CLoD-GS: Continuous Level-of-Detail via 3D Gaussian Splatting
Authors: Zhigang Cheng
Mingchao Sun
Yu Liu
Zengye Ge
Luyang Tang
Mu Xu
Yangyan Li
Peng Pan
Venue: ICLR 2026 Poster (2026)
Keywords: Level of Detail
3D Gaussian Splatting
Neural Scene Representation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Level of Detail (LoD) is a fundamental technique in real-time computer graphics for managing the rendering costs of complex scenes while preserving visual fidelity. Traditionally, LoD is implemented using discrete levels (DLoD), where multiple, distinct versions of a model are swapped out at different distances. However, this long-standing paradigm suffers from two major drawbacks: it requires significant storage for multiple model copies and causes jarring visual "popping" artifacts during transitions, degrading the user experience. We argue that the explicit, primitive-based nature of the emerging 3D Gaussian Splatting (3DGS) technique enables a more ideal paradigm: Continuous LoD (CLoD). A CLoD approach facilitates smooth and seamless quality scaling within a single unified model, thereby circumventing the core problems of DLOD. To this end, we introduce CLoD-GS, a framework that integrates a continuous LoD mechanism directly into a 3DGS representation. Our method introduces a learnable distance-dependent decay parameter for each Gaussian primitive that dynamically adjusts its opacity based on viewpoint proximity. This allows for the progressive and smooth filtering of less significant primitives, effectively creating a continuous spectrum of detail within one model. To train this model to be robust across all distances, we introduce a virtual distance scaling mechanism with point count regularization. Our approach not only eliminates the storage overhead and visual artifacts of discrete methods but also reduces the primitive count and memory footprint of the final model. Extensive experiments demonstrate that CLoD-GS achieves smooth, quality-scalable rendering from a single model, delivering high-fidelity results across a wide range of performance targets.

Published as a conference paper at ICLR 2026
CLOD-GS:
CONTINUOUS LEVEL-OF-DETAL VIA 3D
GAUSSIAN SPLATTING
Zhigang Cheng", Luyang Tang²
Mingchao Sun?
Yu Liu
Zengye Ge Peng Pan 't
Mu Xu
Yangyan Lift
Tsinghua University
AMAP
xum.xxm@alibbbaainno com,
yangyan. www.@eeeemmll.... com, pengpangtsinghua. edu.cn
ABSTRACT
Level of Detail (LoD) is a fundamental technique in real-time computer graph- ics for managing the rendering costs of complex scenes while preserving visual fidelity. Traditionally, LoD is implemented using discrete levels (DLoD), where multiple, distinct versions of model are swapped out different distances. How- ever, this long-standing paradigm suffers from two major drawbacks: it requires significant storage for multiple model copies and causes jarring visual "popping" artifacts during transitions, degrading the user experience. We argue that the explicit, primitive-based nature of the emerging 3D Gaussian Splatting (3DGS) technique enables more ideal paradigm: Continuous LoD (CLoD). A CL.D approach facilitates smooth and seamless quality scaling within single unified model, thereby circumventing the core problems of DLOD. To this end, we in- troduce CLoD-GS, a framework that integrates a continuous LoD mechanism di- rectly into a 3DGS representation. Our method introduces Iearnable distance- dependent decay parameter for each Gaussian primitive that dynamically adjusts its opacity based on viewpoint proximity. This allows for the progressive and smooth filtering of less significant primitives, effectively creating a continuous spectrum of detail within one model. To train this model be robust across all distances, we introduce a virtual distance scaling mechanism with point count regularization. Our approach not only eliminates the storage overhead and visual artifacts of discrete methods but also reduces the primitive count and memory footprint of the final model. Extensive experiments demonstrate that CLoD-GS achieves smooth, quality-scalable scalable rendering from a single model, delivering high- fidelity results across a wide range of performance targets.
INTRODUCTION
The pursuit of photorealism in real-time computer graphies is characterized by fundamental ten- sion between ever-increasing scene complexity and the finite computational budget of rendering hardware. To maintain interactive frame rates, systems must intelligently manage the number of primitives rendered per frame, a challenge first articulated decades ago (Funkhouser & Séquin 1993). This constrained optimization problem-gennrratng the best possible image within fixed time budget- bagget-hssaaa driven the development of Level of Detail (LoD) techniques, which adaptively re- duce an object's complexity based on its perceptual importance to the viewer (Luebke et al.) 20022).
The most established paradigm for LoD is Discrete Level of Detail (DLoD). In this approach, artists or automated algorithms create multiple, distinct versions of model at varying complexities (Clark) 1976). At runtime, the system selects the appropriate version based on metrics like distance or screen-space projection size. While computationally efficient, DLoD suffers from two critical draw- backs. First, storing multiple copies of every asset leads to significant memory overhead, limiting scene scale and variety. Second, the instantancous swap between discrete models causes jarring visual "popping" artifacts, degrading the user experience (Giegl & Wimmer) 2007).
"Work done during internship at AMAP. 1 "Co-corresponding authors.

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Published as a conference paper at ICLR 2026
The recent advent of 3D Gaussian Splatting (3DGS) has revolutionized novel view synthesis, achiev- ing state-of-thh-arr visual quality at real-time rendering speeds (Kerbl et al. 2023). By representing scenes as collection of explicit 3D Gaussian primitives, 3DGS leverages a highly optimized ras- terization pipeline. However, this paradigm does not escape the fundamental constraint of rendering cost; performance still scales with the number of primitives, making LoD necessity for complex scenes. Given that 3DGS is a primitive-based representation, a straightforward approach would be to apply the traditional DLoD paradigm by creating multiple, discrete sets of Gaussians varying levels of detail (Kulhanek et al. 2025). While feasible, this strategy inevitably reintroduces the clas- sic DLoD drawbacks: significant storage overhead for maintaining multiple Gaussian clouds and the visually disruptive "popping" artifacts during transitions.
We argue that more ideal paradigm could be achieved with the unique characteristics of the 3DGS: Continuous Level of Detail (CLoD). The profound suitability of 3DGS for CLoD framework stems from its fundamental representational properties, which distinguish it from traditional discrete prim- itives like meshes point clouds. First, each Gaussian is not discrete point but continuous volumetric entity-a probability distribution with a "soft" footprint. This makes modulating its con- tribution to the scene (e.g.. via its opacity) intrinsically smooth and continuous operation. In con- trast, simplifying a mesh requires discrete topological changes like edge collapses(Hoppe [996). Second, each primitive is defined by set of continuous parameters that can be finely controlled. This allows for per-primitive filtering rather than the abrupt removal of entire geometric elements. Finally, the entire representation is end-to-end differentiable. This feature allows the LoD mecha- nism itself to be learned. We can introduce new learnable parameters that control simplification and optimize them directly within the primary training process.
To this end, we introduce CLoD-GS, a framework integrating a continuous LoD mechanism directly into the 3DGS representation. Our key contribution is to augment each Gaussian primitive with an additional learnable parameter: a distance-dependen decay factor. This parameter dynamically modulates the primitive's opacity based on its proximity to the viewpoint, allowing for the smooth filtering of less significant details, effectively creating continuous spectrum of detail within a single, unified model. To train a single model that is robust across the entire LoD spectrum, we introduce novel virtual distance scaling training strategy. This involves rendering from virtually scaled distances to activate the LoD mechanism, with a point count regularization loss that explic- itly encourages the model to learn more compact representation for distant views. Our framework eliminates the storage overhead and popping artifacts inherent to discrete methods while simultane- ously reducing the final model's primitive count. Experiments demonstrate that CLoD-GS achieves high-fidelity results across a wide range of performance targets, paving the way for more scalable and visually coherent real-time neural rendering applications.
2 RELATED WORK
LoD. The earliest and most common approach, DLoD, involves pre-generating multiple versions of a mesh at different resolutions (Clark) 1976). At runtime, the system selects an appropriate model based heuristics like distance or screen-space size (Funkhouser & Séquin, 1993). The creation of these simplified meshes spurred a rich field of research in polygonal simplification algorithms (Schroeder et al., 1992; Garland & Heckbert, 1997). DLoD's reliance on multiple asset copies leads to high storage costs and visually jarring "popping" artifacts during transitions (Luebke et al.) 2002]. Techniques were developed to mitigate popping, but often at the cost of increased render- ing complexity or other visual artifacts (Giegl & Wimmer] 2007). To address the shortcomings of DLoD, CLoD techniques were developed. The foundational work in this area is Progressive Meshes by Hoppe (1996). which represents mesh as a coarse base model plus a sequence of vertex split operations that can incrementally refine This concept was extended to View-Dependent Simplifi- cation, where the LoD can vary locally across single object's 's surface based on viewing parameters (Hoppe. 1997| Luebke & Erikson| 1997). While CLoD successfully climinates storage overhead and popping. shifts complexity from the asset pipeline the runtime algorithm, often incurring significant CPU overhead to traverse the hierarchical data structures and generate the appropriate mesh each frame (Lindstrom al. 1996).
Neural Scene Representations. The field of novel view synthesis was revolutionized by Neural Radiance Fields (NeRF), mapping 5D coordinates (position and viewing direction) to volumetric
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density and color (Mildenhall et al. 2021). In the context of NeRF, the LoD problem manifests primarily as aliasing when viewing scenes at different scales. This was addressed by |Brrron et al. (202I) in Mip-NeRF. Other works like Strata-NeRF have explored imposing discrete structures onto implicit fields, representing form of discrete Lol for NeRFs (Dhiman et al.) 2023). The develop- ment of methods like Instant-NGP accelerated NeRF training times, making these implicit methods more practical (Müller et al. 2022). To bridge the gap between the quality of implicit methods and the speed of traditional rasterization, explicit neural representations were developed. Early works like Plenoxels (Fridovich- Keil et al. 2022) and TensoRF (Chen et al., (022) discretized the scene into explicit representations, enabling much faster training and rendering. The most significant breakthrough in this area has been 3DGS (Kerbl et al. 2023). The return to explicit, primitive- based representation makes the vast body knowledge on geometric LoD directly relevant once again, creating a fertile ground for new frameworks like ours.
LoD for 3D Gaussian Splatting. Several works have adapted the DLoD philosophy to 3DGS by creating hierarchical representations. A notable example is LODGE (Kulhanek al. 2025), which creates multiple, discrete sets of Gaussians by iteratively applying smoothing filters and importance- based pruning. Hierarchical-3DD tures and streaming systems for massive-scale scenes. Similarly, Octree-GS (Ren et al.] 2025) or- ganizes the scene into octree, assigning "anchor Gaussians" different levels of the hierarchy. While these methods provide structured control over detail, they inherit the complexities of tradi- tional DLoD. They rely on rigid, explicit data structures that add algorithmic and memory overhead, and their management of discrete levels can cause popping artifacts. The objectives of these meth- ods are thus different from ours. Consequently, our method can complement these large-scale DLoD systems by offering finer- aher-grained, internal quality control for each loaded data chunk or level of the hierarchy. In the CLoD field, Milef al. (2025) propose a method that achieves continuous LoD through a separate, subsequent training phase. After an initial 3DGS model is trained, they perform an additional optimization stage where random subsets of splats are used for rendering.
(Kerbl al.) 2024) focuses designing hierarchical data struc-
3DGS Model Compression. It is important to distinguish dynamic LoD techniques from static model compression. While LoD a runtime optimization for performance scaling, compression pre-process for reducing storage and transmission costs. Recent work in 3DGS compression includes pruning- methods like LightGaussian, which permanently remove redundant Gaus- sians from trained model (Fan al., 2023), and quantization- methods like Compact3D and CompGS, which reduce the precision of Gaussian attributes using techniques like vector quantiza- tion (Lee et al.] 2024 Navaneet et al.
2024). A sophisticated pruning approach presented by
Massaain (Liu et al. (0025). which models each Gaussian as probabilistic entity with a learn- able "probability of existence" to guide a more robust pruning process. Fundamentally, these are static compression techniques designed to produce a single, smaller model.
x
Z
Σ
a'
d
f(d) a
S
S
La
4
Ls, + breg
Lsy Lyeg
Rendering quality
R Rendering quality
G Gaussian count
LoD mask
Gaussian count
Figure 1: Framework of the proposed methodology.
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3 METHODOLOGY
Our core objective to develop a single, unified 3DGS model that can dynamically and smoothly adjust its LoD at render time. This allows for seamless trade-off between visual quality and perfor- mance, catering to diverse hardware capabilities and user requirements. As illustrated in Figure [ our framework achieves this by building upon the standard 3DGS representation with three key innovations: a novel learnable parameterization for adstance-adaptive opacity, a dynamic masking mechanism driven by the viewing distance, and specialized virtual distance scaling training strat- egy to ensure robustness across all detail levels.
3.1 PRELIMINARIES: 3D GAUSSIAN SPLATTING
The standard 3DGS framework (Kerbl et al. 2023) represents 3D scene collection of explicit, anisotropic 3D Gaussian primitives. Each Gaussian is defined by a set learnable attributes: a 3D position (mean) j4g 3D covariance matrix Σ; (represented by scaling and rotation factors), base opacity 0g and coefficients for Spherical Harmonics (SH) to model view-dependent color c;- Rendering is achieved through highly optimized, differentiable rasterization process. For given viewpoint, 3D Gaussians are projected onto the 2D image plane. The final color C' for pixel is then computed by alpha-blending the projected Gaussians sorted by depth:
i-1 Ca (I IEN
where N the of Gaussians overlapping the pixel, and af is the effective 2D opacity, which is a product of the base opacity a; and the 2D Gaussian's value at the pixel center. This explicit, primitive-based and differentiable representation serves the foundation for our proposed LoD mechanism.
3.2 LEARNABLE CONTINUOUS LOD VIA DISTANCE-ADARE OPACITY
To achieve a continuous LoD, our goal is smoothly modulate the contribution of each Gaussian primitive based on its perceptual importance, which is strongly correlated with its distance from the viewer. Instead of discretely removing primitives, which causes popping, we propose dynamically attenuate their opacity, allowing them to fade out gracefully. This leverages the continuous nature of the Gaussian representation and integrates seamlessly into the alpha-blending pipeline.
To this end, we introduce single additional learnable parameter for each Gaussian primitive i: the distance decay factor da,i- This sealar parameter optimized alongside the Gaussian's 's other at- tributes and learns to control how rapidly the primitive's visibility should decrease with distance. At render time, for a given camera center c, we first compute the Euclidean distance d, = 11/l - cll for each Gaussian. To ensure the decay effect is consistent across different scenes and camera per- spectives, we normalize this distance. We define the normalized distance d' = de/ /maxxj N... (d,) ). where Nview is the set of all Gaussians within the current view frustum. We then this normalized distance, along with a user-controllable virtual distance scale factor 8., to compute the attenuated opacity a!
of = a: exp (-2 (ReLU((as))) +6)
where a; is the original learned opacity. The virtual distance scale sy allows the user simulate the effect of viewing the scene from farther away, thereby increasing the attenuation. The ReLU(-) function ensures the learned decay parameter od, remains non-negative, and is small constant for numerical stability. This formulation effectively weights the original opacity with Gaussian falloff whose variance is learned per-prinn primitiee, a functional form widely used to model distance-dependent
effects (aakikawa et al. 2021
Strugar 2009).
After computing the attenuated opacity a,", we apply a dynamic threshold to determine which prim- itives are significant enough to be sent to the rasterizer. This filtering is defined by a boolean mask M; = (a > T st), where т is a small base opacity threshold. By scaling the threshold with su. apply stricter culling criterion when simulating more distant views. Only Gaussians with M, = 1 are rendered. This simple, per-primitive computation allows for a continuous and smooth performance-quality trade-off by adjusting the single scalar 8p.
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3.3 VIRTUAL DISTANCE SCALING TRAINING FOR UNIFIED LOD MODEL
A key challenge in creating a unified LoD model is training it to perform effectively across the entire spectrum of detail, from full fidelity aggressive simplification. A model trained only on high- quality views would not learn a meaningful simplification behavior. To address this, we propose a virtual distance scaling training strategy that forces the model learn representation that robust at all viewing scales.
During each training iteration, we randomly sample virtual distance scale factor Sy from pre- defined range (e.g., (1, 10)), where U denotes uniform distribution. This strategy compels the model to optimize its parameters not only for the ground-trute -tuth camera views (where 8, 1) but also for simulated distant views (5, > 1).
However, we observed that relying solely on rendering from virtual distances was insufficient, as the model could converge to trivial solutions. We therefore introduce a more direct form of supervision through a primitive count regularization loss. This loss explicitly encourages the model to utilize fewer primitives for more distant views. To this end, we define target primitive ratio, T/arget, which inversely proportional to the virtual distance scale:
Marget = 1/s..5
(3)
The exponent 1.5 is an empirically determined value that controls the rate of geometric simplifica- tion. We then formulate regularization loss, Leeg which penalizes the model if the actual ratio of rendered primitives, Macttal, exceeds this target:
Lieg (s, 1.0)² (ReLU(Taacual Tharget)
(4)
where Tactual (E, Mooal Here, Noaa the total number of primitives in the scene, and M; E 10 } is binary mask indicating whether the i-th primitive is rendered. The quadratic weight term (s, - 1.0) ensures that this penalty is applied only simulated distant views (8p 1) and that its magnitude increases with the virtual distance.
The final training objective combines the standard 3DGS rendering loss, Lrender (a weighted sum of LI and D-SSIM losses), with our regularization term:
Liotl = W. (Irenter + Areg Ireg).
(5)
Here, the weight w. = (1-0..-... (x.)) 2 is designed to apply weaker supervision to renderings from larger virtual distances. This prevents the model from over-pruning primitives at the expense of rendering quality. The hyperparameter Areg balances the trade-off between reconstruction fidelity and the sparsity constraint. This comprehensive training strategy equips the model with the ability to learn an efficient, view-dependent representation, thereby enabling a single, robust model with controllable and continuous Level-of- -etail (LoD) capabilities.
4 EXPERIMENTS
We conduct series of comprehensive experiments to validate the effectiveness of our CLoD-GS framework. We first detail the experimental setup, then present quantitative and qualitative compar- isons against of-t-oof-th-arr methods, and finally provide in-depth ablation and robustness studies.
4.1 EXPERIMENTAL SETUP
Datasets. Our evaluations are performed on real-world scenes from three challenging public datasets: the BungeeNeRF dataset (Xiangli et al. 2022) (8 scenes), the Tanks and Temples dataset (napitsch et al. 2017) (2 scenes), the Deep Blending dataset (Hedman et al. 2018) (2 scenes) and the MipNeRF360 dataset (Barron al. (022). For all experiments, follow the original 3DGS train/test split to ensure fair comparisons.
Evaluation Metrics. We use three standard metrics for novel view synthesis: Peak Signal-to-Noo Ratio (PSNR), Structural Similarity Index (SSIM) (Wang et al.) 22044,, Learned Perceptual Image Patch Similarity (LPIPS) (Zhang et al. 2018) and Frames Per Second (FPS).
Implementation Details, Our framework built on the official 3DGS implementation. All exper- iments are run Ubuntu server with four NVIDIA RTX 4090 GPUs. We train models for
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30,000 iterations and enable the proposed mechanism since 5000 iterations. The learning rate for our learnable distance decay factor dd,, is 1e-2, and the weight for the regularization loss Avg is set to 1.0. Notably, our method utilizes the same set of hyperparameters actoss all datasets. The scale parameter, tested at values of 1, 3, 5 and 7, defines the maximum allowable value for the 8y during training. For experiments, both 3DGS and MaskGaussian were implemented on the latest public 3DGS codebase. Unless otherwise specified, MaskGaussian uses the 'beta' settings from its original paper (Liu et al. 2025).
Compared Methods. We compare CLoD- GS against several leading methods: 3DGS
Table 1:
Quantitative comparison of highest- quality models. Best results are bold, second best underlined. The fifth and sixth columns indi- cate the number of Gaussian primitives (#GS) and memory consumption (Mem). 't' indicates that lower is better.
(Kerbl et al.] 2023):: The original method, serving as the high-quality, high-cost base- line. Fast Rendering (Milef et al. 2025): A state-of-the-ar continuous Lol method based on learning static importance ranking for splats, Octree-GS (Ren et al.] 2025) and H-
3DGS (Kerbl et al. 2024): State-of-the thrt dis-
crete LoD methods using hierarchical struc- tures. MaskGaussian (Liu al., (025):: A state-of-the-art static compression method that uses probabilistic masks for robust pruning. For Fast Rendering and Octree-GS, we report the metries from their respective papers. All other methods were trained locally under identical conditions for a fair comparison.
Method
PSNR
SSIM LPIPS
#GSIK)
MemrMB)
DungenNeRF
SIGGS
01,096
Rendering
1592.45
Octree-GS
MackGanssian
1253.13
Ours (scale=1)
28.05
0.919
0.100
4185
1005,87
Ours (scale=3)
27.70
0.908
0.117
2738
658.01
Ours (scalo=")
27.09
0.885
0.150
1855
445.72
Junka J
SDGS
23.70
0,853
0,169
1574
372.19
Fist Rendering
23.62
0.853
372.19
OctreeGG
24.17
0L855
385.00
MaskGaussian
23.56
292.65
4.2 RESULTS AND COMPARISONS
H-3DGS (==0)
21.71
0.820
0.200
H-3DGS (rw3)
21.77
0.821
0.200
H-3DXS (--6)
21.76
0.818
0.2006
Table and Table summarize the performance of all methods at their highest quality settings.
II-3DGS (==15)
21.55
0,800
0.239
Ours (scale=1) 1)
23.75
0.843
0.185
1159
278.53
Ours (scalom3)
23.79
984
236
(scalo=?)
884
212.54
Our CLoD-GS consistently achieves higher rendering quality and speed, often surpassing the original 3DGS in PSNR and SSIM while using significantly fewer Gaussians (e.g., a 38% reduction on BungeeNeRF). This demonstrates that our regularization strategy inherently pro- duces a more compact and efficient represen- tation. While Octree-GS achieves the high- est compression ratios, it introduces consider- able rendering overhead for smaller scenes and not natively supported by existing renderers.
Deep Rlroding
ADGS
587.95
Fist Rendering
29.00
587.95
Octree
29.55
0.901
0.257
180.00 00
MackGanssian
29.50 66
0.907
0.244
1778
420.41
H. 3DGS (r40))
27.41
0.857
0.254
H-SDGS (--3)
27.40
0.857
01.54
II 113DGS (ra6)
27.38
0.857
0.255
H-3DGS
(scale=1)
29.93
407.72
(scale-3)
29.95
1258
302.27
Ours (scale=7)
29.54
0.907
0.251
662
159.04
The superior performance of our method is par- ticularly pronounced on datasets with large depth variations and significant focal length changes, such BungeeNeRF. This highlights the effectiveness of our designed LoD mechanism in handling complex, multi-scale scenes.
As shown in Figure 2 our method produces clearer results at similar primitive counts, par- ticularly in areas with repetitive textures or complex lighting, while often using fewer Gaussians. Figure 3 and Figure 4 illlstratee the trade-off between rendering quality and speed as a function of the number of Gaussians. For a fair comparison, the points for 3DGS and MaskGaus are also selected by applying our opacity attenuation formula (Equation 3) to their trained models and selecting the prim- itives with the highest resulting opacities. The curves for CLoD-GS show that increasing the virtual distance scale range during training (c.g., from 8o E [1,3] to 8, (1)5)) produces models that more robust. to simplification. Notably, this
Table 2: Comparison of FPS on various datasets. Best results are bold, second best are underlined. Higher is better (t).
Methed
Tanko lemplo:
Deep Blending
Mip-Nelli JN0
Outie-G5 GS
26.06
134.57
141.5)
120.5
H-5008 ( -0)
43.30
35.30
n-m022 (ra))
01.51
41:57
nemies (ea
7533
51/13
Okn ocalecll
136.44
145.3
12535
Ous (sell=))
$7.08
19112
187.48
141.1

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3DGS
MeekGaussien
Ours (ecele-1)
Ours (scale-3)
Chicago
A.99
2
1294
424
DOTAA
100%
NORA
17
SASTA
Playtoom
Train
Figure 2: Visual comparison at similar primitive counts. The number of Gaussians used and the corresponding PSNR are annotated in the bottom-right corner of each image.
Bungeenert
Deep Blending
Radouni Drced
Figure 3: Quality primitive count the BungeeNeRF and Deep Blending datasets. The dashed lines indicate the maximum virtual scale during training. Our method with varying virtual distance scale ranges (s.) shows more graceful quality degradation.
improved low-detail performance is achieved while maintaining nearly identical peak quality, con- firming our method's ability to create single, versatile model that performs well across the entire
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perfote spectrum. Ultimately, when pruning Gaussians to enhance FPS, method consistently achieves higher rendering quality.
FPS vu Point count (Bungwaree))
PSNR va FRS (Bungweree))
furiore de
0
Figure 4: Relationship between FPS, the number of Gaussians, and rendering quality. As shown, rendering speed exhibits strong negative correlation with the number of Gaussians. Reducing the number of Gaussians boosts FPS for all methods, with our method showing a more significant increase.
To directly compare our continuous LoD (CLoD) ap- proach against traditional discrete LoD (DLoD) strat- egy, we designed a specific experiment. As shown in Figure|5] we divide the rendered image into four vertical regions. For the DLoD approach, we train two separate models: a high-quality baseline and low-quality com- pressed model (using MaskGaussian with An 0.02). We render the two left regions with the low-quality model and the two right regions with the high-quality model, simulating a hard switch between LoD levels. For our CLoD approach, we use single trained model and ren- der the four regions with progressively increasing detail by setting the scale factor Su, respectively, while keep- ing the number of rendered Gaussians comparable to the DLoD setup. The visual results clearly show the draw- back of the DLoD strategy: a prominent "popping" arti- fact is visible the boundary between the two models (indicated by the red dashed line), where the quality changes abruptly. In contrast, our CLoD approach provides smooth, continuous transition across the regions. This is further quantified in Figure 6 where the metric curves for DLoD show a sharp jump at the boundary, while our CLob method exhibits a smooth, gradual change. Impor- tantly, our CLoD approach also more efficient, requiring the training of only one model, which typically takes half the time needed to train the two models required for the DLoD setup.
Table 3: Ablation study on our key
training components. The full model outperforms all ablated versions.
Method
PSNR
SSIM
LPIPS 1.
BungeeNeRF
Full Model (Ours)
27.59
0,902
0.123
weight (1c,)
27.39
0.127
w/o loss (Lng)
27.56
0.902
0.123
weight
26.71
0.871
0.169
Deep Blending
Full Model (Ours)
29.76
0.908
0.245
who weight (vc,)
29.58
0.906
0.246
w/o loss (Zang
29.72
0.9066
0.246
who weight & loss
29.57
0.905
0.252
4.3 ABLATION STUDIES
To validate our design choices, we conduct ablation studies on the key components of our training strategy, with all models trained using maximum virtual scale of s. 5. As shown in Table removing any of these components degrades performance. We analyze the effects of the regulariza- tion loss (Leeg) and adaptive weight (w,). The results confirm that all three components are crucial for achieving optimal performance. The full model consistently outperforms the ablated versions, demonstrating that the combination of our regularization loss, its adaptive weighting, and the multi- scale training approach is essential for learning robust and efficient LoD representation.
4.4 ROBUSTNESS ANALYSIS AND EFFECTIVENESS ANALYSIS
To demonstrate the robustness and generality of our method, we apply our CLoD-GS training strat- egy to model pre-trained with MaskGaussian. As shown in Figure 7 our method successfully imparts continuous LoD capabilities onto the already compressed MaskGaussian model. The re- sulting quality-compression curves show similar trend: increasing the virtual distance scale range
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GT
MaskGaussian
Ours (scale=3)
Ours (scale=5)
Ours (scale=7)
Amsterdam
Barcelona
Quebec
Figure 5: Visual comparison of DLoD vs. CLoD strategies. The DLoD approach (the second column) uses two separate models, causing visible quality jump the boundary (red dashed line). Our CLoD approach (the left three columns) uses single model with varying scale factors, resulting smooth, artifact-free transition.
Amsterdam
Barcelona
Quebec
0.54
0.51
0.34
0.55
0.91
SSIM
0.00
0.85
MadiGaumian
Cura (ocle-3)
041% Scr
Dart (sralen))
(trale-))
0.00
Da's Scalce5)
Durs (scalo)))
boolow7)
bacale?))
Figure 6: Metric curves for the DLoD vs. CLoD comparison. The DLoD strategy exhibits a sharp, discontinuous jump in quality, whereas our CLoD strategy shows a smooth progression.
SSIM
1.2
Rcall Omae
Paints Manyy
Figure 7: Robustness analysis. Our CLoD-GS training strategy is applied a MaskGaussian model on Bungeenerf dataset, successfully enabling continuous LoD on compressed representation.

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Published as a conference paper at ICLR 2026
improves the model's ability gracefully handle simplification. This demonstrates that our CLoD mechanism is an orthogonal enhancement that can be effectively combined with state-of-the-an compression techniques.
To validate the mechanism of our proposed method, we analyze the distributions of opacity and the learned decay factors (d,,)) for the Amsterdam scene from the BungeeNeRF dataset, as depicted in Figure X The analysis empirically demonstrates that the virtual distance range during training is the governing factor for da.i We observe that a wider distance range encourages distribution of aa, skewed towards larger values. Primitives with larger od,i values exhibit slower opacity decay, thus remaining visible at greater virtual distances. This confirms that our model automatically learns to assign higher persistence to perceptually important primitives without requiring any manual super- vision. Our method adds only one additional float parameter per Gaussian. In standard 3DGS implementation, each Gaussian requires approximately 248 bytes of storage, an increase of only 1.6%, which an entirely acceptable overhead.
Amaterdam of Bungsenerf
Quscity Probuisiity Dousity Parctiin
Opaaity Cursulative Ditribation Panction
Donay Facter Probuldiiy Deraity Farction
Decay Pactor Cursdative Datribation Parction
Dacty Factor Value
Oacay Focter
Figure 8: Distribution analysis of opacity and decay factor da, for various models in the Amsterdam scene. The figure plots their respective PDFs and CDFs.
CONCLUSION
In this paper, introduced CLoD-GS, novel framework that seamlessly integrates CLoD mech- anism into the 3DGS representation. We identified the core limitations of applying traditional DLoD paradigms to 3DGS-namely -nmelly prohibitive storage overhead and jarring visual "popping" artifacts. Our core contribution is a lightweight, learnable mechanism that augments each Gaussian primi- tive with distance-dependent decay parameter. This parameter, optimized directly within novel virtual distance scaling training strategy, allows each primitive to learn its own view-dependent sim- plification behavior. The resulting model contains a continuous spectrum of detail within a single, unified representation, enabling smooth, pop-free quality scaling. Our experiments have shown that CLoD-GS not only eliminates the fundamental drawbacks of DLoD but also achieves high render- ing quality, often with more compact set of primitives than the baseline 3DGS. While our method demonstrates significant advantages for static scenes, future work could explore the integration of more sophisticated perceptual metrics beyond distance to guide the learned decay, or hybrid sys- tems that combine our continuous per-primitive scaling with chunk-based loading for rendering massive-scale environments. Ultimately. CLoD-GS represents significant step towards creating truly scalable, efficient, and visually coherent neural scene representations, paving the way for the next generation of real-time, high-fidelity graphics applications.
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6 ACKNOWLEDGEMENT
This study was supported by National Key Research and Development Program of China (202YFC3803000), and the Xplorer Prize.
REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our research. To this end, we provide com- prehensive resources to facilitate the verification of our findings. All experiments were conducted on publicly available datasets, ensuring that the data is accessible to the broader research commu- nity. The detailed experimental setup, including all hyperparameters and training configurations, is described in Section of the main paper.
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Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, and Dahua Lin. Bungeenerf: Progressive neural radiance field for extreme multi-scale scene rendering. In European conference on computer vision, pp. 122. Springer, 2022.
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A RENDERING RESULT ON MIPNERF360 DATASET
We have added experiments on the challenging Mip-NeRF 360 dataset. As shown in the Table without any dataset- -ataset-specifie tuning, CLoD-GS demonstrates competitive trade-off between quality and memory. We acknowledge that the results are not state-of-the-ar on this specific dataset, which attribute
of universal parameters that scenes. However, this finding underscores a key strength of our method: generality. In contrast, other methods like Octree-GS often require dataset-specific configurations, as evidenced by the separate training scripts provided in its official repository for different datasets.
optimized for these special
Table 4: Quantitative comparison of different methods on the MipNeRF360 dataset. The arrows indicate whether higher (f) or lower (1) values are better.
Method
PSNR1
SSIM
LPIPS
#GS (k)
Memory (MB)
3DGS
27.59
0.8136
0.2206
2638.53
624.04
Fast Rendering
27.58
0.8240
0.2420
2638.53
624.04
Octree-GS
27.65
0.8150
0.2200
418.60
Mask Gaussian
27.23
0.8035
0,2253
2145.10
507.34
Ours (scale=1)
27.01
0,8069
0,2299
2414.38
580.24
Ours (scale=3)
26.99
0.8042
0.2361
1778.10
427.32
Ours (scale=7)
26.71
0.7968
0.2494
1361.49
327.20
B DETAILED METRIC TABLES
This section provides a comprehensive, scene-by-scene breakdown of the performance metrics for the methods evaluated in our study. These tables offer granular view of the results that are summa- rized in the main body of the paper, detailing the PSNR, SSIM, LPIPS, and Gaussian count for our method, the original 3DGS, MaskGaussian, and the ablation study configurations across all tested datasets.
C DETAILED METRIC CURVES
This section presents the detailed in the BungeeNeRF dataset. The following figures illustrate the trade-off between PSNR and the number of rendered primitives for both the original 3DGS and the MaskGaussian methods, providing visual complement to the quantitative data in the preceding tables.
curves for each of the eight scenes
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Table 5: ours (scale=1)
Dataset
PSNRT
SSIMT
LPIPS
#GS (M)
BungeeNeRF
amsterdam
27.78
0.911
0.108
3.89
barcelona
27.84
0.922
0.085
4.91
bilbao
29.02
0,919
0.101
3.63
chicago
28.62
0.934
0.087
3,50
hollywood
26.53
0,880
0.138
4.58
pompidou
27.37
0,920
0.094
5.14
quebec
29.03
0.938
0.092
3.75
rome
28.23
0,925
0.098
4.08
Deep Blending
drjohnson playroom
29.47
0,905
0.237 0.241
2.07
30.39
0.911
1.33
Tanks & Temples
train truck
0.806 25.35
22. 14
0.220 0.150
0.57
0.880
1.75
Table 6: Mask Gaussian
Dataset
PSNRT
SSIMI
LPIPS]
#GS (M)
BungeeNeRF
amsterdam
27.69
0.916
0.100
4.69
barcelona
27.49
0.919
0.085
6.62
bilbao
28.86
0,919
0.097
4.28
chicago
28.03
0.931
0.085
4.75
hollywood
26.35
0.873
0.133
5.62
pompidou
27.13
0.920
0.093
6.83
quebec
28.84
0.936
0.092
4.44
rome
27.66
0.918
0.100
5.17
Deep Blending
drjohnson playroom
29.21
0.904 0.910
0.243 0.244
2.34
30.12
1.22
Tanks & Temples
train truck
21.80
0.811 0.881
0.212
0.88
25.31
0.149
1.60
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Table 7: Original 3DGS
Dataset
PSNR
SSIMT
LPIPS
#GS (M)
BungeeNeRF
amsterdam
27.85
0.918
0.096
6.24
barcelona
27.67
0.920
0.083
8.18
bilbao
28.98
0,918
0.095
5,49
chicago
28.54
0.933
0.080
6.08
hollywood
26.24
0.868
0.135
6.79
pompidou
27.26
0,921
0.091
8.64
quebec
28.95
0.937
0.089
5.91
rome
27.77
0.920
0.096
6.55
Deep Blending
drjohnson playroom
29.43
0,905
0.236
3.12
30.25
0.909
0.240
1.85
Tanks & Temples
train truck
21.97
0.821 0.885
0.197
1.09
25.44
0.142
2.06
Table 8: Full Model (scale=5)
Dataset
PSNRT
SSIM1
LPIPS
#GS (M)
BungeeNeRF
amsterdam
27.15
0.889
0.137
2.37
barcelona
26.99
0.886
0.123
2.78
bilbao
28.62
0.905
0.122
2.22
chicago
28.01
0.917
0.108
2.30
hollywood
26.75
0.878
0.147
3.34
pompidou
26.85
0.902
0.118
2.92
quebec
28.89
0.932
0.105
2.53
rome
27.43
0.903
0.127
2.16
Deep Blending
drjohnson playroom
29.32
0.906 0.909
0.241 0.248
1.16
30.20
0.86
Tanks & Temples
train truck
21.99
0.806 0.880
0.219
0.59
25.29
0.148
1.46
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Table Without Weight Adaptation (scale=5)
Dataset
PSNR
SSIMI
LPIPS
#GS (M)
BungeeNeRF
amsterdam
26.82
0.876
0.146
2.34
barcelona
26.33
0.863
0.138
3.11
bilbao
28.27
0.896
0.128
2.07
chicago
27.82
0.913
0.111
2.17
hollywood
26.75
0,882
0.140
3.65
pompidou
26.86
0.895
0.121
3.10
quebec
28.90
0.933
0.103
2.40
rome
27.32
0.895
0.130
2.29
Deep Blending
drjohnson playroom
29.15
0.905
0.242 0.250
1.12
30.01
0.907
0.92
Tanks & Temples
train truck
22.02 25.26
0.809 0.881
0.216 0.147
0.63
1.46
Table Without Regularization (scale=5)
Dataset
PSNRT
SSIM1
LPIPS
#GS (M)
BungeeNeRF
amsterdam
27.09
0.888
0.138
2.37
barcelona
27.10
0.893
0.119
2.85
bilbao
28.51
0.906
0.121
2.29
chicago
27.98
0.917
0.108
2.29
hollywood
26.67
0.878
0.147
3.42
pompidou
26.95
0.906
0.116
2.91
quebec
28.86
0.932
0.105
2.56
rome
27.34
0.899
0.129
2.14
Deep Blending
drjohnson playroom
29.28
0.906 0.907
0.242 0.250
1.12
30.17
0.93
Tanks & Temples
train truck
22.10
0.808 0.880
0.218
0.60
25.22
0.149
1.38
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Table 11: Without Weight & Regularization (scale=5)
Dataset
PSNRT
SSIMT
LPIPS
#GS (M)
BungeeNeRF
amsterdam
25.93
0.848
0.193
1.03
barcelona
26.04
0.849
0.171
1.37
bilbao
27.97
0.884
0.158
1.06
chicago
27.18
0.893
0.147
0.81
hollywood
26.02
0.843
0.208
1,46
pompidou
26.08
0.877
0.153
1.37
quebec
28.05
0,912
0.140
1.06
rome
26.46
0.866
0.179
0.97
Deep Blending
drjohnson playroom
29.31
0.905 0.904
0.248 0.257
0.94
29.84
0.63
Tanks & Temples
train truck
22.30 25.21
0.800 0.876
0.236 0.158
0.38
1.09
PSNR
SSIM
LPIPS
0.54
32
0.22
0.50
Amsterddam
0.00
NealGemiknn
0.38
Oet Ita
Oun Hose
0.30
Dur
Jurt ace
B/ck Videe delt Rigeo
1iio indea lirit (ight)
0.18
Dus booker)
Dun (uolen
0.55
Barcelona
0.00
0.91
0.12
Ioalow1
Oka bee exc
0.50
Oart Pesticlll
0.54
Durt
1500 inde luch hoght)
Ous (oile-3)
0.54
0.50
32-
052
Quebec
0.53
20
0.55
(noloo3)
0.30
Dura e-d)
Dard (ookow
Dar'
Blociinen to Right
pit inde. Left Rien
Blook Inder Luch Fight)
Figure 9: All metric curves for the DLoD vs. CLoD comparison.
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PSNR Points Count bungeenerf a mmsterdam
27.5
25.0
22.5
20.0
PSNR
17.5
15.0
3X3 |[ecay!
12.5
MaskGaussian (Decay)
Ours/call=-3)
0u/S/scal=55)
10.0
Oursiscale=))
Qus//call-100 10)
Points Count (Many Few)
Figure 10: 3DGS: PSNR vs. primitive count on BungeeNeRF amsterdam dataset.
PSNR vs Points Count bungeenert barcelloa
27.5
22.5
PSNR
17.5
15.0
3035 [Decay]
MaskGaussibn (Decay)
10.0
Ours/saal==))
Oustssale=55
Ours/scale=))
Qurs/ccale-10 10)
Points Count (Many Few)
Figure 11: 3DGS: PSNR vs. primitive count on the BungeeNeRF barcelona dataset.
D USE OF LLMs
During the preparation of this manuscript, we utilized the Large Language Model (LLM) Gemini 2.5 Pro"] developed by Google. Its role was strictly limited to that of writing assistant. Specifically, the model was employed for proofreading and copy-editing improve the grammatical accuracy, clarity, and overall readability of the text. The LLM was not used for generating core research ideas, developing the methodology, conducting experiments, analyzing results, drawing scientific
hhups://emminiggogle.com//
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PSNR Points Count bungeenerf bilbao
PSNR
[Decay]
MaskGsussien (Decay) Ours/scale-1) Ours(scale=3) Ourstoclle-S) Ourstsclle-10)
Points (Many
1c6
Figure 12: 3DGS: PSNR vs. primitive count on the BungeeNeRF bilbao dataset.
PSNR vs Points Count bungeenerf
27.5
20.0
17.5
MaskGaussian (Decay)
12.5
Ours/scale=1)
Oursicale==3
Ours:scale-5)
10.0
Ours/ccale=))
Points Count (Many Few)
leb
Figure 13: 3DGS: PSNR primitive count on the BungeeNeRF chicago dataset.
conclusions. All intellectual contributions, including the concepts, experiments, and conclusions presented in this paper, are solely the work of the human authors.
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Points bungeenert bungeenerf_hollywood hollywood
(dB)
30G5 [Decay]
MaskGaussian (Decay)
0urs/ccal==1)
Ou/Siscale-3)
Ourstscale-3)
Ours/callo=)) 7)
Oursicale==00
Points Count (Many Few)
Figure 14: 3DGS: PSNR vs. primitive count on the BungeeNeRF hollywood dataset.
(dB)
20
3DGS [Decay]
MaskGaussian (Decay) Ours(scale=1)
Ours/scal=-3
Ourstscolo-7)
16)
Points Count (Many - Few)
Figure 15: 3DGS: PSNR vs. primitive count on the BungeeNeRF pompidou dataset.
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Count bungeenerf, quebec
27.5
17.5
15.0
30G5 [Decay]
MaskGaussian (Decay)
Ours/scal==1)
12.5
Ou/Siscale-3)
Ourstscale-3)
Ours/ccllo=7)
0ursisalee=10
10.0
Points Count (Many Few)
Figure 16: 3DGS: PSNR vs. primitive count on the BungeeNeRF quebec dataset.
PSNR Points Count bungeenerf_comm rome
27.5
22.5
17.5
3DGS [Decay]
12.5
MaskGaussian Ours/scale=1)
OusS:scal=-3)
Oursiscale=5)
Ours/scal==))
Points Count (Many Few)
Figure 17: 3DGS: PSNR vs. primitive count on the BungeeNeRF rome dataset.
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Count bungeenerf sungeenerf_amsterdam
28
24
MaskGaussian (Decay)
MaskGaussienbccale:7)
Points Count (Many + Few)
Figure 18: MaskGaussian: PSNR vs. primitive count on the BungeeNeRF amsterdam dataset.
Points Count
bungeenerf barcelona
(dB)
22
20
MaskGaussian (Decay)
Our MaskGuussen/scllas-
MaskGenssansscalee)))
Points Count (Many - Few)
Figure 19; MaskSaussia PSNR vs. primitive count on the BungeeNeRF barcelona dataset.
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bungeenerf bilbao
(dB)
MaskGaussian (Decay)
MaskGnussenkscalee-)
MaskGuussan/cclas-55)
Our MaskGnussänkscalee))
4.0
3.5
Points Count (Many + Few)
Figure 20: MaskGaussian: PSNR vs. primitive count on the BungeeNeRF bilbao dataset.
PSNR Points Count
bungeenerf chicago
24
22
PSWR
MaskGaussian (Decay]
Our (ask)aussson/ccalee
4.0
Points Count (Many - Few)
Figure 21: MaskGaussian: PSNR vs. primitive count on the BungeeNeRF chicago dataset.
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bungeenerf_ hoolyyooddd
(dB)
20
MaskGaussan (Decay)
MaskGhussansccle=33
Our Maskianssan(/allee
MaskGaursen/scale=7)
Points Count (Many - Few)
Figure 22: MaskGaussian: PSNR vs. primitive count on the BungeeNeRF hollywood dataset.
Points Count bungeenerf pommpidouu
24
(dB)
MaskGaussian (Decay)
Our MaskGuussen/scllas-
Mark(inssanbscatee)))
Points Count (Many - Few)
Figure 23: MaskGaussian: PSNR vs. primitive count on the BungecNeRF pompidou dataset.
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PSNR Points Count bungeenerf quebec
26
(dB)
MaskGaussin (Decay)
Our MaskKaanssan(/aaleee)
MaskGaussen/scale=7))
Points Count (Many + Few)
Figure 24: MaskGausiinn: PSNR vs. primitive count on the BungeeNeRF quebec dataset.
PSNR Points bungeenerf rome
24
(dB)
MaskGaussian (Decay)
MeskGeussinkceele=))) Our MaskGuussen/sclla--
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Points Count (Many - Few)
Figure 25: MaskGaussian: PSNR vs. primitive count on the BungeeNeRF rome dataset.
25


Reviews:
[{"review_id": "i6fDryptdr", "reviewer": "Reviewer_cpAX", "summary": "The paper presents a simple yet effective way to make 3D GS models smoothly adjust their rendering quality without switching between discrete versions. Traditional discrete lod methods require multiple stored models, leading to high memory use and visible popping when changing between levels. The paper overcomes this by introducing a learnable distance-based opacity control that allows each Gaussian to fade smoothly as the camera moves farther away. Combined with a coarse-to-fine training process that encourages the model to use fewer points at larger viewing distances, this approach produces a single unified model capable of continuous, artifact-free LoD scaling. Experiments on several public datasets show that CLoD-GS achieves comparable or better visual quality than existing methods, while using about 30–40% fewer Gaussians and memory. The method adds minimal computational cost, integrates easily into existing pipelines, and provides high-quality, scalable rendering results.", "questions": "Same as weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces a simple, learnable distance-based opacity mechanism that enables continuous LoD in 3D Gaussian Splatting without adding complex structures or large overhead.\n- Experiments show consistent improvement in visual quality and memory efficiency across multiple datasets and methods.\n- The paper is well written, provides detailed implementation settings, ablation studies, and public datasets, making the work easy to understand and verify.", "weaknesses": "- The paper lacks deeper analysis of why the proposed opacity function and training design work optimally or how parameters affect convergence and quality.\n- Experiments focus only on static scenes. How about generalization to dynamic or large-scale environments remains unexplored?\n- Only briefly mentions similar approaches without in-depth experimental or conceptual comparison.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Y2ZrLtvpTa", "reviewer": "Reviewer_Uu5C", "summary": "The paper introduces CLoD-GS, which augments each 3D Gaussian with a learnable distance-decay parameter to modulate opacity continuously as viewpoint distance changes. This creates a single representation that supports smooth quality–performance trade-offs at test time. A complementary training strategies and regularizers are incorporated for effective learning.", "questions": "1. Does the distance-driven opacity exhibit hole artifacts under aggressive thresholds or grazing views? Some visual or quantitative results could demonstrate the generalizability of the proposed method.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The proposed distance-adaptive opacity gives a smooth transition within a single representation, avoiding multi-copy asset storage and popping artifacts of DLoD.\n2. The virtual distance scale offers a continuous speed-quality curve, which is practical for real-time budegets.\n3. This paper is well-written and easy to follow.", "weaknesses": "1. Prior efforts have designed sophisticated LoD for 3DGS [A], which are very effective even in large-scale scenes; a direct, side-by-side comparison with [A] is missing and would strengthen the empirical case and clarify trade-offs.\n2. The effectiveness of distance-driven opacity in regions with high-frequency textures or potential aliasing—central issues in LoD—is not convincingly demonstrated.\n3. Because opacity modulation retains the finest Gaussians and culls softly, rasterization workload may remain high. A report of FPS and visible-splat counts may be included to demonstrate the efficiency of the proposed method.\n4. Statements such as \"treating preliminary 3DGS as the key innovation\" and \"consistently achieves state-of-the-art rendering quality\" appear stronger than the presented evidence supports.\n\n[A] A hierarchical 3d gaussian representation for real-time rendering of very large datasets. [SIGGRAPH 24]", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XaJftD0cNH", "reviewer": "Reviewer_XUcB", "summary": "This paper proposes CLoD-GS, a continuous level-of-detail (CLoD) mechanism embedded directly into the 3D Gaussian Splatting (3DGS) representation. Instead of maintaining multiple discrete LoD models (which incurs storage overhead and produces visual \"popping\" during switching), this work introduces a learnable distance-dependent decay factor for each Gaussian, allowing opacities to vary smoothly with viewpoint distance.", "questions": "1. Could semantic or frequency-based saliency be introduced alongside distance? Would this improve LoD behavior in highly textured scenes?\n\n2. How stable is the learned decay factor across camera trajectory distributions? Does it overfit camera placement biases?\n\n3. Could this be extended to dynamic scenes where Gaussian parameters evolve over time?\n\n4. The opacity threshold scaling seems hand-tuned. Have you explored adaptive thresholds learned per scene?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Continuous control without model duplication, avoids DLoD storage and popping artifacts.\n\n2. Minimal parameter overhead (~1.6% per Gaussian), practical for deployment.\n\n3. Unified training strategy that naturally encourages Gaussian sparsity at distant views.", "weaknesses": "1. The method assumes that viewing distance is the dominant factor in determining perceptual relevance. However, perceptual saliency often depends on texture frequency, geometric edges, semantic relevance, and shading sensitivity, not merely spatial distance.\n\n2. The paper does not provide sensitivity analyses showing whether these parameters generalize across scene types.\n\n3. Since the decay factor is learned independently for each Gaussian, the simplification behavior lacks global structural organization.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uT51gnuC0R", "reviewer": "Reviewer_yMt5", "summary": "This paper presents CLOD-GS, a continuous level-of-detail framework for 3D Gaussian Splatting that enables smooth, pop-free quality scaling within a single model. Each Gaussian learns a distance-dependent opacity decay, allowing fine-to-coarse detail control without discrete model swaps. A coarse-to-fine training strategy with virtual distance scaling and sparsity regularization ensures robustness across viewing scales. CLOD-GS reduces Gaussian count and memory while maintaining or improving rendering quality on benchmarks like BungeeNeRF and Tanks & Temples.", "questions": "please see the weakness above.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The LoD topic is meaningful.\n2. The paper is well written and clearly organized.", "weaknesses": "**Major**\n\n1. The coarse-to-fine training strategy is similar to that used in Octree-GS.\n2. No demo has been submitted; therefore, the performance of the proposed method cannot be effectively demonstrated.\n3. This is an LoD paper, which should be related to large-scale reconstruction for the topic to be meaningful. However, the paper only conducts experiments on small-scale scenes, and the reported metrics are similar to those of the baselines. I believe these results could easily be surpassed by tuning the hyperparameters. Also, I thinks the authors should compare with hierarchical-3dgs and more lod baselines.\n\n**Minor**\n\n1. The paper lacks references to key related work on Level of Detail (LoD) rendering. The authors should consider citing the following works:\n    - *Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian*\n    - *A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets*\n    - *Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes*\n    - *Virtualized 3D Gaussians: Flexible Cluster-based Level-of-Detail System for Real-Time Rendering of Composed Scenes*", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)