Optimized Minimal 3D Gaussian Splatting
Authors: Joo Chan Lee
Jong Hwan Ko
Eunbyung Park
Venue: NeurIPS 2025 poster ()
Keywords: 3D Gaussian Splatting
Efficient Neural Rendering
Primary area: applications

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time, high-performance rendering, enabling a wide range of applications. However, representing 3D scenes with numerous explicit Gaussian primitives imposes significant storage and memory overhead. Recent studies have shown that high-quality rendering can be achieved with a substantially reduced number of Gaussians when represented with high-precision attributes. Nevertheless, existing 3DGS compression methods still rely on a relatively large number of Gaussians, focusing primarily on attribute compression. This is because a smaller set of Gaussians becomes increasingly sensitive to lossy attribute compression, leading to severe quality degradation. Since the number of Gaussians is directly tied to computational costs, it is essential to reduce the number of Gaussians effectively rather than only optimizing storage. In this paper, we propose Optimized Minimal Gaussians representation (OMG), which significantly reduces storage while using a minimal number of primitives. First, we determine the distinct Gaussian from the near ones, minimizing redundancy without sacrificing quality. Second, we propose a compact and precise attribute representation that efficiently captures both continuity and irregularity among primitives. Additionally, we propose a sub-vector quantization technique for improved irregularity representation, maintaining fast training with a negligible codebook size. Extensive experiments demonstrate that OMG reduces storage requirements by nearly 50% compared to the previous state-of-the-art and enables 600+ FPS rendering while maintaining high rendering quality. Our source code is available at https://maincold2.github.io/omg/.

Optimized Minimal 3D Gaussian Splatting
Joo Chan Lee Sungkyunkwan University Suwon, South Korea maincold2@skku.. edu
Jong Hwan Ko Sungkyunkwan University Suwon, South Korea jhko@skku. edu
Eunbyung Park" Yonsei University Seoul, South Korea epark@yonsei. ac kr
3DGS
Mini-Splattifi
Rate- Dostoititotiinn Curve with Rendering Speed
27.5
FPs Scale
OMG-XL
LoCoGS-L
20D rrs
PSNR 25.08 PG 5. 5.7M 885
PSNR 24 MB
OMG-M
270
300 FPS
(5.0108)) (108) FPS
BG 881K (382 (632) FPS
27.2
OMG-XS (Outs)
OMG-L (Ours)
27.1
288
C3DGS
EAGLES
LocoGS
CompGS
SOG
27.0
350
310
Compact- DDGS
Light.
PSNR 95 14.6 MB #G (882)
PSNR 25.24 7.7 MB (471)) FPS
20 Storage (MB) [Log Scale]
10
50
100
Figure 1: Our approach focuses on minimizing storage requirements while using only a minimal number of Gaussian primitives. To achieve this, introduce local distinctiveness metric to identify the important Gaussians. Additionally, we propose a more efficient attribute representation, particularly for sparse Gaussians, by exploiting their irregularity and continuity. As result, method enables scene representation under MB while achieving 600+ FPS rendering.
Abstract
3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time, high-performance rendering, enabling wide range of applications. However, representing 3D scenes with numerous explicit Gaussian primitives imposes significant storage and memory overhead. Recent studies have shown that high-quality rendering can be achieved with a substantially reduced number Gaussians when represented with high-precision attributes. Nevertheless, existing 3DGS compression methods still rely on a relatively large number of Gaussians, focusing primarily attribute compression. This is because smaller set of Gaussians becomes increasingly sensitive to lossy attribute compression, leading severe quality degradation. Since the number of Gaussians directly tied computational costs, is essential to reduce the number of Gaussians effectively rather than only optimizing storage. In this paper, we propose Optimized Minimal Gaussians representation (OMG), which significantly reduces storage while using minimal number primitives. First, determine the distinct Gaussian from the near ones, minimizing redundancy without sacrificing quality. Second, we propose a compact and precise attribute representation that efficiently captures both continuity and irregularity among primitives. Additionally, we propose sub- vector quantization technique for improved irregularity representation, maintaining fast training with negligible codebook size. Extensive experiments demonstrate that OMG reduces storage requirements by nearly 50% compared to the previous
Corresponning authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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oftteoo-theear and enables 600+ FPS rendering while maintaining high rendering quality. Our source code available at http:///mainoood...iiibb...b///
Introduction
3D Gaussian Splatting (3DGS) 28 has gained popularity for fast and photorealistic 3D scene recon- struction and rendering, offering compelling alternative to conventional methods. By leveraging tile-based parallelism to approximate NeRF's [40] volumetric rendering. 3DGS enables significantly accelerated rendering while maintaining high visual quality. This has facilitated a wide range of applications, such as dynamic scene reconstruction [6] (58), photorealistic avatar generation [48] 41),
generative models [56] [.]. and city-scale rendering 29 49], demonstrating its versatility across
various domains.
3DGS adjusts the number of Gaussian primitives during training by iteratively cloning or splitting Gaussians with high positional gradients while removing low-opacity Gaussians. However, this optimization process introduces a substantial number of redundant Gaussians (over million per 360 scenes 22,, leading to excessive storage requirements and computational overhead. To address this issue, various approaches have been proposed, including pruning based on rendering loss [32] (6) or importance score 446 [4] and optimized densification strategies [39]. Notably, several methods [14] 15 [4] reduce the number of Gaussians to around 0.5 million, enabling real-time rendering even on low-capacity GPUs while preserving rendering quality.
Despite these efforts, reducing the number of Gaussians alone does not sufficiently mitigate storage overhead. Each Gaussian is parameterized by 59 learnable parameters, so even with a reduced number of primitives, storage consumption remains substantial (e-g.. 133 MB in Figure [ To address this, many works have explored compressing Gaussian attributes by leveraging vector quantization 13,, neural fields [53), sorting mechanisms 42, and entropy optimization 8 57] demonstrating considerable improvements in reducing storage consumption.
However, the aforementioned compression methods typically rely on a large number of Gaussians (over one million). This is due to two major challenges when the number of Gaussians is drastically reduced: 1) each Gaussian needs to represent a larger portion of the scene, making it more suscep- tible to compression loss, and 2) the increased spacing between Gaussians disrupts spatial locality, leading to higher attribute irregularity and posing challenges for entropy minimization and efficient compression. Since the number of Gaussians directly impacts computational costs, including training time and rendering speed, it is crucial to develop approaches that effectively minimize the number of Gaussians while maintaining compressibility.
In this paper, we propose Optimized Minimal Gaussian representation (OMG), an efficient compres- sion framework that operates with a minimal number of primitives. To address the irregularity of novel
sparse Gaussians and maximize the compressibility, we employ per-Gaussian features way. Although the reduced number of Gaussians leads to decrease in local continuity, we can still leverage the spatial correlation associated with each Gaussian's 's position. Therefore, we introduce lightweight neural field model with negligible parameters to capture the coarse spatial feature. This feature integrated with the per- per-Gaussian features represent each attribute, as shown in Figure This approach requires fewer per- Gausian parameters than directly learning the original attributes, enabling a more compact representation. While the proposed OMG architecture effectively represents sparse Gaussians, the use of per-Gaussian features impacts storage efficiency. To mitigate this, we introduce sub-vector quantization (SVQ, Figure c)), which splits the input vector into multiple sub-vectors and applies vector quantization each sub-vector. This approach alleviates the computational overhead associated with large vector quantization codebooks (Figure 3 a)) and reduces the storage burden caused by the multiple indexing stages of residual vector quantization (Figure B))),, while maintaining nigh-precision representation.
Finally, to retain only the minimal number of Gaussians, we introduce novel importance metric that evaluates each Gaussian's local distinctiveness relative to neighbors, identifying the most informative Gaussians. This metric is used alongside existing importance scoring methods based on blending weights from training views 46 [4] further reducing the number of Gaussians while preserving scene fidelity.

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Extensive experimental results demonstrate that OMG achieves a 49% reduction in storage compared the previous sate-of-the-ar method [53], requiring only 4.1MB for the Mip-NeRF 360 dataset 2 while preserving comparable rendering quality. Additionally, OMG utilizes only 0.4 million Gaus- sians, enabling 600+ FPS rendering. These results underscore the effectiveness of OMG in both compression efficiency and computational performance, demonstrating it as highly promising approach for 3D Gaussian Splatting representation.
2 Related work
2.1 Neural Radiance Fields
Neural Radiance Fields (NeRF) 140 introduced a pioneering approach for novel view synthesis by leveraging volumetric rendering in conjunction with multi-layer perceptrons (MLPs) to model continuous 3D scenes. While NeRF achieves high-quality rendering. its reliance on ML. leads to inefficiencies, particularly in terms of slow training and inference times. To overcome these limitations, the following methods [66 34] utilized explicit voxel-based representations, enabling significantly faster training compared to traditional MLP-based NeRF models. However, these approaches still suffer from slow inference speeds and impose substantial memory requirements, posing challenges for scalability and practical deployment in large-scale environments.
Compact representation. To mitigate the memory overhead while maintaining rendering fidelity, various works have been introduced, including grid factorization 66 5, [2 18], hash grids 43
7) grid quantization [55 52), and pruning-base -aaing--aased strategies 50 Nevertheless, achieving real- time rendering for complex, large-scale scenes remains formidable challenge. The fundamental limitation of these approaches stems from the necessity of dense volumetric sampling, which, despite optimizations, continues to constrain training and inference speed.
2.2 3D Gaussian Splatting
Recently, 3D Gaussian Splatting (3DGS) 22 has emerged a paradigm-shitee shifting technique for real-time neural rendering by representing scene with 3D Gaussian primitives. 3DGS leverages customized CUDA kernels and optimized algorithms to achieve unparalleled rendering speed while preserving high image quality. Unlike volumetric methods that require dense per-ray sampling, 3DGS projects Gaussians onto the image plane and rasterizes them tile-wise, significantly improving computational efficiency. Due to its versatility, 3DGS has become a dominant paradigm in 3D representation, leading to advancements across various domains and applications, such as mesh extraction 24], simultaneous localization and mapping (SLAM) [27]. dynamic scene represen- rendering quality [14. However, 3DGS requires substantial number of Gaussians maintain high-quality rendering. Furthermore, each primitive is represented with multiple attributes, such as covariance matrices and spherical harmonics (SH) coefficients, requiring a large number of learnable parameters. Conse- quently, 3DGS demands substantial memory and storage resources, often exceeding 1GB per scene in high-resolution environments.
tation 38 multi-resolution rendering [62], and further improvements
Reducing the number of primitives. To alleviate the substantial computational and memory overhead of 3DGS, numerous methods have been proposed to reduce the number of Gaussians while preserving rendering quality. Several approaches follow 3DGS by pruning low-opacity Gaussians, incorporated with opacity regularization [44) anchored Gaussians (37), or hyperparameter search (42) An alternative approach utilized binary masking techniques [32
8 54 57 [9], where pruning decisions are directly learned based rendering loss. To optimize the binary masks, Compact-
3DGS 44 initially adopted STE [4] while subsequent works
66
36 employed Gumbel-Softmax.
Another direction focuses on importance-based metrics to identify and remove redundant Gaussians. These methods primarily leverage each Gaussian' blending weight contribution to rendering training- view images as a measure of importance [3] 14] 46 19 39]. LightGaussian [13] further incorporates
Gaussian volume and opacity into the importance computation, while Taming 3DGS [39 integrates multiple information, including gradients, pixel saliency, and Gaussian attributes. Building upon these advancements, we introduce a novel importance metrie that incorporates color distinction among neighboring Gaussians, enabling more effective selection of essential primitives.
3

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Attribute compression. Earlier methods employed conventional compression techniques such as scalar and vector quantization (VQ) T
47 60 and entropy coding 45 42 32
g
to reduce storage requirements. VQ-based representations have proven highly efficient by the fact that many Gaussian attributes are redundant across a scene, allowing for compact encoding. However, large codebook leads to substantial computational overhead, increasing training time. While residual vector quantization (R-VQ) [32] can alleviate computational costs, it introduces additional storage inefficiencies due to the need for multiple code indices.
Another line of work explored structured representations, incorporating anchor-based encoding 37
8,559 355 and factorization techniques [54]], integrated with grid representations. Scaffold-G5 [37
first introduced an anchor- based approach. where attributes of grouped Gaussians are encoded using shared anchor features and MLP-based refinements. Building upon this, subsequent methods [8] 10
59 35] incorporated context modeling to further improve compression rates. While showing high compression performance, these approaches require per-view processing, involving multiple MLP forward passes, which results in significant rendering latency.
Recent efforts have utilized neural field architectures to exploit the local continuity of neighboring Gaussians. Compact-3DGS (32 encodes view-dependent color, while LocoGS (53) represents all Gaussian attributes except for view-independent color. However, unlike NeRF-based representations, where exact spatial positions are used inputs to neural fields, mapping Gaussian center points to their corresponding attributes remains challenging. This difficulty leads to the use of large neural field models to achieve accurate reconstruction. In this work, we propose novel approach that effectively captures both the continuity and irregularities across Gaussians, enabling more efficient and compact attribute representation.
3 Method
Background. 3DGS represents a scene using a set of N Gaussians, parameterized by their attributes: center position p E R x3 opacity o E [0, 1] 3D scale s E R 3D rotation represented as a quaternion r E R Nx4 and view-dependent color modeled using spherical harmonics (SH) coefficients (0) E Nx3 (0 degree for static color), M(12,3) E RNx15 (1 to 3 degrees for view- dependent color). The covariance matrix of each Gaussian E,, E R 3x3 is determined by scale 8, and rotation Tn attributes.
To render an image, 3D Gaussians are projected into 2D space. Each pixel color in the image C(-) is then rendered through the alpha composition using colors e, (determined by spherical harmonics under the given view direction) and the final opacity in 2D space a.,(),
((x)
C(x)=
Σ
CRau (x)
II (1--a)()))
1 (x-
On (x(x) = On exp
-1-))
where x denotes a pixel coordinate and E,, P/1 are the projected Gaussian covariance and center position. (xx) represents the number of Gaussians around x, where the Gaussians are depth-sorted based on the given viewing direction.
3.1 Overall architecture
OMG is designed to accurately and efficiently represent the attributes of the minimal Gaussian primitives. Existing approaches 32 53 have leveraged neural fields to exploit the local continuity of Gaussian attributes. While effective in dense representations, this assumption weakens as Gaussians become sparser. In sparse settings, neighboring Gaussians further apart, and smooth transitions between them become insufficient to maintain fidelity. Especially for geometry, each Gaussian covers larger spatial region, requiring a more specific scale and rotation to accurately capture structural details. Therefore, we retain the per-Gaussian parameterization for scale E RNx3 and rotation E N-xx as in 3DGS.
For appearance, local continuity can still be maintained even with increased sparsity. However, unlike NeRF, where the query input is direct spatial point, mapping Gaussian center points to

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Position-based Continulty
Leamable Embeddling
Geometry feats.
Sq
Scale
Postiin
Space feet.
Sub Sub-Vectt Vector Quiant
Pon
Rot.
Appearance feats.
Tm
Opacity
Gaussian Points
MLP P
3D Gaussians
Figure 2: The overall architecture of our proposed OMG. OMG learns gar-Saas geometric and appearance features, applying veb-Vector Quantization (SVQ) to all of them. The SVQ-applied geometric attributes are used for rendering, while the space feature based on the Gaussian center position is integrated into the appearance features define the final appearance.
corresponding appearances is inherently challenging. This requires larger neural field model to maintain high fidelity. On the other hand, entirely disregarding local continuity leads to an inefficient representation, limiting the ability capture meaningful spatial relationships. OMG addresses these challenges by integrating per- Ger-Gausiinn attributes with a small neural field structure, effectively leveraging both irregularity and continuity.
To represent appearance, we learn per-Gaussian attributes, namely a static feature T E RN and a view-dependent feature V NxD, where D is the dimensionality of each feature. As illustrated in Figure 2 each feature is concatenated with the space feature F., derived from each Gaussian's 's center position, generate static and view-dependent color, and opacity. The space feature itself is efficiently parameterized using positional encoding and an MLP, ensuring a highly compact representation. Formally, this process can be expressed as follows:
(0) (Catt(ca(TT,,, F,)), on MLLPo(ca(TT... Fx)),
A (1.2.3) = MLP, ( n,,, Fx)), F2 MLP, (7(x))
(4)
where cat(...) denotes the concatenation function, 7()) represents the positional encoding function, and (+), M-),(-), MLP,, (-), (.),(.) ()) are the MLPs for static color, opacity, view-dependent color, and space feature, respectively.
3.2 Sub-vector quantization
Vector quantization (VQ) 20 has shown high efficiency for representing Gaussian attributes, capi- talizing on their inherently vectorized structure and strong global coherence across an entire scene. However, to maintain high fidelity, a large codebook size is required, inevitably resulting in substantial computational overhead and increased training complexity [63] (Figure Ba)). To address these issues, Residual Vector Quantization (R- (Q) (3 has been used as a hierarchical quantization strategy (32) progressively refining representations while reducing the size of each individual codebook. However, as shown in Figure b)), multiple code indices per attribute result in increased storage overhead, illustrating tradeoff between reducing per-codebook complexity and increasing overall storage requirements.
To navigate this tradeoff, we propose Sub- Vector Quantization (SVQ). which partitions the attribute vector into multiple sub-vectors and applies vector quantization separately to each component (Figure C)). motivated by Product Quantization [26]. By reducing the dimensionality of each quantized unit, SVQ allows for smaller codebooks and more efficient lookups. which can balance computational cost and storage efficiency while maintaining high fidelity. We can apply SVQ to an input vector 2 E ML where M and L represent the total number of sub-vectors (partitions) and the sub-vector length, respectively. Each partition m [I.... M) has an independent codebook ((()) R Bx where B denotes number of codewords per codebook. The codeword selection based on the nearest match from (im)), with ((rm [j] representing the j-th codeword corresponding to the m-th sub-vector. More formally, SVQ-applied vector can be formulated as follows.
= q(;; M) = cat(C(()) [1], (x2) [i2]. ((M)))
(5)
im arg min 2m - (1)) Lills m (1,...,
(6)
5

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258
256
Heavy computation, Largeflimited #codes
Large indices, Repeated computation
Reduced computation and #oodes
(a) Vector Quantizution
(b) Residual Vector Quantization
Sub-Vector Quantization
Figure 3: Conceptual diagram of (a) vector quantization, (b) residual vector quantization, and (c) sectvector quantization. + and @ denote the element-wise summation and the vector concatenation.
where ((2; M) denotes applying SVQ with M sub-vectors and im E {l...., B3 is the selected index of m-th sub-vector.
SVQ ensures significantly reduced computation with small codebooks compared to VQ. We apply SVQ to geometric attributes Sn: ''- resulting in quantized vectors šn An. which then used for 3DGS rendering. For the appearance features Tn, Vn, we first concatenate them and apply SVQ. The resulting quantized features are then split back into two components T,, Vn, which replace T, and V, in Equations (3) and (4).
Although the reduced codebook size significantly decreases computational overhead compared to VQ, the process of updating both the indices and codes at every training iteration increases training time. Moreover, we observe that as training converges, the selected codebook indices remain largely unchanged. Therefore, adopt fine-tuning strategy in the final 1K iterations: after initializing with K-means, we freeze the indices and finetune only the codebook using the rendering loss, without introducing any additional losses. Since K-means initialization is completed within seconds due to the small codebooks, this approach adds minimal additional training time, unlike other methods that incur significant overhead.
3.3 Local distinctiveness for important scoring
OMG adopts importance scoring to identify essential Gaussians and retain minimal number of them. Existing scoring- based pruning methods typically determine the importance of each Gaussian based on its blending weights (the values multiplied by G₁ in Equation (T) across training-view renderings. We use two factors our bascline metric: (1) whether it has been the most dominant contributor for at least one ray [14] [5] and (2) its total blending weight contribution across all training rays [46] [9] Formally, define the base importance score 1 as:
NR p=1 Wipp
if 3p E {1,..., NR. Huwp = max, Wjps otherwise,
1, =
where W represents the blending weight of Gaussian i for ray p and N R is the number of total rays in training views.
While this score captures global importance, it does not account for redundancy among closely-
positioned Gaussians. In cases where multiple Gaussians are located in close proximity, their blending weights tend be highly similar, thus naively thresholding them can lead two potential issues: (I) abrupt performance degradation when all similar Gaussians are simultancously removed, and (2) redundancy when multiple Gaussians with near-identical contributions are retained.
To mitigate these issues, we propose incorporating local distinctiveness metric into the importance computation. Specifically,
introduce additional term that measures the similarity of the static appearance feature T R between neighboring Gaussians, ensuring that locally distinct Gaussians show high importance. The final importance score is defined as:
A
I -=
Σ JENK
TT [T-l]]]]]]
Tyy1 -T]l1)
D
where N,K denotes the set of -nearest neighbors of Gaussian and a scaling factor that adjusts the sensitivity to appearance variation. As computing exact K K-nearest neighbors for every Gaussian is

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GT
3DGS
LocoGS (COLMAP)
OMG-XS
Bicycle
21.01 / 1.4 GB
20.95 / 12.3 MB
20.89 4.6 MB
Kitchen
31.94 417 MB
31. 14. / 5.0 MB
32.45 3.3 MB
Figure 4: Qualitative results of OMG compared 3DGS and LocoGS with COLMAP initialization. We provide per-image rendering PSNR with storage requirements for each scene.
computationally expensive, we approximate neighbor selection by sorting Gaussians in Morton order and selecting Gaussians with adjacent indices as their local neighbors. We remove low-importance Gaussians using CDF-based thresholding (35 with threshold r.
Experiment
4.1
Implementation details
Following the previous works, evaluated approach on three real-world datasets, Mip-NeRF 360 (2). Tanks&Temples [I], and Deep Blending [23]. Our model implemented upon Mini- Splatting [14], of the methods achieving high performance with small number of Gaussians. We have conducted simple post-pree mostprocesssigs after training: 1) Applying I 16-bit quantization to the position and compressing with G-PCC (51). 2) Huffman encoding (25) to SVQ indices. 3) Storing all the components into single file with I I.ZMA II compression. We provide five OMG variants (XS, X, M. L. XL), adjusting storage requirements. The only factor controlling the storage is the CDF-based threshold value of Gaussian importance, which is set to 0.96, 0.98, 0.99, 0.999, and 0.9999 for each variant, respectively. Further implementation details are provided the supplementary materials.
4.2 Performance evaluation
Compression performance. Tables and 2 compare the performance OMG against various baseline methods on the Mip-NeRF 360, Tanks & Temples, and DeepBlending datasets. OMG consistently shows the smallest storage requirements while maintaining high performance across all datasets, achieving state-of-the-art (SOTA) results. Notably, the Mip-NeRF 360 dataset, OMG-XS achieves nearly a 50% reduction storage compared to small variant LocoGS (53], previous SOTA compression method, while retaining PSNR and SSIM. With over 30% reduced storage, OMG-M outperforms LocoGS-S in all quality metrics. Moreover, OMG-XL surpasses LocoGS-L in all metrics, even though requiring less storage than LocoGS-S.
The qualitative results presented in Figure 4 also demonstrate the strong performance of OMG, Despite achieving over 100x compression compared to 3DGS, OMG maintains comparable visual quality. Especially, in the bicycle scene, OMG-XS achieves over 300x compression relative to 3DGS while accurately reconstructing details that 3DGS fails to represent, resulting in a blurry area (highlighted in red) its rendering. This superiority can be attributed to the blur split technique of our baseline model, Mini-Splatting [14]. Despite reducing the number of Gaussians by an additional 20% compared to Mini-Splatting (Table OMG-XS retains high visual fidelity, demonstrating its effectiveness in extreme compression scenarios.

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Table 1: Quantitative results of OMG evaluated the Mip-NeRF 360 dataset. Baseline results sourced from the LocoGS [3 paper, where the rendering results were obtained using an NVIDIA measured using the same GPU, with the values in parentheses obtained from an NVIDIA RTX 4090 GPU. LocoGS* refers to LocoGS initialized with COLMAP, instead of dense Nerfacto initialization. We highlight the results among compression methods by coloring the best
RTX 3090 GPU. Our rendering performance
second-best and third-best performances.
Mip-NeRF 360
Method
PSNR
SSIM
LPIPS +
Size(MB)) 4
FPS 1
3DGS
27. 44
0.813
0.218
822.6
127
Scaffold-GS
37
27.66
0.812
0.223
187.3
122
Mini-Splattig
14
27.39
0.822
0.216
119.5
(601)
CompGS
40
27.04
0.804
0.243
22.93
236
Compact-3DGS
32
26.95
0.797
0.244
26.31
143
C3DGS 45
27.09
0,802
0.237
29.98
134
LightGaussian
183
26.90
0,800
0.240
53,96
244
EAGLES 19
27.10
0,807
0.234
59,49
155
SOG 42
27.01
0,800
0.226
43.77
134
HAC 8
27.49
0,807
0.236
16.95
110
LocoGS-S
53
27.04
0,806
0.232
7,90
310
LocoGS-L
53
27.33
0,814
0.219
13.89
270
27.09
0.798
0.250
7.96
(396)
LocoGS (COLMAP)
27.37
0.807
0,236
15.10
(325)
OMG-XS
27.06
0.807
0.243
4.06
350 (612)
OMG-M
27.21
0.814
0.229
5.31
298 (511)
OMG-XL
27.34
0.819
0.218
6.82
251 (416)
Table Quantitative results of OMG evaluated on the Tanks& Temples and Deep Blending datasets.
Baseline results are sourced from the LocoGS (53] paper, where the rendering results were obtained using an NVIDIA RTX 3090 GPU. Our rendering performance was measured using the same GPU, with the values in parentheses obtained from an NVIDIA RTX 4090 GPU.
Tank& Temples
Deep Blending
Method
PSNR
SSIM 1
LPIPS
Size L
FPS
PSNR
SSIM
LPIPS
Size
FPS
3DGS
28
23.67
0.844
0.179
452.4
175
29.48
0.900
0.246
692.5
134
Scaffold-GS
37
24.11
0.855
0.165
154.3
109
30.28
0.907
0.243
121.2
194
Mini Mini-Splattin Spiattin
14
23.41
0.846
0.180
67.6
(1095)
30.04
0.910
0.241
124.9
(902)
CompGS
23.29
0.835
0.201
14.23
329
29.89
0.907
0.253
15.15
301
Compact-3DGS
30
23.33
0.831
0.202
18.97
199
29.71
0.901
0.257
21.75
184
C3DGS 45
23.52
0.837
0.188
18.58
166
29.53
0.899
0.254
24.96
143
LightGaussian
23.32
0.829
0.204
379
29.12
0.262
45.25
287
EAGLES 19
23.14
0.833
0.203
30.18
244
29.72
0.906
0.249
54.45
137
S0G
23.54
19.32
HAC g
24.08
0.846
0.186
8.42
129
29.99
0,902
0.268
4.51
235
LocoGS 53
23.63
0.847
0.169
6.59
333
30.06
0.904
0.249
7.64
334
LocoGS-I.
23.84
0.852
0.161
12.34
311
30.11
0.906
0.243
13.38
297
OMG-M
23.52
0.842
0.189
3.22
555 (887)
29.77
0.908
0.253
4.34
524 (894)
OMG-L
23.60
0.846
0.181
3.93
478 (770)
29.88
0.910
0.247
5.21
479. (810)
Computational efficiency. OMG achieves remarkable efficiency alongside high performance. As shown in Table OMG shows superior compared to LocoGS. This reduction results in substantial rendering speed improvements of 13%, 67%, and 57% for the Mip-NeRF 360, Tank& Temples, and Deep Blending datasets (Tables and 2) respectively, compared to LocoGS, highlighting its potential for real-time rendering on low-capacity devices. Furthermore, OMG accelerates training speed. The substantial improvement over LocoGS can be attributed to two key factors: the reduced number of Gaussians and the absence of large neural field. By efficiently exploiting coarse spatial information through a tiny MLP, OMG achieves high computational efficiency.
fidelity with significantly fewer Gaussian primitives
4.3 Ablation study
Local distinctiveness (LD) scoring. OMG improves Gaussian pruning by incorporating LDD scoring into the importance estimation. Without attribute compression, LD scoring enables high

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Table 3: Efficiency comparison of OMG variants evaluated on the Mip-NeRF 360 dataset. We present training time, the number of Gaussians, and the storage requirement with rendering quality.
Method
Training
#Gauss
Size
PSNR
SSIM
LPIPS
Sini-Splatting
19m 25s
531K
119.5
27.39
0.822
0.216
LocoGS-S
1.09M
7.9
27 07.04
0.806
0.232
#1h
LocoGS-L
1.32M
13.89
27.33
0.814
0.219
OMG-XS
20m 15s
427K
4.06
27.06
0.807
0.243
OMG-S
20m 57s
501K
4.75
27.14
0.811
0.235
OMG-M
21m 10s
563K
5.31
27.21
0.814
0.229
OMG-L
21m 32s
696K
6.52
27.
0.818
0.220
OMG-XL
22m 26s
727K
6.82
27.34
0.819
0.218
27.5
Table 4: Ablation study OMG using the Mip- NeRF 360 dataset.
Mip-NeRF 360
23.4
T&T
27.4
23.3
27.3
23.2
Method
PSNR
SSIM
L.IPS
#Gauus
Size
>30% #G
27.2
23.1
OMG-M
27.21
0.814
0.229
0.56M
5.31
23
wio Space feature
26.96
0.811
0.232
0.59M
5.58
27.1
22.9
wio scuring
5.36
+20%
w/o Both
26.81
0.809
0.234
0.59M
5,59
27
#G
22.8
26.9
22.6
22.7
Mini-Splatting
OMG-XS
w/o SVQ
27.06
27.26
0.807
0.817
0.226
0.243
0.43M
0.56M
4,06
26.1
scoring
wio Space feature
26.85
0.804
0.246
0.44M
4.17
26.8
22.5
w/o LD scoring
26.83
0.804
0.246
0.43M
4.12
300
350 400 450 500 550
150
200
250
300
w/o Both
26.52
0.798
0.252
0.45M
4.24
Number of Gaussians (K)
Figure 5: Evaluation without attribute compression.
SVQ
27.06
0.43M
19.8
rendering quality with extremely reduced Gaussians, achieving similar performance compared to Mini-Splatting that use 20-30% more Gaussians, as shown in Figure 5 When applying attribute compression (Table OMG with LD scoring still outperforms, leading to significant performance improvement with a similar number Gaussians. This effect becomes even more pronounced when the target Gaussian number is lower, demonstrating that LD scoring provides effective approach for further reducing a sparse set Gaussians.
OMG architecture. OMG leverages highly compact neural field to capture coarse spatial information while reducing the number of learnable parameters per Gaussian. Table 4 validates the contribution of this space feature. Although the total number of Gaussians slightly increases, performance significantly degrades. The absence of spatial information introduces instability in attribute learning, hindering effective importance scoring. This trend is consistently observed in both our small and medium models, highlighting the effectiveness of the space feature despite its minimal parameter overhead. Furthermore, when both the space feature and LD scoring removed, the model experiences the most substantial performance drop. This indicates that the two contributions orthogonal, independently contributing model efficiency and performance.
Sub-vector quantization. Table 4 shows the effectiveness of SVQ in terms of performance- storage efficiency. In addition, we replaced it with VQ and RVQ while keeping the proposed clever training strategy: K-means clustering performed once prior to the final 1K iterations, after which only the codebook is updated. As shown in Table 5 VQ incurs a 13-18x increase in codebook initialization time compared SVQ, resulting significantly higher training overhead due to need for large codebooks 12 entries per scale, rotation, and two appearance features) to ensure accurate representation. Nevertheless. VQ leads to lower rendering quality and/or increased storage costs across variants. In contrast, RVQ achieves slightly faster codebook initialization than SVQ but performs poorly in terms of rate-distortion efficiency, yielding higher storage requirements and lower rendering quality. These results demonstrate that SVQ outperforms both VQ and RVQ in representing per-Gre Gaussian attributes efficiently.
Generalization ability. The OMG attribute representation is applicable to all 3DGS-basee methods, as it flexibly represents attributes with spatial locality (via space features and SVQ) as well as those without (using SVQ alone). To validate this generalizability, we applied OMG to 3DGS-MCMC 30 a method well-known for effective densification. As reported in Table OMG preserves the original performance and reduce storage requirements, demonstrating its broader applicability.

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Table Ablation study of SVQ on the Mip-NeRF 360 dataset. We replace SVQ with either VQ or RVQ. 'CB Init' denotes the time (in seconds) required for codebook initialization.
Method
OMG-XS
OMG-M
OMG-XL
Metric
CB Init
Size|
PSNRT
CB Init
Sizel
PSNR
CB Init
Size
PSNR
OMG
4.8
4.06
27.06
7.6
5.31
27.21
7.9
6.82
27.34
86.4
4.21
27.06
100.9
5.36
27.18
117.4
6.70
27.27
SVQ-
4.0
4.19
27.01
5.5
5.52
27.13
6.9
7.10
27.25
Table 6: Results of applying OMG representation 3DGS-MCMC [30
on the Mip-NeRF dataset.
Method
#Gauss
PSNR
SSIM
LPIPS
Size
#Gauss
PSNR
SSIM
L.PIPS
Size
MCMC 30
500K
27.42
0.807
0.248
115 MB
IM
27.83
0.823
0.221
230 MB
MCMC+OMG
500K
27.21
0.797
0,256
5.1 MB
IM
27.63
0.813
0.227
10.0 MB
Table 7: Performance evaluation using Zip-NeRF 3 dataset. indicates results reported in the SMERF [12) paper, where 3DGS hyperparameters were tuned for higher performance. We train and report results for 3DGS and Mini-Splatting using their default settings.
Method
PSNR
SSIM
LPIPS
#Gauss
Size
Zip-NeRF*
27.37
0.836
0.305
607 MB
SMERF*
12
27.28
0.829
0.350
4.1 GB
3DGS*
28
25.50
0.810
0.369
212 MB
3DGS
28
26.16
0.813
0.358
934K
210 MB
Mini-Splatting 14
24.57
0.802
0.370
337K
75.7 MB
MCMC (30
26.37
0.838
0.318
3M
675 MB
MCMC+OMG
26.69
0.839
0.310
3M
29.0 MB
Limitations and broader applicability
Baseline dependancy. OMG builds upon 3DGS and Mini-Splatting with the goal of preserving original rendering quality under compact representations. However, its applicability remains inher- ently bounded by the capabilities of these baselines. For instance, 3DGS and subsequent approaches exhibit clear limitations in capturing the complexity of larger-scale scenes such as the Zip-NeRF [3] dataset. As shown in Table 7 both 3DGS and Mini-Splatting fail to densify sufficient number of Gaussians, leading to degraded performance. Consequently, OMG inherits these limitations, restricting its applicability in such scenarios.
Broader applicability. On the other hand, as demonstrated in Table 6 OMG can be applied in a generalizable manner across different baseline methods. Based on this strength, we tune 3DGSMMMMC for large-scale scenes and incorporate the OMG representation, achieving superior performance while drastically reducing storage requirements (Table More broadly, when integrated with ongoing research across diverse domains, OMG has the potential to provide a highly general and efficient representation.
6 Conclusion
In this paper, we proposed Optimized Minimal Gaussians (OMG), novel framework that significantly reduces the number of Gaussian primitives while maximizing compressibility and maintaining high rendering quality. By effectively identifying and preserving locally distinct Gaussians, OMG minimizes the redundancy of Gaussians with minimal loss of visual fidelity. Furthermore, our compact and precise attribute representation, combined with sub-vector quantization, enables efficient exploitation of both continuity and irregularity, ensuring high efficiency. Experimental results demonstrate that OMG reduces storage requirements by nearly 50% compared to the previous state- of-the-art method while allowing over 600 FPS rendering performance. OMG sets a new benchmark for highly efficient 3D scene representations, paving the way for future advancements real-time rendering on resource-constrann devices.
10

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Acknowledgements
This work was supported part by the Institute of Information and Communications Technology Plan- ning and Evaluation (IITP) grants (RS- (RS-2019-11190421, RS-2020-11201821, RS-2021-112 RS- 1921-11212068, RS-2024-00457882, RS-2025-002116613, RS-2025- 1069298,, RS-2025-25442569) funded by the Korean government (MSIT); the Technology Innovation Program (RS-2023-002357) 18, 13FC) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) (1415187505) Samsung Research Funding & Incubation Center of Samsung Electronics (SRFC-IT2401-00) -004); grant of the Korea-UP US Collaborative Research Fund (KUCRF), funded by the Ministry of Science and ICT and Ministry of Health & Welfare, Republic of Korea RRS-2024-00468417),
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction accurately reflect the paper's contribution, Optimized Minimal Gaussians (OMG) representation, which reduces storage requirements and enables faster rendering compared existing 3DGS methods.
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A Difficulty in compressing small set of Gaussians
To assess the challenge of compressing small number of Gaussians, integrate Mini- Mini-Splatting [4 with existing methods [2]] 531. We trained LocoGS [53] with Simi-Splatting-aaeee densification in an end-to-end manner. For Compact-3DGS 32). however, we observe that training it directly with Mini-Splatting-hasse pruning fails to converge, due to inaccurate attribute representations during training. Rather, we halt Mini-Splatting training early 20K iterations, where the pruning process is ended, and integrate Compact-3DGS representation in the subsequent 10K iterations.
As shown in Table 8 the integration of Mini-Splatting results in significant degradation in visual quality across both methods, primarily caused by imprecise attribute representations for irregular and sparse Gaussians. In contrast, OMG preserves high visual fidelity with drastically reduced storage by through its novel attribute representation.
Table 8: Performance evaluation compared to Compact-3DGS number Gaussians.
32
and LocoGS
53 with the reduced
Method
PSNR
SSIM
LPIPS
#G
Size
Mini-Splatting (MS)
27.39
0.822
0.216
531K
120 MB
Compact-3DGS
27.03
0.797
0.247
1.39M
29.1 MB
MS Compact-3DGS
26.85
0.807
0.234
525K
14.8 MB
LocoGS-S
27.04
0.806
0.232
1.09M
7.9 MB
MS + LocoGS-S
26.52
0.789
0.257
537K
6.2 MB
LocoGS-L
27.33
0.814
0.219
1.32M
13.9 MB
MS + LocoGS-L
26.76
0.799
0.247
525K
11.3 MB
B KNN approximation using Morton order
Table Presents comparison between Morton-order-baane based approximation and KNN for LD scoring. The performance is nearly identical, demonstrating that Morton order effectively approximates KNN while achieving 146x speedup, our current setup (LD scoring once at 20K with fewer than 1M Gaussians), our decision to adopt Morton order motivated by the need for scalability and generalizability. In more complex or larger-scale scenes, the number of Gaussians can increase significantly during earlier training stages. In such cases, KNN becomes impractical due to its high memory usage and computational complexity of (NN)). Morton order, by contrast, provides a well-approximated yet efficient alternative that scales better with the number of primitives.
with the smallest model. While using KNN is reasonable in
Table 9: Performance evaluation of KNN approximation on the Mip-NeRF 360 dataset.
Method
PSNR
SSIM
LPIPS
Time (ms)
OMG-XS
27.06
0.807
0.243
4100
Morton - KNN
27.07
0.808
0.241
28
C Rendering speed on weaker devices
We have measured the FPS of our method on an NVIDIA RTX 4090 and lower-end GPU, NVIDIA GTX 1080 Ti. As shown in Table I0] OMG achieves substantially higher FPS on both devices compared to LocoGS, primarily due to its drastically reduced number of Gaussians. Especially, OMG-XS show 1.5x faster rendering on the GTX 1080Ti while requiring only half the storage and preserving high visual quality. These results indicate that OMG's efficiency directly extends to practical real-time applications, enabling deployment lower-end GPUs.
22

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Table 10: Rendering FPS evaluated on the RTX 4090 and GTX 1080Ti, compared to LocoGS (with
COLMAP initialization).
Method
4090
1080T1
PSNR
SSIM
LPIPS
#Gauss
Size (MB)
LocoGS
396
73
27.09
0.798
0.250
1.04M
7.96
OMG-XS
612
106
27.06
0.807
0.243
0.43M
4.06
LocoGS
325
27.37
0.807
0.236
1.44M
15.1
OMG-XL
416
77
27.34
0.819
0.218
0.73M
6.82
Table 11: Ablation study on the post-processing methods applied in OMG,
G-PCC Huffman
OMG-XS
OMG-S
OMG-M
OMG-L
OMG-XI.
5.82
6.83
7.66
9.47
9.89
4.30
5.04
5.64
6.92
7.25
V
5.58
6.54
7.33
9.08
9.46
/
4.06
4.75
5.31
6.52
6.82
D Implementation details
D.1 Mip-NeRF 360 dataset
All experiments were conducted using an NVIDIA RTX 4090. Our method was implemented within framework and trained for 30K iterations. At the 20K iteration simplification process, local distinetiveness scoring was incorporated where the factor was set to The dimension of appearance features D was set 3. Scale and rotation were trained from the initial training, while appearance features were introduced at 15K iterations. At this stage, the static features were initialized using the spherical harmonics DC coefficients trained until 15K iterations, whereas view-dependent features were initialized as zero vectors.
the Mini-Splatting 14
From 29K iterations (last 1K iterations), SVQ (Sub- Vector Quantization) was applied to ner-Gaussian features. As mentioned the paper, enhance training efficiency, K-means clustering was performed once. The assigned indices based on K-means were fixed, and only the codebooks were optimized for the remaining IK iterations. For SVQ, different bit allocations were assigned.
Scale: length 1, 2 codes for each sub-vector
Rotation: length 2, 29 codes for each sub-vector
Appearance features: length 2, 2 codes for each sub-vector
The length SVQ applied scale can be interpreted scalar quantization, dynamically learning the quantization range with the codebooks. All codes in the codebook are stored with 16-FP precision.
This SVQ configuration
commonly applied across all variants from XS to XL. The model storage for each variant was determined only by the importance score threshold т, which is used for simplification at the 20K iteration, set to 0.96, 0.98, 0.99, 0.999, and 0.999, respectively.
D.2 Zip-NeRF dataset
We used the Gaussians for 150K iterations with densification interval of 1K iterations. Other hyperparameters set to default values. For OMG training, the appearance features were introduced at 100K iterations, and from 140K iterations (last 10K iterations), SVQ applied to per-Gaussian features. For SVQ. bit allocations were assigned as follows:
30 framework, and tuned it for the Zip-NeRF dataset, by training 3M
Scale: length 1, 2 codes for each sub-vector
Rotation: length 2, 20 codes for each sub-vector
Appearance features: length 2, 211 codes for each sub-vector
23

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Table 12: The average storage allocation for each component across OMG variants. 'Actual size' refers to the total size of single file containing all components.
Attribute
OMG-XS
OMG-S
OMG-M
OMG-L
OMG-XL
Position
0.93
1.08
1.20
1.43
1.52
Scale
0.83
0.97
1.09
1.33
1.41
Rotation
0.87
1.02
1.15
1.40
1.49
Appearance
1.39
1.63
1.82
2.22
2.35
MLPs
0.03
0.03
0.03
0.03
0.03
Total
4.04
4.73
5.29
6.42
6.80
Actual size
4.06
4.75
5.31
6.52
6.82
E
Effect of post-processings
As mentioned in the main paper, we applied the following two post-processing methods:
Compressing the 16-bit quantized position with G-PCC [51
Huffman encoding 25 to SVQ indices and compressing the results with LZMA
Both methods are applied losslessly, and we report the resulting storage changes in Table When applied independently, G-PCC and Huffman encoding consistently reduce the total storage by 26-27% and 4-5% across all storage budgets, respectively. Applying both methods together also results in the overall storage reduction remaining consistent at approximately 30-32%.
F Storage analysis
We conducted experiments to analyze the storage requirements of OMG for representing each attribute, shown in Table 12 Across all variants, OMG allocates approximately 20-25% of the total storage to position, scale, and rotation, while around 35% is dedicated to representing appearance attributes, including static and view-dependent color well opacity. The four MLPs for representing local continuity and aggregating appearance attributes exhibit negligible storage requirements, even without extra compression.
G Per-scene results
We report per-scene results in Table 13 (Mip-NeRF 360 (2) and Table [4 (T&T 31 and DB
23
24

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Table 13: Per-scene results evaluated on the Mip-NeRF 360 2 dataset.
Method
Metric
bicycle
bonsai
counter
flowers
garden
kitchen
room
stump
trechill
Avg.
PSNR
24.95
30.90
28.40
21.32
26.42
30.81
31.09
27.00
22.60
27.06
0.743
0.932
0.596
0.818
0.918
0.788
0.647
0.807
LPIPS
0.276
0.202
0.206
0.368
0.190
0.137
0.208
0.247
0.357
0.243
OMG-XS
Train
18:03
20:30
24:44
19:18
18:02
23:45
20:30
17:49
19:40
20:15
#Gaaus
480772
263892
310056
543034
6017254
356752
281236
523821
479520
427371
4.61
2.53
2,95
5.24
5.65
2.67
4.95
4.64
4.06
FPS
682
648
433
616
615
498
648
708
658
612
PSNR
25.08
31,05
28.56
21.18
26.56
30.89
31.20
27.08
22.64
27,14
SSIM
0.750
0.936
0.903
0.602
0,826
0.921
0,922
0.792
0.650
0.811
0.177
0,235
OMG-S
Train
19:01
21:09
25:19
20:13
18:41
24:12
21:38
18:29
19:55
20:57
#Gauss
573126
310096
360930
633607
691441
412126
338884
619734
573425
501485
2.94
3,41
6.10
6.43
3.19
5.54
4.75
PPS
601
585
401
555
556
462
620
601
588
552
PSNR
25.14
31.06
28.62
21.40
26.71
31.05
31.30
27.06
22.55
27.21
SSIM
0.756
0,938
0.905
0.606
0.832
0.923
0,923
0.794
0.652
0.814
LPIPS
0.256
0.190
0.195
0.351
0.169
0.129
0.198
0.233
0.339
0,229
OMG-M
Train
21:01
20:35
18:51
22:14
18:31
20:22
21:10
#Gauss
646191
350999
400442
708074
772338
454908
375520
704907
649157
562504
Size
6.15
3.33
3,76
6.79
7.18
4.21
3.53
6.61
6.24
5,31
536
371
510
522
566
566
525
511
PSNR
25.24
31.47
28.66
21.45
26.83
31.03
31.26
27.05
22.57
27.28
SSIM
0.762
0,941
0.907
0.613
0.837
0.924
0.926
0.795
0.653
0.818
LPIPS
0.241
0.183
0.189
0.338
0.160
0.126
0.191
0.226
0.329
0.220
OMG-L
Train
19:25
21:16
26:06
20:50
19:14
24:20
22:05
19:22
21:14
21:32
#Gauss
813561
463285
480133
859963
909961
524457
524457
869388
819435
696071
Size
7.69
4.32
4,48
8.23
8.42
4.82
4.82
8.14
7,81
6.52
FPS
476
492
332
422
422
405
$39
468
414
441
PSNR
25.22
31.51
28.78
21.52
26.93
31.15
31.25
27.00
22.69
27.34
0.908
0.839
0.926
0.796
LPIPS
0.239
0.182
0.187
0.334
0,157
0.126
0.191
0.224
0.324
0.218
OMG-XL
Train
20:43
21:54
26:21
22:09
20:23
24:56
22:37
20:22
22:33
22:26
#Gauss
864124
450246
507473
922061
953050
547636
493754
920589
885229
727129
4.22
8.82
4.58
8.44
6.82
FPS
430
465
324
379
422
397
512
435
384
416
Table 14: Per-scene results evaluated on the Tank& Temples 31
and Deep Blending
23 datasets.
Tank&
Deep Blending
Method
Metric
Train
Truck
Avg.
drjohnson
Playroom
Avg.
PSNR
21.78
25.25
23.52
29.37
30.18
29.77
SSIM
0.806
0.878
0.842
0.905
0.910
0.908
LPIPS
0.233
0.144
0.189
0.253
0.253
0.253
OMG-M
Train
12:12
11:30
11:51
17:18
14:51
16:05
IGauus
303187
257649
330418
520385
404237
462311
2.95
3.49
3.22
4.87
3.82
4.34
829
894
PSNR
21.85
25.36
23.60
29, .4
30.32
29.88
SSIM
0.811
0.881
0.846
0.907
0.912
0.910
LPIPS
0.225
0.136
0.181
0.247
0.247
0.247
OMG-L
Train
11:39
11:56
17:39
16:19
#Gauss
369440
442359
405900
627868
485329
$56599
Size
3.58
4.28
3,93
5.86
4.55
5.21
780
770
810
25


Reviews:
[{"review_id": "2FhbuoT8pj", "reviewer": "Reviewer_wotF", "summary": "This paper (OMG) aims at reducing storage and computational demands for 3D scene representation while preserving real-time rendering capability. It tackles redundancy in 3DGS models through a pruning mechanism based on a novel Local Distinctiveness scoring and an attribute compression via Sub-Vector quantization. The scoring identifies essential primitives by combining blending weight importance with an appearance-aware metric that measures color variation among neighboring primitives, to prune primitives. For compression, SVQ decomposes scale, rotation, and appearance features into sub-vectors quantized independently using small, storage-friendly codebooks. OMG uses a lightweight neural field, driven by gaussian positions, captures coarse spatial context to compensate for continuity loss from sparsity. Results demonstrate appreciable storage and training time reduction versus prior art (LocoGS) and rendering at 612 FPS (with an RTX 4090 GPU).", "questions": "Addressed in Strengths and Weaknesses", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. Proposes a novel sub-vector quantization method for compressing primitives, to facilitate rendering with high FPS\n2. Appearance-aware local distinctiveness score for pruning redundant gaussians\n3. Addresses spatial continuity modeling using space features from positions\n\n\nWeaknesses\n1. Exposition: \nThe following points were raised as general questions, due to the lack of adequate explanation\n\n- 600+ FPS claim is with an RTX 4090. What’s the FPS for other methods on the same GPU?\n\n- In sparse point clouds Morton adjacency (which is already an approximate metric) often maps distant points, leading to spurious similarity measures and erroneous pruning. How is it handled? Since the number of points is already low, doesn’t it make sense to use KNN itself?\n\n- The LD metric is based on the outputs after SVQ is applied. Does this not cause compression artifacts to bias the pruning process, i.e., what if geometrically critical gaussians now appear ‘redundant’ because of the quantization noise?\ni. What is done to avoid over-pruning in texture-less regions?\nii. Could table-4 have another row that shows the distortion effects of SVD?\n\n- Is it necessary to compute the LD-metric using Ti/j? How does it work when RGB (DC components of SH) values are used instead for finding local similarity?\n\n- How important are the space features (Fn)? Intuitively, T denotes the DC and V denotes the other SH bands. Please clarify why the positional encoding of a gaussian is needed to derive this?\n\n\n2. Experimentation: The following points were raised due to lack of proper empirical evidence\n\n- Taming-3DGS also reduces the number of gaussians with competitive quality. Why have there been no comparisons? How sensitive is OMG to the initialization?\n- What is OMG-XS performance in MipNeRF360?\n- SVQ uses fixed-length partitioning (M). How does it perform with non-uniform attribute distributions? E.g., high-variation regions like texture edges\n\n3. Ablations\na. How were M and L decided? Is there a sensitivity analysis on the choice of these parameters?\nb. It’s unclear if the improvement in FPS comes from a low number of gaussians or their quantized states. Could there be an ablation where FPS among methods are compared with the same number of primitives?\n\nc. There are several post-processing steps (g-pcc compression, huffman encoding, etc.). How much does it reduce the storage footprint? \n\nTo make the comparison fair, could there be a table where the storage is reported without the above steps or, the above steps are applied to other methods?", "was_revised": true, "final_justification": "After reading the rebuttal from the authors to my comments and other reviewers, I moved my recommendation to Border Line Accept.\nI think  that the paper is a significant contribution to the GS compression paradigm. \n\nTo justify my decision, let me address how the Authors have satisfactorily answered my questions/comments in the rebuttal process.\n\n1. Exposition: The following points were raised as general questions, due to the lack of adequate explanation\n\n- 600+ FPS claim is with an RTX 4090. What’s the FPS for other methods on the same GPU?\n\nSolved, Authors provided a table for both a RTX 4090 and 1080Ti, and they outperformed state of the art by a large margin.\n\n- In sparse point clouds Morton adjacency (which is already an approximate metric) often maps distant points, leading to spurious similarity measures and erroneous pruning. How is it handled? Since the number of points is already low, doesn’t it make sense to use KNN itself?\n\nSolved, Authors provided a good explanation of their choice of Morton order approximation versus Knn, they provided numerical and theoretical motivation.\nThus showing that Morton adjacency was the right choice for their design.\n\n\n- The LD metric is based on the outputs after SVQ is applied. Does this not cause compression artifacts to bias the pruning process, i.e., what if geometrically critical gaussians now appear ‘redundant’ because of the quantization noise? i. What is done to avoid over-pruning in texture-less regions? ii. Could table-4 have another row that shows the distortion effects of SVD?\n\nSolved, Authors clarify that LD scoring is not applied after the primitives have been quantized by SVQ. It was a misunderstanding in my side. \n\n- Is it necessary to compute the LD-metric using Ti/j? How does it work when RGB (DC components of SH) values are used instead for finding local similarity?\n\nSolved, Authors provided a good motivation to why it is necessary to compute the LD-metric using Ti/j. They provided empirical validation too.\n\n\n- How important are the space features (Fn)? Intuitively, T denotes the DC and V denotes the other SH bands. Please clarify why the positional encoding of a gaussian is needed to derive this?\n\nSolved, Authors provided a justification of how important is the space feature for their method. Namely, they do not claim that the space feature should be implemented using PE+MLP. They provided empirical evidence that an extremely small neural field is performant enough. \n\n\n2. Experimentation: The following points were raised due to lack of proper empirical evidence\n- Taming-3DGS also reduces the number of gaussians with competitive quality. Why have there been no comparisons? How sensitive is OMG to the initialization?\n\n Solved, Authors provide a good explanation of why they did not compare with Tamming, and also they provided numbers comparing OMG to Tamming.\n\n- What is OMG-XS performance in MipNeRF360?\n\nSolved\n\n- SVQ uses fixed-length partitioning (M). How does it perform with non-uniform attribute distributions? E.g., high-variation regions like texture edges\n\nSolved. Authors addressed their partitioning strategy through empirical analysis, showing that 2-dimensional sub-vectors offers a favorable trade-off, avoiding excessive computational overhead while achieving high performance\n\n\n3. Ablations a. How were M and L decided? Is there a sensitivity analysis on the choice of these parameters? b. It’s unclear if the improvement in FPS comes from a low number of gaussians or their quantized states. Could there be an ablation where FPS among methods are compared with the same number of primitives?\n\nSolved when Authors answered my previous comment.\n\n - There are several post-processing steps (g-pcc compression, huffman encoding, etc.). How much does it reduce the storage footprint?\n\nSolved, Authors provided that their results show that applying post-processing achieves an overall storage reduction of approximately 30–32%.", "rebuttal": "We sincerely appreciate the reviewers’ thoughtful comments and efforts in reviewing our manuscript. In the following, we address each comment individually, providing detailed explanations and clarifications as required.\n\n**Significance of our contributions**\n \nOur method achieves a **40-50% reduction in storage compared to the current state-of-the-art**, LocoGS (to put this into perspective, for a large-scale and ultra-high resolution 3D digital twin of a city, this would be **from 1 TB to 500 GB**). To further contextualize this gain: in the field of video coding, each major standard over the past two decades, such as **H.264 (2003), H.265 (2013), and H.266 (2020)**, has achieved approximately a **30–50% reduction per generation**, typically at the cost of significantly increased computational complexity. Put differently, our method achieves a decade’s worth of progress without any sacrifices in other important factors, including rendering quality, rendering speed, and training time. We would also like to highlight our training efficiency (LocoGS: 1 hour vs. ours: 20 mins). Even when compared to MiniSplatting, a method not optimized for compression, we introduce **only a 1-minute training time overhead.** While our method has only been tested on standard benchmarks (Mip-NeRF360, Tanks and Temples, and the Deep Blending dataset), we believe the improvements demonstrated on these datasets remain highly relevant and reflect the current interests of the research community. Last but not least, as we already provided the source code in the supplementary materials, **the codes and the model weights will be publicly available.**\n\n**[W1-5/2-2/3-c] Already Provided Results**\n\nWe respectfully note that the following key points have already been addressed in the main manuscript or the appendix:\n- **[W1-5] Importance of the space feature:** The space feature F, obtained via positional encoding (PE) and a tiny MLP, is one of the core components of OMG. It is designed to capture local continuity among sparse Gaussians, as elaborated in Lines 169–175 of the main paper. Since this feature encodes the basic structural information of Gaussians, the per-Gaussian features can concentrate more on representing fine details. Importantly, we do not claim that the space feature should be implemented using PE+MLP; rather, we demonstrate that even an extremely small neural field (with 6K weight parameters) is sufficiently effective. The quantitative impact of this space feature is reported in Table 4.\n\n- **[W2-2] OMG-XS performance on the Mip-NeRF 360 dataset:**\n  The performance of OMG-XS is reported in Table 1, the main table in our manuscript. OMG-XS achieves comparable rendering quality to the previous state-of-the-art method, LocoGS, while requiring nearly half the storage.\n- **[W3-c] Effectiveness of post-processing:**\n  We have analyzed the impact of post-processing techniques in Appendix 4, with corresponding quantitative results shown in Table 3 of the appendix. The results show that applying post-processing achieves an overall storage reduction of approximately 30–32%.\n\n**[W1-1] Other methods' FPS with RTX 4090**\n\nWe have measured the FPS of LocoGS (with COLMAP initialization for a fair comparison) on an NVIDIA RTX 4090 and a lower-end GPU, NVIDIA GTX 1080 Ti. As shown in the results, OMG achieves significantly faster rendering speeds.\n| Mip-NeRF 360 | 4090 | 1080Ti | PSNR  | SSIM  | LPIPS | #Gauss | Size (MB) |\n|--------------|-----|--------|--------|--------|--------|--------|--------|\n| OMG-XS       | 612 | 106 | 27.06  | 0.807  | 0.243  | 0.43M  | 4.06   |\n| LocoGS       | 396 |  73 | 27.09  | 0.798  | 0.250  | 1.04M  | 7.96   |\n| OMG-XL       | 416 | 77 | 27.34  | 0.819  | 0.218  | 0.73M  | 6.82   |\n| LocoGS       | 325 | 59 | 27.37  | 0.807  | 0.236  | 1.44M  | 15.1   |\n\n**[W1-2/1-3/1-4] Clarification of LD scoring**\n\nTo ensure a fair comparison in LD scoring experiments, we strictly matched the number of Gaussians by applying Top-K sampling to the importance scores, aligning it exactly with our model.\n- **[W1-2] Usage of Morton order approximation instead of KNN**\n    The table below presents a comparison between Morton-order approximation and KNN-based scoring on 9 scenes from the Mip-NeRF 360 dataset. The results are nearly identical, demonstrating that Morton order effectively approximates KNN while offering significantly faster runtime.\n\n    | Mip-NeRF 360     | PSNR  | SSIM  | LPIPS | Time (ms) |\n    |------------------|-------|--------|--------|-------|\n    | OMG-XS           | 27.06 | 0.807  | 0.243  | 4100 |\n    | Morton -> KNN    | 27.07 | 0.808  | 0.241  | 28   |\n\n    While we agree with the reviewer that using KNN is reasonable in our current setup (LD scoring once at 20K with fewer than 1M Gaussians), our decision to adopt Morton order is motivated by the need for scalability and generalizability. In more complex or larger-scale scenes, the number of Gaussians can increase significantly during earlier training stages. In such cases, KNN becomes impractical due to its high memory usage and computational complexity of $O(N^2)$. Morton order, by contrast, provides a well-approximated yet efficient alternative that scales better with the number of primitives.\n- **[W1-3] LD scoring and SVQ**\n    LD scoring is not applied after the primitives have been quantized by SVQ. Importance-based pruning with LD scoring is performed at the 20K iteration (as noted in Line 25 of the appendix), whereas SVQ is applied from 29K iteration (during the final 1K iterations). Therefore, SVQ introduces no bias into the pruning process.\n\n- **[W1-4] LD scoring with RGB**\n    The static appearance feature *T* is used to represent both RGB values (the DC component of spherical harmonics) and opacity. Since T shares similar characteristics with RGB, LD scoring based on RGB also yields comparable but slightly less performance.\n    | Mip-NeRF 360 | PSNR  | SSIM  | LPIPS |\n    |--------------|--------|--------|--------|\n    | OMG-XS       | 27.06  | 0.807  | 0.243  |\n    | T → RGB      | 26.99  | 0.807  | 0.243  |\n    | OMG-M        | 27.21  | 0.814  | 0.229  |\n    | T → RGB      | 27.19  | 0.814  | 0.229  |\n\n**[W1-3-ii] Including the effect of SVQ in Table 4**\n\nS-VQ achieves substantial storage reduction while preserving the original performance. We appreciate the reviewer’s suggestion and will include this result in Table 4 in the final version of the paper.\n| Mip-NeRF 360 | PSNR  | SSIM  | LPIPS | #Gauss | Size (MB) |\n|--------------|--------|--------|--------|--------|--------|\n| OMG-M       | 27.21  | 0.814  | 0.229  | 0.56M  | 5.31   |\n| w/o SVQ      | 27.26  | 0.817  | 0.226  | 0.56M  | 26.1   |\n| OMG-XS       | 27.06  | 0.807  | 0.243  | 0.43M  | 4.06   |\n| w/o SVQ      | 27.06  | 0.809  | 0.241  | 0.43M  | 19.8   |\n\n**[W2-1-i] Comparison with Taming-3DGS**\n\nTaming-3DGS is primarily designed to accelerate training and achieves faster training times compared to our method. However, it does not incorporate any attribute compression, and yet it shows inferior rendering quality compared to OMG. In contrast, OMG achieves substantially better rendering quality while also requiring significantly less storage.\n\n| Mip-NeRF 360 | PSNR  | SSIM  | LPIPS | #Gauss | Size (MB)  | \n|--------------|--------|--------|--------|--------|---------|\n| Taming-3DGS  | 27.31  | 0.801  | 0.252  | 0.63M  | 141.8   |\n| OMG-M       | 27.21  | 0.814  | 0.229  | 0.56M  | 5.31   |\n| OMG-XL       | 27.34  | 0.819  | 0.218  | 0.73M  | 6.82   |\n\n**[W2-1-ii] Sensitivity to initialization**\n\nSince OMG is built upon Mini-Splatting, it suffers from performance degradation without initialization. However, OMG can also be applied to other baseline methods such as 3DGS-MCMC, which exhibit stable performance regardless of COLMAP initialization. The following result highlights the broader applicability of OMG.\n\n| Mip-NeRF    | #G    | PSNR  | SSIM  | LPIPS | Size   |\n|-------------|-------|-------|-------|-------|--------|\n| 3DGS-MCMC   | 500 K | 27.42 | 0.807 | 0.248 | 115 MB |\n| MCMC+OMG    | 500 K | 27.21 | 0.797 | 0.256 | 5.1 MB |\n| 3DGS-MCMC   | 1 M   | 27.83 | 0.823 | 0.221 | 230 MB |\n| MCMC+OMG    | 1 M   | 27.63 | 0.813 | 0.227 | 10.0 MB |\n\n**[W2-3/W3-a] Clarification of M and L**\n\nAs noted by Reviewer WdNJ, SVQ is proposed to strike a better balance among computational cost, storage efficiency, and representational precision for Gaussian attributes. OMG requires four types of vectors: a 3-dimensional scale vector, a 4-dimensional rotation vector, and two 3-dimensional appearance vectors. As demonstrated in Table 5, although these vectors are already short, applying naive vector quantization still demands significant computation due to the need for large codebooks. Through empirical analysis, we found that using 2-dimensional sub-vectors offers a favorable trade-off, avoiding excessive computational overhead while achieving high performance. The scale vectors, having an odd dimension (3), are instead quantized using scalar quantization.\n\nFurthermore, by dividing the vector into sub-vectors, the value distribution within each sub-vector becomes less complex. This simplifies the statistical characteristics of the data, allowing Huffman encoding to exploit redundancy more effectively. As a result, the final storage size remains stable across varying bit allocations, making the method less sensitive to quantization bit-width choices and more robust to distributional variations.\n\n**[W3-b] Quantization and rendering speed**\n\nQuantization does not affect rendering speed, as rendering in this field is commonly performed on GPUs using floating-point operations. Therefore, the improved speed of OMG results from the reduced number of primitives rather than from quantization.\n\n| Mip-NeRF 360 | PSNR   | #Gauss | FPS |\n|--------------|--------|--------|--------|\n| OMG-XS       | 27.06  | 0.43M  |  613  |\n| w/o SVQ      | 27.06  | 0.43M  |  601  |"}, {"review_id": "o14gOjp4js", "reviewer": "Reviewer_WdNJ", "summary": "This paper addresses the significant storage and computational overhead associated with 3DGS. While prior work has focused on compressing the attributes of a large set of Gaussians, this paper tackles the more challenging problem of achieving high-quality results with a minimal number of primitives. The authors argue that as the set of Gaussians becomes sparse, each primitive becomes more sensitive to compression loss, and attribute irregularity increases, making traditional compression less effective.\nThis paper presents Optimized Minimal Gaussians (OMG), a compact 3DGS representation that reduces both the number of Gaussians and storage requirements. OMG minimizes redundancy by selecting distinct Gaussians and introduces a precise, compact attribute representation. A sub-vector quantization technique further improves efficiency with minimal overhead. OMG achieves nearly 50% storage reduction than the SOTA and enables 600+ FPS rendering on NVIDIA RTX 4090 GPU without compromising quality.", "questions": "1. Size, FPS, PSNR is similar for LocoGS-S and OMG-XS. Does that imply that there's not enough significant performance gains?\n\n2. *Table 3:* LocoGS-S uses more than twice the number of Gaussians yet achieves a similar storage size with comparable performance. Does this imply that LocoGS has a better compression ratio than OMG?\n\n3. *Line 261-262:* It's unclear whether these statements refer specifically to the Mip-NeRF dataset, but they do not hold consistently when considering all datasets across Tables 1 and 2.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**A. Strengths:**\n\n1. The paper addresses a critical bottleneck in the practical application of 3DGS. As 3DGS becomes more widely adopted, methods for extreme compression and efficient rendering are quite important. The focus on minimizing the number of primitives, rather than just compressing their attributes is an important direction.\n\n2. The hybrid architecture for attribute representation is clever. It elegantly balances the need for per-primitive specificity (to capture geometric detail) with the efficiency of a continuous representation (for appearance), which is perfectly suited for the sparse Gaussian problem. However, discussing other design choices would have been useful too.\n\n3. The Sub-Vector Quantization (SVQ) method is a practical and effective solution to a well-known problem in quantization, providing a better balance between computational cost, storage, and precision than prior approaches. Bringing it to 3DGS totally makes sense.\n\n4. I also like the local distinctiveness scoring metric. It’s an intuitive and direct way to handle redundancy among nearby Gaussians.\n\n5. The paper is well-written and easy to follow. The core ideas are explained clearly.\n\n6. Good literature review.\n\n\n**B. Weaknesses:**\n\n1. The paper misses out a lot of important qualitative and quantitative comparisons as discussed below.\n\n2. Qualitative comparisons are only conducted with vanilla 3DGS; there are no visual comparisons with state-of-the-art methods such as LocoGS or HAC in either the main paper or the supplementary materials. Including these comparisons—particularly in the form of rendered videos in the supplementary—would have been a valuable addition.\n\n3. The results for LocoGS-L on the Tanks & Temples and Deep Blending datasets are missing. Including these is important to fully assess the performance gains of the proposed method across a diverse range of datasets.\n\n4. The scoring relies on finding K-nearest neighbors, which is approximated using a Morton order sort for efficiency. The potential impact of this approximation (i.e., not using the true geometric neighbors) on pruning performance is not analyzed.\n\n5. The metric is based on the L1 norm of the difference between static appearance features (T_i​). This overlooks the potential importance of view-dependent features in distinguishing primitives, and the choice of the L1 norm over other distance metrics (e.g., L2) is also not motivated.\n\n6. The LD term is multiplicatively combined with the baseline importance score. This means a Gaussian that is highly unique but never the single most dominant contributor to any ray will have its importance score zeroed out, which may not be the most idea solution.\n\n7. The decision to encode geometry directly while using a hybrid model for appearance is a fixed design choice. The paper would be stronger if it discussed or ablated this choice.\n\n8. Tables 1 and 2 lacks comparison with Mini-Splatting. Also visual comparisons will be greatly helpful in identifying the benefits of the proposed method over the baseline.", "was_revised": true, "final_justification": "This is a good paper with strong potential to influence downstream research in 3DGS.", "rebuttal": "We sincerely appreciate the reviewers’ thoughtful comments and efforts in reviewing our manuscript. In the following, we address each comment individually, providing detailed explanations and clarifications as required.\n\n**Significance of our contributions**\n \nOur method achieves a **40-50% reduction in storage compared to the current state-of-the-art**, LocoGS (to put this into perspective, for a large-scale and ultra-high resolution 3D digital twin of a city, this would be **from 1 TB to 500 GB**). To further contextualize this gain: in the field of video coding, each major standard over the past two decades, such as **H.264 (2003), H.265 (2013), and H.266 (2020)**, has achieved approximately a **30–50% reduction per generation**, typically at the cost of significantly increased computational complexity. Put differently, our method achieves a decade’s worth of progress without any sacrifices in other important factors, including rendering quality, rendering speed, and training time. We would also like to highlight our training efficiency (LocoGS: 1 hour vs. ours: 20 mins). Even when compared to MiniSplatting, a method not optimized for compression, we introduce **only a 1-minute training time overhead.** While our method has only been tested on standard benchmarks (Mip-NeRF360, Tanks and Temples, and the Deep Blending dataset), we believe the improvements demonstrated on these datasets remain highly relevant and reflect the current interests of the research community. Last but not least, as we already provided the source code in the supplementary materials, **the codes and the model weights will be publicly available.**\n\n**[Q1/2] Performance improvement over LocoGS**\n\n- **[Q1] OMG-XS is 48.6% smaller than LocoGS-S:** We believe this gain is not trivial. For instance, in the field of video coding, each generation has achieved approximately a 2× improvement in compression ratio: H.265 (2013) roughly doubles the efficiency of H.264 (2003), and H.266 (2020) further doubles that of H.265, while imposing significanly increased computation. While 3DGS compression continues to be highly optimized, we would like to argue that the additional compression gains achieved by our method are substantial, especially when considering the accompanying improvements in rendering speed.\n    | Mip-NeRF 360 | PSNR   | Size (MB) | FPS (3090) |\n    |--------------|--------|--------|--------|\n    | LocoGS-S     | 27.04  | 7.90  |  310  |\n    | OMG-XS       | 27.06  | 4.06 (-48.6%)  |  350  |\n    \n    Furthermore, while a 4 MB reduction in absolute terms may appear trivial, it becomes highly significant in large-scale scenes. OMG leverages per-Gaussian features to represent the irregularity of Gaussians, making it inherently scalable and well-suited for complex environments. To demonstrate this, we tuned the original 3DGS-MCMC  for a large-scale scene, the NYC scene from the Zip-NeRF  dataset, and applied OMG. Our method achieves superior performance while drastically reducing storage requirements. We believe large-scale scenes presents a compelling challenge to other compression approaches that rely on compressibility and locality, and OMG’s scalability opens new possibilities for efficient modeling of extremely large scenes.\n    \n    | NYC         | #G  | PSNR  | SSIM  | LPIPS | Size   |\n    |-------------|-----|--------|--------|--------|--------|\n    | Zip-NeRF    | -   | 28.42  | 0.850  | 0.281  | 607 MB |\n    | 3DGS-MCMC        | 3 M | 27.77  | 0.857  | 0.295  | 675 MB |\n    | MCMC+OMG    | 3 M | 27.82  | 0.853  | 0.291  | 29 MB  |\n    \n- **[Q2] MB per Gaussian is not the key factor:**  To reduce the number of Gaussians in LocoGS, we incorporate Mini-Splatting’s densification strategy while retaining LocoGS’s attribute representation. Although this approach successfully reduces storage by decreasing the number of Gaussians, LocoGS fails to maintain high-quality rendering. This limitation arises because a single neural field struggles to represent multiple attributes of sparse Gaussians effectively. A similar challenge is observed in Compact-3DGS as well, as demonstrated in Table 1 of the appendix.\n\n    | Mip-NeRF     | #G    | PSNR  | SSIM  | LPIPS | Size    |\n    |--------------|-------|--------|--------|--------|---------|\n    | MS+Loco-S    | 946 K | 27.04  | 0.804  | 0.225  | 9.1 MB  |\n    | MS+Loco-S    | 537 K | 26.52  | 0.789  | 0.257  | 6.2 MB  |\n    | MS+Loco-L    | 923 K | 27.35  | 0.815  | 0.214  | 14.2 MB |\n    | MS+Loco-L    | 519 K | 26.76  | 0.799  | 0.247  | 11.3 MB |\n    | OMG-XS       | 427 K | 27.06  | 0.807  | 0.243  | 4.1 MB  |\n    | OMG-XL       | 727 K | 27.34  | 0.819  | 0.218  |  6.8 MB |\n    \n**[Q3] Clarification of L. 261-262** \n\nLines 261–262 refer to the results on the Mip-NeRF 360 dataset. For a more comprehensive evaluation, we assessed overall performance on Mip-NeRF 360 (9 scenes), Tank & Temples (2 scenes), and Deep Blending (2 scenes). OMG-M achieves higher PSNR and SSIM than LocoGS-S, while reducing storage by 37%. Furthermore, OMG-XL achieves superior SSIM compared to LocoGS-L, with a smaller storage footprint than LocoGS-S. We will revise the final version accordingly to clarify this point.\n\n| Mip-NeRF 360 | PSNR  | SSIM  | LPIPS | Size        |\n|--------------|--------|--------|--------|-------------|\n| LocoGS-S     | 26.98  | 0.827  | 0.225  | 7.66        |\n| OMG-M        | 27.03  | 0.833  | 0.226  | 4.84 (-37%) |\n| LocoGS-L     | 27.22  | 0.834  | 0.214  | 13.6        |\n| OMG-XL       | 27.14  | 0.837  | 0.216  | 6.23 (-54%) |\n\n**[W4/5/6] Clarification of LD scoring**\n\nTo ensure a fair comparison in LD scoring experiments, we strictly matched the number of Gaussians by applying Top-K sampling to the importance scores, aligning it exactly with our model. Since the effect of LD scoring is more pronounced in smaller models, we adopt OMG-XS as our baseline for evaluation.\n- **[W4] Usage of Morton order approximation instead of KNN**\n    The table below presents a comparison between Morton-order approximation and KNN-based scoring on 9 scenes from the Mip-NeRF 360 dataset. The results are nearly identical, demonstrating that Morton order effectively approximates KNN while offering significantly faster runtime.\n\n    | Mip-NeRF 360     | PSNR  | SSIM  | LPIPS | Time (ms) |\n    |------------------|-------|--------|--------|-------|\n    | OMG-XS           | 27.06 | 0.807  | 0.243  | 4100 |\n    | Morton -> KNN    | 27.07 | 0.808  | 0.241  | 28   |\n\n    As our current setup applies LD scoring only once at the 20K iteration with fewer than 1 million Gaussians, KNN is affordable and potentially more reliable in this context. However, our decision to adopt Morton order is motivated by the need for scalability and generalizability. In more complex or larger-scale scenes, the number of Gaussians can increase significantly during earlier training stages. In such cases, KNN becomes impractical due to its high memory usage and computational complexity of $O(N^2)$. Morton order, by contrast, provides a well-approximated yet efficient alternative that scales better with the number of primitives.\n\n- **[W5] Design choice for LD scoring**\nWe conducted an ablation study on the design choices for LD scoring and found that the results remain nearly unchanged. We would like to include this result in the final version of the paper.\n    | Mip-NeRF 360   | PSNR  | SSIM  | LPIPS |\n    |----------------|--------|--------|--------|\n    | OMG-XS         | 27.06  | 0.807  | 0.243  |\n    | T -> T&V       | 27.06  | 0.807  | 0.243  |\n    | L2             | 27.05  | 0.806  | 0.244  |\n    \n- **[W6] LD scoring and the most dominant contributor**\nAs the reviewer pointed out, LD scoring is applied to Gaussians that have been the dominant contributor for at least one ray. This design reflects our objective to construct a minimal yet effective set of Gaussians. Given the limited number of Gaussians, further pruning typically leads to performance degradation. However, LD scoring enables the identification of less distinctive Gaussians whose contributions can be compensated by learning (generally extending) nearby Gaussians over the following iterations. The effectiveness of this approach is validated in Table 4 and Figure 5.\n\n**[W7] Hybrid model for geometry**\n\nConsidering that the geometry of sparse Gaussians exhibits weak spatial continuity, we chose not to fuse the space feature into the geometry representation. We agree that an ablation study on this aspect would be valuable. However, incorporating geometry predicted by an MLP is not feasible in our setting, as it cannot be properly initialized during training (at 15K iteration). Randomly initialized geometry leads to out-of-memory issues, especially due to the scale of each Gaussian. Despite considerable effort, we found it challenging to train the model under such conditions.\n\n**[W1/2/3/8] More results**\n\nWe sincerely appreciate the reviewer’s suggestion and will include the following results in the final version of the paper.\n- **[W2/8] Qualitative results compared to other methods**\nDue to the strict rebuttal guidelines of NeurIPS, we are unable to include qualitative results here. However, we will certainly incorporate them in the final version.\n- **[W3/8] LocoGS-L in Table 2 and Mini-Splatting in Table 1,2**\n\n    | Tank&Temples   | PSNR  | SSIM  | LPIPS | Size  | FPS |\n    |--------|-------|-------|--------|-------|------|\n    | LocoGS-L   | 23.84 | 0.852 | 0.161  | 12.34 | 311  |\n    | Mini-Splatting   | 23.41 | 0.846 | 0.180  | 67.6 | (1095)  |\n\n    | Deep Blending | PSNR  | SSIM  | LPIPS | Size   | FPS |\n    |--------|-------|-------|--------|-------|------|\n    |  LocoGS-L  | 30.11 | 0.906 | 0.243  | 13.38 | 297  |\n    |   Mini-Splatting  | 30.04 | 0.910 | 0.241  | 124.9 | (902)  |\n\n    | Mip-NeRF 360 | PSNR  | SSIM  | LPIPS | Size   | FPS |\n    |--------|-------|-------|--------|-------|------|\n    |   Mini-Splatting  | 27.39 | 0.822 | 0.216  | 119.5 | (601)  |"}, {"review_id": "KqOIS2sOJl", "reviewer": "Reviewer_cPXW", "summary": "This paper introduces Optimized Minimal Gaussians (OMG), a framework for efficient 3D scene representation using significantly fewer Gaussian primitives. It innovates in three key areas: (1) a local distinctiveness-based importance metric for selecting informative Gaussians, (2) a compact and hybrid attribute representation combining per-Gaussian features and spatial-aware neural fields, and (3) sub-vector quantization (SVQ) to compress attributes with low codebook complexity. Experimental results across multiple benchmarks demonstrate strong compression performance (up to 50% smaller models) while preserving rendering quality and enabling real-time rendering at over 600 FPS.", "questions": "1. How sensitive is the distinctiveness score to the choice of λ or the number of nearest neighbors K? Is there a principled way to set these values?\n2. How does SVQ compare to product quantization or learned codebooks in terms of training stability and encoding speed?\n3. Can OMG handle dynamic 3D scenes where Gaussian properties evolve over time? If not, what modifications would be needed?\n4. The neural field used to compute the space feature is described as lightweight—how many parameters does it use, and how critical is it to the final performance?\n5. How does OMG perform on mobile GPUs or CPUs? Can it realistically enable interactive rendering in those contexts?", "limitations": "Yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n1. Novel contribution in Gaussian pruning: The introduction of a local distinctiveness metric addresses a practical challenge in sparse Gaussian selection, improving pruning efficiency.\n2. Efficient representation via SVQ: The use of sub-vector quantization is well-motivated and achieves a good trade-off between compression and computational cost.\n3. Practical utility: The system supports real-time rendering at >600 FPS with minimal memory usage, making it suitable for deployment on edge devices.\n\nWeaknesses:\n1. Lack of theoretical analysis: The paper introduces important ideas (e.g., distinctiveness scoring, SVQ), but lacks formal analysis or theoretical guarantees about convergence, generalization, or approximation.\n2. Limited novelty in architecture: While effective, parts of the pipeline (e.g., feature fusion with MLPs) resemble prior works, especially Mini-Splatting and Compact-3DGS, and the innovation is largely incremental.\n3. No generalization test beyond rendering: The method is tightly scoped to rendering tasks; it would be stronger if generalized to other downstream 3D vision applications.", "was_revised": true, "final_justification": "Thanks for the authors' response. \nWhile the paper presents notable technical contributions, the direct comparison to video coding standards (e.g., H.266 vs. H.264)  in the response is not suitable. The claimed 40-50% improvement appears to be derived from a single operating point, whereas video coding advancements are typically evaluated using the BDBR (Bjøntegaard Delta Bit Rate) metric, which rigorously quantifies gains across multiple quantization points (typically four). This discrepancy in evaluation methodology may lead to an overestimation of the contribution.  \nAnyway, I raise my score.", "rebuttal": "We sincerely appreciate the reviewers’ thoughtful comments and efforts in reviewing our manuscript. In the following, we address each comment individually, providing detailed explanations and clarifications as required.\n\n**Significance of our contributions**\n \nOur method achieves a **40-50% reduction in storage compared to the current state-of-the-art**, LocoGS (to put this into perspective, for a large-scale and ultra-high resolution 3D digital twin of a city, this would be **from 1 TB to 500 GB**). To further contextualize this gain: in the field of video coding, each major standard over the past two decades, such as **H.264 (2003), H.265 (2013), and H.266 (2020)**, has achieved approximately a **30–50% reduction per generation**, typically at the cost of significantly increased computational complexity. Put differently, our method achieves a decade’s worth of progress without any sacrifices in other important factors, including rendering quality, rendering speed, and training time. We would also like to highlight our training efficiency (LocoGS: 1 hour vs. ours: 20 mins). Even when compared to MiniSplatting, a method not optimized for compression, we introduce **only a 1-minute training time overhead.** While our method has only been tested on standard benchmarks (Mip-NeRF360, Tanks and Temples, and the Deep Blending dataset), we believe the improvements demonstrated on these datasets remain highly relevant and reflect the current interests of the research community. Last but not least, as we already provided the source code in the supplementary materials, **the codes and the model weights will be publicly available.**\n\n**[Q1] Sensitivity of λ and K**\n\nWe have tested sensitivity of hyperparameters for LD scoring. To ensure a fair comparison in this experiment, we strictly matched the number of Gaussians by applying Top-K sampling to the importance scores, aligning it exactly with our model. Since the effect of LD scoring is more pronounced in smaller models, we adopt OMG-XS as our baseline for evaluation.\n\nThe following results show similar performance across different values of λ and K, with our chosen configuration (λ, K = 2) yielding the best results. Although these values were determined empirically, we demonstrate that this setting consistently shows strong performance and generalizes well across a variety of scenes.\n\n| λ   | K | PSNR  | SSIM  | LPIPS |\n|-----|---|--------|--------|--------|\n| **2**   | **2** | **27.06** | **0.807** | **0.243** |\n| 2   | 1 | 27.05 | 0.807 | 0.243 |\n| 2   | 4 | 27.00 | 0.806 | 0.244 |\n| 1.5 | 2 | 26.97 | 0.806 | 0.244 |\n| 2.5 | 2 | 26.98 | 0.807 | 0.243 |\n\n**[Q5] FPS on a weaker device**\n\nWe have measured the FPS of our method on a low-end GPU, NVIDIA GTX 1080Ti, compared to LocoGS (with COLMAP initialization for a fair comparison). Thanks to the minimal number of Gaussians, OMG-XS achieves over 100 FPS even on this low-end hardware.\n\n| Mip-NeRF 360 | 1080Ti | PSNR  | SSIM  | LPIPS | #Gauss | Size (MB) |\n|--------------|--------|--------|--------|--------|--------|--------|\n| OMG-XS       | 106 | 27.06  | 0.807  | 0.243  | 0.43M  | 4.06   |\n| LocoGS       |  73 | 27.09  | 0.798  | 0.250  | 1.04M  | 7.96   |\n| OMG-XL       | 77 | 27.34  | 0.819  | 0.218  | 0.73M  | 6.82   |\n| LocoGS       | 59 | 27.37  | 0.807  | 0.236  | 1.44M  | 15.1   |\n\n**[Q4] Effectiveness of neural field**\n\nThe neural field to represent the space feature is constructed by positional encoding and a tiny MLP with 6K weight parameters, The quantitative impact of space feature represented by the neural field is reported in Table 4.\n\n| Method             | PSNR  | SSIM  | LPIPS | #Gauss | Size |\n|--------------------|-------|-------|-------|--------|------|\n| OMG-M              | 27.21 | 0.814 | 0.229 | 0.56M  | 5.31 |\n| w/o Space feature  | 26.96 | 0.811 | 0.232 | 0.59M  | 5.58 |\n| OMG-XS             | 27.06 | 0.807 | 0.243 | 0.43M  | 4.06 |\n| w/o Space feature  | 26.85 | 0.804 | 0.246 | 0.44M  | 4.17 |\n\n\n**[Q2] Training strategy of SVQ**\n\nAs referenced in L.197, SVQ is motivated by PQ, sharing the core principle of dividing the original vector into multiple subvectors and applying vector quantization independently to each. However, we introduce SVQ into the 3DGS literature with a novel design that offers scalability across multiple attributes and an efficient training strategy. Specifically, SVQ is applied once, followed by short 1K fine-tuning steps while keeping the quantization indices fixed, which significantly simplifies training. In contrast, prior works using vector quantization, such as CompGS [1] and Compact-3DGS [2], suffer from the high complexity of learning codebooks with VQ iterations, leading to significantly increased training times compared to the original 3DGS, even though they utilize only about one-third of the Gaussians. Our SVQ approach avoids such overhead and enables efficient training, as demonstrated in Table 3 of the main paper.\n\n**[Q3] OMG for dynamic scenes**\n\nThe OMG attribute representation is applicable to all 3DGS-based methods, as it flexibly represents attributes with spatial locality (via space features and SVQ) as well as those without (using SVQ alone). This generalizability is supported by the following results, where OMG is applied to another baseline, 3DGS-MCMC [3]. We believe the same principle can extend to dynamic scenes; however, the process of obtaining a minimal set of Gaussians for such scenes remains an open question and we left this for future work.\n\n| Mip-NeRF    | #G    | PSNR  | SSIM  | LPIPS | Size   |\n|-------------|-------|-------|-------|-------|--------|\n| 3DGS-MCMC   | 500 K | 27.42 | 0.807 | 0.248 | 115 MB |\n| MCMC+OMG    | 500 K | 27.21 | 0.797 | 0.256 | 5.1 MB |\n| 3DGS-MCMC   | 1 M   | 27.83 | 0.823 | 0.221 | 230 MB |\n| MCMC+OMG    | 1 M   | 27.63 | 0.813 | 0.227 | 10.0 MB|\n\n**[W2] Architectural novelty**\n\nAs noted by Reviewer WdNJ, the hybrid architecture for attribute representation is a clever design that elegantly balances the need for per-primitive specificity with the efficiency of continuous representation. This design makes OMG inherently scalable and well-suited not only for sparse Gaussians but also for complex environments. To validate this, we tuned the original 3DGS-MCMC [3] for a large-scale scene, the NYC scene from the Zip-NeRF [4] dataset, and applied OMG. Our method achieves superior performance while drastically reducing storage requirements. We believe large-scale scenes presents a compelling challenge to other compression approaches that rely on compressibility and locality, and OMG’s scalability opens new possibilities for efficient modeling of extremely large scenes. In this regard, we argue that the OMG architecture represents a distinct and novel contribution compared to existing approaches.\n| NYC         | #G  | PSNR  | SSIM  | LPIPS | Size   |\n|-------------|-----|--------|--------|--------|--------|\n| Zip-NeRF    | -   | 28.42  | 0.850  | 0.281  | 607 MB |\n| 3DGS-MCMC   | 3 M | 27.77  | 0.857  | 0.295  | 675 MB |\n| MCMC+OMG    | 3 M | 27.82  | 0.853  | 0.291  | 29 MB  |\n\n**[W3] Generalization test**\n\nAlthough our experiments focus on the rendering task, the significance of our contributions extends beyond it. When 3DGS models are used as inputs for downstream tasks, the efficiency of the representation, particularly in achieving high fidelity with a small number of Gaussians and attribute parameters, is directly tied to training and inference complexity. And this issue is exacerated in large-scale scenes, while we prove OMG's generalization ability to large-scale scenes above. We believe that the effective minimal-set Gaussian representation proposed in this paper can benefit a wide range of downstream applications, representing a promising direction for future research.\n\n[1] Navaneet, K. L., et al. Compgs: Smaller and faster gaussian splatting with vector quantization. In European Conference on Computer Vision, 2024.\n\n[2] Lee, J. C., et al. Compact 3d gaussian representation for radiance field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.\n\n[3] Kheradmand, S. et al. 3d gaussian splatting as markov chain monte carlo. Advances in Neural Information Processing Systems, 2024.\n\n[4] Barron, J. T., et al.  Zip-nerf: Anti-aliased grid-based neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023."}, {"review_id": "aZQMEXLmXO", "reviewer": "Reviewer_MiKC", "summary": "Overall, this submission focuses on the problem of 3D Gaussian Splatting compression. It in specific focuses on the compression of Gaussian primitive attributes given the existence of minimal Gaussian primitives.", "questions": "Overall, the reviewer is currently around the borderline level for this submission. Below are the current concerns.\n\n1. Firstly, before section 3.1, besides background about Gaussian Splatting, the authors are also suggested to briefly review how existing methods leverage neural fields. From the reviewer's perspective, this can help reducing the reading difficult of this submission.\n\n2. In lines 207-211, the authors claim that they perform SVQ seperately for different geometric attributes while unifiedly for the appearancee features. The authors are expected to better elaborate on this part, i.e., explain the reason behind this difference.\n\n3. The reviewer is a bit confused over the claim in line 232-233. Specifically, since the final rendering is achieved through the blending (aggregation) of several Gaussian kernels, the reviewer is confused that, why the existence of multiple Gaussians with near-identical contributions can be redundant. It seems to the reviewer that even if they have near-identical contributions, removing one (or several) of them does not mean that their aggregated contribution can be kept. The reviewer thus appreciates more detailed explanation for this part.\n\n4. Lastly, if the reviewer is not wrong, the authors seem to build their model on Mini-Splatting (see line 246). While this is fine, the reviewer also appreciates the application on other baselines to better demonstrate the method's generalizability and enhance the experiment's comprehensiveness. Note that, from the reviewer's perspective, this can be especially important considering that the authors claim in Appendix in the limitation section that they believe that OMG could provide a primising direction when compined with effective large-scene representation. In this case, the reviewer believes that the authors at least need to show the generalization ability of OMG beyond a certain single baseline.\n\nGiven the above concerns, the reviewer currently is at the borderline level for this submission.", "limitations": "The submission has properly discussed its limitations while the reviewer believes that something may need to be done to better show their potential in addressing the limitations (see the last question above).", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n\n1. This submission is in general easy-to-follow.\n\n2. This submission achieves relatively good experimental results.\n\nWeaknesses:\n\n(See the question section for more details)", "was_revised": true, "final_justification": "After reading the rebuttal, I believe that my concerns are largely addressed. I thus raise my score from 3 to 4", "rebuttal": "We sincerely appreciate the reviewers’ thoughtful comments and efforts in reviewing our manuscript. In the following, we address each comment individually, providing detailed explanations and clarifications as required.\n\n**Significance of our contributions**\n \nOur method achieves a **40-50% reduction in storage compared to the current state-of-the-art**, LocoGS (to put this into perspective, for a large-scale and ultra-high resolution 3D digital twin of a city, this would be **from 1 TB to 500 GB**). To further contextualize this gain: in the field of video coding, each major standard over the past two decades, such as **H.264 (2003), H.265 (2013), and H.266 (2020)**, has achieved approximately a **30–50% reduction per generation**, typically at the cost of significantly increased computational complexity. Put differently, our method achieves a decade’s worth of progress without any sacrifices in other important factors, including rendering quality, rendering speed, and training time. We would also like to highlight our training efficiency (LocoGS: 1 hour vs. ours: 20 mins). Even when compared to MiniSplatting, a method not optimized for compression, we introduce **only a 1-minute training time overhead.** While our method has only been tested on standard benchmarks (Mip-NeRF360, Tanks and Temples, and the Deep Blending dataset), we believe the improvements demonstrated on these datasets remain highly relevant and reflect the current interests of the research community. Last but not least, as we already provided the source code in the supplementary materials, **the codes and the model weights will be publicly available.**\n\n**[Q4] Generalization ability**\n\nWe have applied OMG to 3DGS-MCMC [1], a method well-known for effective densification. The following result highlights the broader applicability of OMG.\n\n| Mip-NeRF    | #G    | PSNR  | SSIM  | LPIPS | Size   |\n|-------------|-------|-------|-------|-------|--------|\n| 3DGS-MCMC   | 500 K | 27.42 | 0.807 | 0.248 | 115 MB |\n| MCMC+OMG    | 500 K | 27.21 | 0.797 | 0.256 | 5.1 MB |\n| 3DGS-MCMC   | 1 M   | 27.83 | 0.823 | 0.221 | 230 MB |\n| MCMC+OMG    | 1 M   | 27.63 | 0.813 | 0.227 | 10.0 MB|\n\nFurthermore, OMG leverages per-Gaussian features to represent the irregularity of Gaussians, making it inherently scalable and well-suited also for complex environments. To demonstrate this, we tuned the original 3DGS-MCMC for a large-scale scene, the NYC scene from the Zip-NeRF [2] dataset, and applied OMG. Our method achieves superior performance while drastically reducing storage requirements. We believe large-scale scenes presents a compelling challenge to other compression approaches that rely on compressibility and locality, and OMG’s scalability opens new possibilities for efficient modeling of extremely large scenes.\n| NYC         | #G  | PSNR  | SSIM  | LPIPS | Size   |\n|-------------|-----|--------|--------|--------|--------|\n| Zip-NeRF    | -   | 28.42  | 0.850  | 0.281  | 607 MB |\n| 3DGS-MCMC   | 3 M | 27.77  | 0.857  | 0.295  | 675 MB |\n| MCMC+OMG    | 3 M | 27.82  | 0.853  | 0.291  | 29 MB  |\n\n**[Q3] Clarification of L. 232-233**\n\nLines 232-233 do not suggest that \"the existence of multiple Gaussians with near-identical contributions is inherently redundant.\" Rather, the intended point is that redundant Gaussians may exist, but cannot be effectively pruned if their contribution-based importance scores all exceed the pruning threshold. This issue often arises because Gaussians in close proximity tend to exhibit similar blending contributions. To address this, we introduce LD scoring, which differentiates their importance by assessing the distinctiveness of their appearance—increasing the importance of Gaussians with distinct appearance and decreasing it for those that are less distinctive. This enables the pruning of less distinctive Gaussians, whose contributions can be compensated by the spatial extension of nearby Gaussians during training. The effectiveness of this approach is demonstrated in Table 4 and Figure 5.\n\n\n**[Q2] SVQ configuration**\n\nOMG requires four types of vectors: a 3-dimensional scale vector, a 4-dimensional rotation vector, and two 3-dimensional appearance vectors. As demonstrated in Table 5, although these vectors are already short, applying naive vector quantization still demands significant computation due to the need for large codebooks. Through empirical analysis, we found that using 2-dimensional sub-vectors offers a favorable trade-off, avoiding excessive computational overhead while achieving high performance. Accordingly, we apply 2-dimensional S-VQ to the rotation vector and the unified appearance feature, while the 3-dimensional scale vector, due to its odd dimensionality, is quantized using scalar quantization.\n\n**[Q1] Existing methods leveraging neural fields**\n\nAlthough we have introduced existing methods that leverage neural fields in the related work section (Lines 139–146), we will provide a more detailed technical discussion in Section 3.0.\n\n[1] Kheradmand, S., et al. 3d gaussian splatting as markov chain monte carlo. Advances in Neural Information Processing Systems, 2024.\n\n[2] Barron, J. T., et al.  Zip-nerf: Anti-aliased grid-based neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023."}]

Author rebuttal:

Meta-review: An efficient 3DGS method is proposed to reduce the number of Gaussian primitives. Removing redundancy and exploiting attribute representations for capturing continuity and irregularity of the primitives. Sub-vector quantization, which better represents irregularity, especially helps with fast training using a smaller codebook.

One of the interesting ideas in this work is the local distinctiveness metric, which considers the difference between the appearance features using the set of pseudo-K-nearest neighbors, where the neighbor selection is done by sorting Gaussians in Morton order.

The successful rebuttal phase gets positive feedback from all reviewers, which consolidates the unanimous positive opinions from the diverse borderline opinions in the pre-rebuttal phase, leaving no major issues. AC acknowledges that this seldom happens. Thank you to the reviewers for the constructive feedback and the efforts from the authors. To sum up, AC is glad to recommend this hardworking paper for our venue.

Besides, AC wants to leave some minor suggestions for further polishing the manuscript, although these are not considered in the recommendation assessment:
- According to the official NeurIPS style guideline, “all headings should be lower case (except for first word and proper nouns)” in Line 55. Major ML/vision conferences adopt this style, except for some other venues. 
- Inconsistent styles for the paragraph titles should be corrected, e.g., “Implementation Details” in Section 4 Experiment.
- Aligning with Reviewer MiKC’s concern, the motivation (especially the first two paragraphs) of Section 3.1 needs to be polished to convey the idea. Some sentences are hard to follow.
- Tables 1, 2, 3, and 5 need a space between the caption and the table. 
- Finally, in Table 5, it’s challenging to determine that each group of three columns represents the model sizes (XS/M/XL) due to the absence of explicit group headers.
- The paper title seems to be too general. AC suggests slightly updating your title, because the proposed method achieves the "optimized minimal 3DGS" using sub-vector quantization and local distinctiveness, if the authors find it suitable.
Decision: Accept (poster)