmarking in future. Ideally we should be able to quantify the effect of pruning on the results easily, but I agree that is not the scope of this project.\n\nHence, I keep my rating as accept.", "rebuttal": "We sincerely thank you for your positive review and for highlighting the novelty, applicability, and conclusive experiments of our method. We are particularly grateful for your appreciation of our quantitative evaluation approach for false transparency.\n\n**Question: Why is the pruning kept in the method?**\n\nThis is an excellent question. We acknowledge that the impact of removing pruning is not obvious from the quantitative metrics in our ablation study (Table 2), a point we will clarify in the manuscript.\n\nWhile the global metrics remain high, pruning is crucial for visual quality, particularly in regions with high geometric complexity or concavity. Without pruning (specifically, the depth-aware pruning and erosion steps in Sec. 3.3), we observed that noise Gaussians can become embedded within the surface layer itself. This creates small but visually distracting artifacts like blurring or color blending on the object's surface.\n\nThese localized artifacts are not well captured by global metrics like PSNR or SSIM, which average errors across the entire image. However, they cause small local blurs of the reconstructed object.\n\nTherefore, we retain pruning not for a major quantitative boost, but to guarantee the visual fidelity and crispness of the surface. It ensures that noise is strictly confined to the object's interior and does not interfere with the foreground, which is a key goal of our work.\n\nTo make this clearer, we will update the revised manuscript to:\n- Clarify this reasoning in the ablation study (Sec. 4.4).\n- Include a visual comparison showing these subtle artifacts.\n- Briefly justify the need for pruning in the method description (Sec. 3.3).\n\nWe hope this clarifies the importance of the pruning step. Thank you once again for your constructive feedback."}, {"review_id": "dVFvASQycs", "reviewer": "Reviewer_6z1z", "summary": "The 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.", "questions": "(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.", "limitations": "The paper has discussed the main limitations of the proposed method.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I have read the authors’ rebuttal as well as the other reviewers’ comments.  The rebuttal clarifies some terms and descriptions, which reveals that the paper indeed contains ambiguities and errors. In the rebuttal, the authors emphasize that a central contribution of the work is the identification and analysis of \"false transparency\", which occurs primarily during interactive viewing. Then a straightforward question arises: how important is this issue, especially in the context of 3D-GS? This significance is neither clearly explained nor well illustrated in the paper. Since the evaluation should mainly be based on the submitted version, I maintain my original rating. However, given that the authors could improve their paper by incorporating the comments or feedback from the reviewers, I would also consider a Borderline accept reasonable.", "rebuttal": "We appreciate the reviewer's recognition of our method's practicality, ease of implementation, and the utility of our proposed metric and dataset. We wish to re-emphasize that one of our primary goals is to introduce the \"false transparency\" problem, a previously unreported issue that is difficult to detect with conventional metrics. Our work provides the first identification of this problem, an effective solution, and practical tools (metrics, dataset) to evaluate it and facilitate future research.\n\n**Weakness 1.1: missing clear dynamic or temporal results to support the claim**\n\nThis point stems from a linguistic misunderstanding. In lines 37-38, we intended to distinguish between: \n- **Static images**: Rendered 2D images from the model\n- **Dynamic renderings**: Interactively viewing the static 3D model in a viewer (e.g., Viser).\n\nOur terminology failed to distinguish this from rendering a 4D dynamic scene. To correct this ambiguity, we will revise the phrase “dynamic renderings” to the more precise “interactive viewing of 3DGS models” throughout the manuscript.\n\n\n**Weakness 1.2: the authors do not clearly identify or exemplify the false transparency problem**\n\nWe agree that we did not sufficiently exemplify this novel problem upfront, which is critical for a previously unrecognized issue. We will revise the manuscript to provide a clearer, more detailed description of the problem from the start:\n- **Abstract**: We will clarify that \"false transparency\" is a novel problem and that this paper provides its identification, solution, and evaluation framework. \n- **Introduction**: We will describe the symptoms more clearly. False transparency manifests during interactive rotation, where background and internal structures appear to \"show through\" the object's surface. This illusion occurs because sparse internal Gaussians, which are blended and hidden in static views, move out of sync with the surface during rotation. This parallax effect creates a distracting, translucent appearance, especially when fine background textures misalign with the surface. \n\n**Weakness 2: The effectiveness of the proposed method on standard datasets is not convincingly demonstrated and lacks comparison with strong baselines.**\n\nAfter revising the comments, we have realized that Table 1 did not include the latest version of experiments and had mislabeled data, noticeably including the old term FTS instead of Surface Opacity Score (SOS), which was our previous name for the new metric. We have updated the table and provide a revised version with corrected SOS label. \n\n|Data|Method|PSNR|PSNR*|SSIM|SSIM*|LPIPS|LPIPS*|SOS|\n|:---|:---|:---|:---|:---|:---|:---|:---|:---|\n|DTU|3DGS|25.575|22.967|**0.891**|0.874|**0.180**|0.250|0.147|\n|DTU|GOF|**25.648**|21.109|0.88|0.816|0.209|0.273|0.179|\n|DTU|StopThePop|22.817|18.885|0.852|0.78|0.213|0.315|0.135|\n|DTU|gsplat + $L_{\\alpha}$|25.435|25.263|0.884|**0.883**|0.183|**0.186**|0.598|\n|DTU|NGS (gsplat + $L_{\\alpha}$ + noise)|25.428|**25.427**|0.881|0.881|0.192|0.192|**0.749**|\n||\n|Stone|3DGS|**34.61**|27.551|0.949|0.909|0.055|0.222|0.14|\n|Stone|GOF|31.469|21.998|0.893|0.78|0.186|0.324|0.126|\n|Stone|StopThePop|32.457|23.047|0.945|0.853|0.078|0.223|0.168|\n|Stone|gsplat+ $L_{\\alpha}$|33.832|33.823|0.948|0.948|0.062|0.062|0.891|\n|Stone|NGS (gsplat + $L_{\\alpha}$ + noise)|34.148|**34.148**|**0.951**|**0.951**|**0.053**|**0.053**|**0.922**|\n||\n|OmniObject3D|3DGS|29.3|27.456|0.94|0.929|0.069|0.116|0.215|\n|OmniObject3D|GOF|32.259|24.931|0.97|0.898|0.062|0.122|0.208|\n|OmniObject3D|StopThePop|32.274|25.095|0.97|0.9|**0.05**|0.113|0.265|\n|OmniObject3D|gsplat+ $L_{\\alpha}$|33.575|33.350|**0.973**|**0.972**|0.060|0.064|0.642|\n|OmniObject3D|NGS (gsplat + $L_{\\alpha}$ + noise)|**33.619**|**33.578**|0.972|**0.972**|0.060|**0.060**|**0.736**|\n \nAs the first work to address this problem, **no direct baselines for false transparency exist**. Therefore, we introduced new evaluation tools:\n1. A **qualitative infill method** to visually highlight transparent surfaces (Fig. 5, S4-6).\n2. **Standard metrics** (PSNR*, SSIM*, LPIPS*) **computed with infills** to quantify errors in these problematic regions.\n3. The **quantitative SOS** to measure surface opacity, with higher values indicating less transparency.\n\nRegarding baseline comparisons, Tables 1, S1, and S2 already present comprehensive quantitative evaluations against strong reconstruction methods (original 3DGS, GOF, StopThePop) on three datasets (DTU, OmniObject, Stone), using both standard metrics and our proposed additions.\n\n\n**Q1: How much does false transparency affect rendering quality**\n\nThis is explained in Weakness 1.2 above. In short, an opaque object appears as a translucent shell filled with distracting, spiky internal structures that are only visible during interactive camera movement. While we cannot include URLs in the paper, we will include interactive demos on our project page. The effect is readily observable on 3DGS sharing platforms in object-centric models with uniform textures (e.g., food, toys, decorations).\n\n**Q2: qualitative visualizations on standard open datasets.**\n\nWe will add more qualitative visualizations for the DTU and OmniObject3D datasets in the revised manuscript, similar to those provided  in Fig. S4-5.\n\n**Q3: whether injected noise infill introduces new artifacts/unintended occlusion, and how it avoids damaging fine surface details.**\n\nThis is a critical point. We prevent noise from degrading surface quality through a multi-step process detailed in Sec. 3.3:\n1. **Depth-aware pruning** (L154): We remove noise Gaussians that are in front of the object's surface based on depth ordering.\n2. **Binary erosion of the occupancy volume** (L155): We erode the occupancy grid to create a buffer zone, ensuring noise Gaussians do not interfere with surface optimization.\n3. **Trainable noise Gaussian opacity** (L163): We freeze the surface and train only the opacity of noise Gaussians, which effectively prunes any noise that has become integrated with the surface.\n4. **Random noise color** (L165): We randomize noise color at each iteration to prevent the model from integrating the noise into the surface texture.\n\nAs shown in our ablation study (Table 2), these measures are essential for maintaining rendering quality. We will add a summary at the beginning of Section 3.3 to make this logic clearer.\n\n**Q4: Generalization to more complex scenes and initialization/management of noise infill in such cases.**\n\nWe will add a discussion on this in the discussion section. Our method is most relevant for large, well-observed objects, as false transparency is less pronounced in small, thin, or partially-viewed objects. For complex scenes with multiple objects, our approach can be extended. One could use segmentation methods like Segment-Anything [arXiv:2304.02643] to isolate each object, compute its convex hull, and then apply our noise infill and opacity enforcement independently to each significant object in the scene.\n\n**Q5: Typos and figure errors.**\n\nWe will proofread the manuscript to correct all typos and figure errors, including the \"FTS\" vs. \"SOS\" terminology in our tables. We thank the reviewer and welcome them to point out any other specific errors they may have noticed."}, {"review_id": "3TgU6KKgPu", "reviewer": "Reviewer_N48H", "summary": "The 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.", "questions": "1. 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.", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### 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.", "was_revised": true, "final_justification": "Regarding the core solution, I agree with the authors' explanation and the method is proved to be effective. However, I believe that providing a stronger theoretical foundation would significantly enhance the method’s credibility and generalizability. \n\nAt present, the solution appears highly dependent on some assumptions such as full opacity, the availability of a good initial point cloud, and the exclusion of thin structures, as replied by the author, which may limit its applicability in broader, more unconstrained scenarios.\n\nMoreover, while the method addresses a genuine artifact in 3DGS and proposes novel evaluation metrics SOS and infill drop, the practical impact remains unclear. If the method does not consistently improve reconstruction quality or NVS performance, its contribution to the community may be limited. A more compelling case is needed to demonstrate why solving false transparency is essential, especially in real-world applications, and how this solution integrates with or benefits downstream tasks in 3D vision.\n\nIn its current form, I remain unconvinced about the broader value of the method beyond the specific diagnostic use case. I will keep my rating to 3, borderline reject.", "rebuttal": "We are grateful for your thorough review and insightful comments. We address your specific points below:\n\n**Q3: What is the metric \"FTS\" in Table 1?** *(We want to address this first, as it is a critical point. )*\n\n**\"FTS\" is a typo and should be Surface Opacity Score (SOS)**. We initially used the term \"False Transparency Score\" (FTS) but changed it to SOS for better perceptual alignment, where a higher score correctly indicates a more opaque surface. We sincerely apologize for failing to update this term in the table and for the confusion it caused.\n\n**Weakness 1.1: The solution is an empirical \"noise fill-in\" heuristic lacking theoretical rationale/analysis**\n\nWe agree the theoretical rationale was not sufficiently clear. The root cause of false transparency is an ill-posed optimization problem (Sec 3.1, Fig 2). Because 3DGS training relies on 2D photometric loss, two scenarios are ambiguous:\n1. An opaque surface\n2. A semi-transparent surface blended with background or internal Gaussians\n\nBoth can produce identical static images, allowing the optimizer to erroneously favor the second case. Our method, NGS, resolves this ambiguity by injecting noise Gaussians inside the object. This noise acts as an **occlusion barrier**, breaking the line of sight from the camera to the background. The photometric loss is now forced to make the surface opaque to hide the random colors of the internal noise. These points are briefly discussed in  Section 3.3 (L135), but we believe this section requires more substantial rationale.\n\nWhile NGS is a heuristic, its empirical nature is a strength, making it an adaptable, **\"plug-and-play\"** solution that minimally alters the core rendering pipeline. Furthermore, by defining the object's interior, NGS enables our proposed SOS and infill evaluation methods, which are valuable tools for characterizing this artifact in any method.\n\n**Weakness 1.2: under what conditions it might fail?**\n\nA primary failure mode is the integration of noise into the object's surface, which would degrade reconstruction quality. We did not state this explicitly, but several core components of NGS are designed specifically to prevent this:\n- Depth-aware pruning\n- Erosion of the occupancy volume\n- Trainable-only noise opacity\n- Randomized noise color\n\nWe will clarify this in the revision. Other limitations of NGS, discussed in L299, include: \n- assumes full opacity and may fail on inherently translucent objects\n- requires a reasonable initial point cloud to define the convex hull for noise injection\n- less effective for very thin structures where injecting noise is difficult (though false transparency is also less common in such cases).\n\n**Weakness 2.1: NGS yields only parity on NVS quality but incurs ~50% extra memory and additional training time**\n \nWe acknowledge this concern. The ~50% overhead reported was for our controlled experiments and is not an optimized lower bound. This cost is highly tunable. As noted in L211, memory and time can be substantially reduced by lowering the noise density or resolution. Removing Spherical Harmonics (SH) parameters from noise Gaussians is a strategy we did not mention, which alone would reduce their memory footprint by ~80%. These were not implement as we only uses 4GB VRAM on average.\n\n**Weakness 2.2: Lack of downstream reconstruction metrics to substantiate claims of improved 3D reconstruction**\n\nWe wish to reiterate that the false transparency artifact is **undetectable with static renderings and standard NVS metrics**. The quality improvement is in the **continuous view coherence** of the 3D reconstruction, which is only apparent during interactive viewing.\n\nFalse transparency manifests as a distracting visual artifact where background and internal structures move out-of-sync with the surface, making it appear translucent during camera motion. Fixing this ensures a stable, solid surface, which is a crucial aspect of 3D reconstruction quality not captured by static 2D metrics. A substantial contribution of our work is providing the first reconstruction metrics to characterize and measure this previously overlooked dynamic artifact.\n\n**Weakness 3: \"Stone\" dataset's niche, single-material nature limits representativeness, thus generality of transparency findings unclear**\n\nWe acknowledge the specific nature of the Stone dataset. It was collected due to its challenging properties (low texture, repeating patterns) that make false transparency artifacts more prominent, establishing it as an ideal diagnostic tool.\n\nHowever, our findings are not limited to this dataset. We demonstrate that false transparency is a general problem by providing analysis and infill add-ons for the diverse DTU and OmniObject3D datasets (see Appendix Fig. S2-S5). To further prove the generality of our findings, we have since scanned 100+ common objects (fruits, vegetables, toys, office supplies, kitchen supplies, sport supplies, etc.) and observed the same artifact. We will release this expanded dataset to support future research.\n\n**Q1: Please provide quantitative evidence that eliminating false transparency truly improves 3D reconstruction**\n\nAs **standard NVS metrics are insensitive to this dynamic artifact**, we proposed two new quantitative evaluation strategies:\n- **Measure with/without the infill**: As shown in Table 1, a large drop in PSNR/SSIM between a normal render (e.g., PSNR) and a render with our noise infill visible (PSNR*) indicates significant transparency, as the \"leaked\" infill pattern degrades image quality.\n- **Surface Opacity Score (SOS)**: This is a direct, quantitative measure of surface opacity designed to assess false transparency without relying on standard metrics.\n\nWe invite the reviewer to see the revised Table 1 with the latest experiments results and corrected terms in our rebuttal to reviewer 6z1z.\n\n**Q2.1: Does this benefit translate into a more surface-aligned Gaussian representation?**\n\nNGS does not explicitly enforce surface-aligned Gaussians. Its goal is to solve the opacity ambiguity. However, it is fully compatible with orthogonal methods that do promote surface alignment (e.g., RaDe-GS, 2DGS).\n\n**Q2.2: Leaner Gaussian? explicit Gaussian-count vs. quality analysis**\n\nThis is an excellent suggestion. While reducing the Gaussian count was not a primary goal, we have conducted this analysis as suggested. We will include a Gaussian-count vs. quality comparison in the revised manuscript.\n\n|Data|Method|PSNR|SSIM|LPIPS|SOS|Gaussian Count|\n|:---|:---|:---|:---|:---|:---|:---|\n|DTU|NGS(gsplat+$L_{\\alpha}$+noise)|25.428|0.881|0.192|**0.749**|**712325**|\n||gsplat+$L_{\\alpha}$|**25.435**|**0.884**|**0.183**|0.598|1046790|\n|Stones|NGS(gsplat+$L_{\\alpha}$+noise)|**34.148**|**0.951**|**0.053**|**0.922**|572770|\n||gsplat+$L_{\\alpha}$|33.832|0.948|0.062|0.891|**467744**|\n|OmniObject3D|NGS(gsplat+$L_{\\alpha}$+noise)|**33.619**|0.972|**0.060**|**0.736**|**219470**|\n||gsplat+$L_{\\alpha}$|33.575|**0.973**|**0.060**|0.642|278457|\n\nInterestingly, inclusion of noise in the training indeed led to a leaner gaussian representation with an average decrease of 30% on DTU and OmniObject3D.  The gaussian count is higher for the stone dataset, but the reconstruction metrics are also substantially higher."}, {"review_id": "Kblg8QrhuK", "reviewer": "Reviewer_BBSm", "summary": "This 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.", "questions": "## 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?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 1, "clarity": 1, "significance": 1, "originality": 2, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "The paper introduces three key contributions:\n\n1. Identification and formalization of the false transparency problem in 3D Gaussian Splatting\n2. An effective algorithm to address this issue\n3. The Surface Opacity Score (SOS) metric for evaluating transparency artifacts.\n\nThese contributions are technically meaningful and justify acceptance.\n\nInitially, the paper was difficult to follow due to fragmented presentation and missing context. However, the authors clearly show how they will restructure the method section and improve readability, which effectively addresses my earlier concerns.", "rebuttal": "We appreciate the detailed feedback. We want to start by clarifying two key points that may have caused major confusion:\n- **Typo**: We mistakenly used an old abbreviation, \"FTS,\" instead of our final metric name, Surface Opacity Score (SOS), in several tables. We apologize for this error and will correct it.\n- **Artifact Definition**: We didn't clearly define the \"false transparency\" symptom upfront, which may have led to confusion with \"floater\" artifacts.\n\nWe address your specific points below.\n\n**Weakness 1: Fragmented content structure**\n\nThe current presentation of our method in Section 3.3 could be more cohesive and step-by-step. We will include an overview in L138 with two major modifications: the introduction of noise Gaussians within the object’s volume in a coarse to fine approach and depth aware noise gaussian pruning and erosion in iteration t1 allowing enough time to establish basic surface structure and appearance, and the addition of alpha consistency loss. The infill step is indeed important for noise injections.\n\n\n**Weakness 2: Incomplete method description**\n\nGood point. The learning rate (LR) reset is an important detail. While we included it in the implementation details and ablation studies, we agree it belongs in the main method description. We'll add it to Section 3 in the revision for better clarity. \n\n**Weakness 3 / Q1: Distinction from floater artifacts**\n\nWe want to clarify that false transparency is fundamentally different from floaters, even though both can lead to visual inconsistencies in interactive viewing and are not well-captured by standard static rendering metrics.\n- Floaters involve sparse points reconstructed at incorrect depths above the surface. \n- False transparency occurs when the opaque surface is reconstructed at the **correct depth** with **wrong opacity**\n- Floaters can show up in static novel view renderings, not the case for false transparency\n- NGS does not solve floater problem\n- Infill and our datasets do not reveal the floater artifact\n- SOS does not measure the floater artifact\n- NGS should be compatible with methods that address floater problem\n\nThis leads to different symptoms. The false transparency artifact is only apparent during viewport transition, where background and internal details seem to move out-of-sync with the surface, creating an illusion of translucency. Floaters, on the other hand, are often visible as detached blobs in static novel-view renderings. Our method (NGS), our evaluation metric (SOS), and our dataset are all designed to address opacity and do not solve or measure the floater problem. We'll add a section to the related works to explicitly discuss this distinction.\n\n**Weakness 4: Significance concerns**\n\nRegarding your concerns:\n- **Limitation**: It's the artifact itself, not our method, that is most relevant to solid, object-centric scenarios. Unlike floaters, which can appear anywhere, false transparency is an issue of objects not appearing solid when viewed from all sides.\n- **Significance**: The significance of this paper isn't just the solution. Our primary contribution is being the first to **identify, define, solve, and provide tools to measure** the previously unreported \"false transparency\" artifact. By raising awareness and contributing new evaluation methods (the infill technique and SOS metric), we believe our work will enable and facilitate future research in this area.\n\n\n**Q2: Could you re-express your method as an algorithm?**\n\nThe key steps missing from the initial algorithm were the noise injection and the distinction between surface and noise Gaussians. Here is a more accurate representation:\n```\nfor iteration in range(30000):\n    if iteration == 6000:\n        # Initialize and Prune Noise Gaussians\n        noise_gaussians = compute_convex_hull_for_surface_saussians_and_voxelize()\n        depth_prune_and_binary_erode(noise_gaussians)\n        freeze_parameters(surface_gaussians)\n        for iteration i from 1 to 1000:\n            refine_prune_based_on_opacity(noise_gaussians)\n            reset_learning_rate(surface_gaussians.means)\n            freeze_parameters(noise_gaussians)\n            unfreeze_parameters(surface_gaussians)\n    if iteration > 6000:\n\tassign_random_RGBCMY_color(noise)\n\trender_image_with_both_gaussian_sets(surface_gaussians + noise_gaussians)\n\tcompute_photometric_loss()\n\tcompute_alpha_consistency_loss(surface_gaussians, seg_mask)\n\tupdate_parameters(surface_gaussians) \n```\n\n**Q3: Is the pruning method potentially flawed?**\n\nThis is an excellent question that stems from the confusion we created. We apologize for the ambiguity. The pruning does not touch the original surface Gaussians. It is applied only to the newly injected noise Gaussians to ensure they are correctly positioned inside the object and don't interfere with the surface. The process is:\n1. We inject a cloud of noise Gaussians inside the object's approximate volume.\n2. We then prune any of these noise Gaussians that were accidentally initialized in front of or too close to the surface Gaussians.\n3. The remaining noise Gaussians form an internal \"infill\" that forces the actual surface to become opaque.\n\nUsing the provided figure as an example:\n- The figure misses the injected noise gaussians.\n- The noise gaussians are injected inside the ball approximately between the red X and green X.\n- The pruning is performed on the injected noise gaussians. \n- Noise gaussians above or near the object surface will be pruned. \n- Noise gaussians inside the object cavity are not pruned and become the infill that indicates the space.\n- The \"floater\" artifact (orange X in the figure) is irrelevant to our pruning step, as it's part of the surface model, which we don't prune.\n\n\n**Q4: Is the Surface Opacity Score (SOS) perceptually aligned? And why does ablation favor lower SOS?**\n\nYes, SOS is designed to be perceptually aligned. A score of 1.0 is fully opaque, and a score near 0.0 is fully transparent. We find that renders with an SOS above 0.5 generally have no visible false transparency. A figure will be provided to demo perceptual linearity. \n\nRegarding the ablation in Table 2, the \"w/o Erosion\" setting shows a trade-off. While its SOS is a marginal 1.0 vs. our 0.968, this comes at a significant cost to standard rendering metrics (PSNR/SSIM). Our method achieves the best balance between high opacity and overall render quality.\n\nAnd to reiterate, we deeply apologize for the \"FTS\" vs. \"SOS\" typo in the tables. It will be corrected everywhere.\n\n\n**Q5: Why limit noise colors to RGBCMY?**\n\nWe chose RGBCMY because these six primary and secondary colors offer maximum contrast against most natural surface colors in RGB and HSV space. This high contrast creates a strong, unambiguous error signal if any noise \"leaks\" through a semi-transparent surface, forcing the model to quickly optimize the surface opacity to hide it.\n\nAs per your suggestion, we ran an experiment with random RGB noise. The results confirm our initial design choice, as RGBCMY provides better overall performance. We'll add this to our ablation study.\n\n\n| Method               | PSNR    | PSNR\\*  | SSIM   | SSIM\\* | LPIPS  | LPIPS\\* | SOS    |\n| :------------------- | :------ | :------ | :----- | :----- | :----- | :------ | :----- |\n| default              | **32\\.201** | **32\\.201** | **0\\.934** | **0\\.934** | **0\\.145** | **0\\.145**  | **0\\.969** |\n| ...          | ... | ... | ... | ... |... | ...  | ... |\n| random RGB noise        | 31\\.442 | 31\\.436 | 0\\.926 | 0\\.926 | 0\\.154 | 0\\.154  | 0\\.962 |\n\n\n**Q6: How is the 360° object case handled, given the limitations of alpha-consistency loss?**\n\nYou've correctly identified a key synergy in our method. While standard alpha-consistency loss can struggle with 360° captures, **NGS directly solves its primary limitation**.\n\nThe injected noise acts as an internal **occlusion barrier**, physically separating the front and back surfaces of the object. This provides the strong bias for opacity on the front-most surface that the alpha-consistency loss alone lacks in a 360° setting. This is why NGS performs well on 360° datasets like DTU and OmniObject3D, as shown by the strong SOS scores in our results."}]

Author rebuttal:

Meta-review: This paper presents a simple, yet highly effective and practical solution to a specific but important problem in 3D Gaussian Splatting (3DGS): erroneous transparency in object-centric scenes. The core idea—strategically seeding opaque, noisy Gaussians within an object's volume to break the line-of-sight between viewpoints—is both novel and intuitive. This approach cleverly forces the optimization to converge on a correct, opaque surface representation.

A significant strength of this work is its practical utility. The method is designed as a plug-and-play module with low computational overhead, making it easily integrable into existing 3DGS pipelines. This usability enhances its potential impact on the community.

The experimental validation is thorough and convincing. Results across three datasets demonstrate a clear and significant improvement over baseline methods. The introduction of a green infill evaluation metric is also a valuable contribution, providing a clear and direct visual method for assessing transparency artifacts. The ablation studies are generally well-designed, effectively isolating the contribution of the core "noisy splatting" mechanism.

The noted weakness—that the pruning step appears unhelpful in the ablation—is a valid observation. However, this is a minor flaw in an otherwise strong paper. The retention of this step does not detract from the primary contribution, which is the overall initialization and optimization strategy. The core concept remains sound and impactful without it.

In conclusion, this work addresses a well-defined problem with an elegant, effective, and highly applicable solution. The novelty, practical utility, and strong empirical results make it a valuable contribution worthy of acceptance. The minor issue with the pruning step can be addressed by the authors in a revision, perhaps by providing a clearer justification or removing the step altogether.
Decision: Accept (poster)