Title: Optimized Minimal 3D Gaussian Splatting
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Gaussian Splatting

Efficient Neural Rendering
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

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 subvector 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/.

1 Introduction

3D Gaussian Splatting (3DGS) [28] has gained popularity for fast and photorealistic 3D scene reconstruction and rendering, offering a 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 [61, 58], photorealistic avatar generation [48, 41], generative models [56, 11], 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 3 million per 360 scenes [2]), leading to excessive storage requirements and computational overhead. To address this issue, various approaches have been proposed, including pruning based on rendering loss [32, 36] or importance score [46, 14] and optimized densification strategies [39]. Notably, several methods [14, 15, 64] 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 1). To address this, many works have explored compressing Gaussian attributes by leveraging vector quantization [44, 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 susceptible 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 compression framework that operates with a minimal number of primitives. To address the irregularity of sparse Gaussians and maximize the compressibility, we employ per-Gaussian features in a novel way. Although the reduced number of Gaussians leads to a decrease in local continuity, we can still leverage the spatial correlation associated with each Gaussian's position. Therefore, we introduce a lightweight neural field model with negligible parameters to capture the coarse spatial feature. This feature is integrated with the per-Gaussian features to represent each attribute, as shown in Figure 2. This approach requires fewer per-Gaussian 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 a sub-vector quantization (SVQ, Figure 3(c)), which splits the input vector into multiple sub-vectors and applies vector quantization to 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 3(b)), while maintaining high-precision representation.

Finally, to retain only the minimal number of Gaussians, we introduce a novel importance metric that evaluates each Gaussian's local distinctiveness relative to its neighbors, identifying the most informative Gaussians. This metric is used alongside existing importance scoring methods based on blending weights from training views [46, 14], further reducing the number of Gaussians while preserving scene fidelity.

Extensive experimental results demonstrate that OMG achieves a 49% reduction in storage compared to the previous state-of-the-art 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 Gaussians, enabling 600+ FPS rendering. These results underscore the effectiveness of OMG in both compression efficiency and computational performance, demonstrating it as a highly promising approach for 3D Gaussian Splatting representation.

2 Related work

2.1 Neural Radiance Fields

Neural Radiance Fields (NeRF) [40] 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 MLP leads to inefficiencies, particularly in terms of slow training and inference times. To overcome these limitations, the following methods [16, 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 [6, 5, 17, 22, 18], hash grids [43, 7], grid quantization [55, 52], and pruning-based strategies [50]. Nevertheless, achieving realtime rendering for complex, large-scale scenes remains a 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) [28] has emerged as a paradigm-shifting technique for real-time neural rendering by representing a 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 [21, 24], simultaneous localization and mapping (SLAM) [27], dynamic scene representation [38], multi-resolution rendering [62], and further improvements in rendering quality [14]. However, 3DGS requires a substantial number of Gaussians to 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. Consequently, 3DGS demands substantial memory and storage resources, often exceeding 1GB per scene in high-resolution environments.

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, 59], where pruning decisions are directly learned based on rendering loss. To optimize the binary masks, Compact3DGS [44] initially adopted STE [4], while subsequent works [65, 36] employed Gumbel-Softmax.

Another direction focuses on importance-based metrics to identify and remove redundant Gaussians. These methods primarily leverage each Gaussian's blending weight contribution to rendering trainingview images as a measure of importance [13, 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 metric that incorporates color distinction among neighboring Gaussians, enabling more effective selection of essential primitives.

Attribute compression. Earlier methods employed conventional compression techniques such as scalar and vector quantization (VQ) [13, 44, 45, 42, 19, 47, 60] and entropy coding [45, 42, 32, 8, 9] 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, a 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, 59, 35] and factorization techniques [54], integrated with grid representations. Scaffold-GS [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 as 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 a novel approach that effectively captures both the continuity and irregularities across Gaussians, enabling a 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 ∈ R N × 3 , opacity o ∈ [0 , 1] N , 3D scale s ∈ R N × 3 + , 3D rotation represented as a quaternion r ∈ R N × 4 , and view-dependent color modeled using spherical harmonics (SH) coefficients h (0) ∈ R N × 3 (0 degree for static color), h (1 , 2 , 3) ∈ R N × 45 (1 to 3 degrees for viewdependent color). The covariance matrix of each Gaussian Σ n ∈ R 3 × 3 is determined by scale s n and rotation r n 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 c n (determined by spherical harmonics under the given view direction) and the final opacity in 2D space α n ( · ) ,

where x denotes a pixel coordinate and Σ ′ n , p ′ n are the projected Gaussian covariance and center position. N ( x ) 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 are further apart, and smooth transitions between them become insufficient to maintain fidelity. Especially for geometry, each Gaussian covers a larger spatial region, requiring a more specific scale and rotation to accurately capture structural details. Therefore, we retain the per-Gaussian parameterization for scale s ∈ R N × 3 + and rotation r ∈ R N × 4 as in 3DGS.

For appearance, local continuity can still be maintained even with increased sparsity. However, unlike NeRF, where the query input is a direct spatial point, mapping Gaussian center points to

Position

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𝐹

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corresponding appearances is inherently challenging. This requires a larger neural field model to maintain high fidelity. On the other hand, entirely disregarding local continuity leads to an inefficient representation, limiting the ability to capture meaningful spatial relationships. OMG addresses these challenges by integrating per-Gaussian 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 ∈ R N × D and a view-dependent feature V ∈ R N × D , where D is the dimensionality of each feature. As illustrated in Figure 2, each feature is concatenated with the space feature F n , derived from each Gaussian's center position, to 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:

where cat ( · , · ) denotes the concatenation function, γ ( · ) represents the positional encoding function, and MLP t ( · ) , MLP o ( · ) , MLP v ( · ) , MLP s ( · ) 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, capitalizing 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 3(a)). To address these issues, Residual Vector Quantization (R-VQ) [63] 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 3(b), multiple code indices per attribute result in increased storage overhead, illustrating a 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 3(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 z ∈ R ML , where M and L represent the total number of sub-vectors (partitions) and the sub-vector length, respectively. Each partition m ∈ { 1 , ..., M } has an independent codebook C ( m ) ∈ R B × L , where B denotes the number of codewords per codebook. The codeword selection is based on the nearest match from C ( m ) , with C ( m ) [ j ] representing the j -th codeword corresponding to the m -th sub-vector. More formally, SVQ-applied vector ˆ z can be formulated as follows,

…

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where q ( z ; M ) denotes applying SVQ with M sub-vectors and i m ∈ { 1 , . . . , B } 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 s n , r n , resulting in quantized vectors ˆ s n , ˆ r n , which are then used for 3DGS rendering. For the appearance features T n , V n , we first concatenate them and apply SVQ. The resulting quantized features are then split back into two components ˆ T n , ˆ V n , which replace T n and V n 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, we adopt a 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

OMGadopts importance scoring to identify essential Gaussians and retain a 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 c k in Equation (1)) across training-view renderings. We use two factors as our baseline metric: (1) whether it has been the most dominant contributor for at least one ray [14, 15] and (2) its total blending weight contribution across all training rays [46, 19]. Formally, we define the base importance score ¯ I as:

where w i,ρ represents the blending weight of Gaussian i for ray ρ 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 closelypositioned Gaussians. In cases where multiple Gaussians are located in close proximity, their blending weights tend to be highly similar, thus naively thresholding them can lead to two potential issues: (1) abrupt performance degradation when all similar Gaussians are simultaneously removed, and (2) redundancy when multiple Gaussians with near-identical contributions are retained.

To mitigate these issues, we propose incorporating a local distinctiveness metric into the importance computation. Specifically, we introduce an additional term that measures the similarity of the static appearance feature T ∈ R N × D between neighboring Gaussians, ensuring that locally distinct Gaussians show high importance. The final importance score is defined as:

where N i K denotes the set of K -nearest neighbors of Gaussian i , and λ is a scaling factor that adjusts the sensitivity to appearance variation. As computing exact K -nearest neighbors for every Gaussian is

32.45 / 3.3 MB

31.14 / 5.0 MB

31.94 / 417 MB

Bicycle

Kitchen

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3DGS

LocoGS (COLMAP)

OMG-XS

20.89 / 4.6 MB

20.95 / 12.3 MB

21.01 / 1.4 GB

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 [33] with a threshold τ .

4 Experiment

4.1 Implementation details

Following the previous works, we evaluated our approach on three real-world datasets, Mip-NeRF 360 [2], Tanks&Temples [31], and Deep Blending [23]. Our model is implemented upon MiniSplatting [14], one of the methods achieving high performance with a small number of Gaussians. We have conducted simple post-processings after training: 1) Applying 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 a single file with LZMA [1] 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 τ 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 in the supplementary materials.

4.2 Performance evaluation

Compression performance. Tables 1 and 2 compare the performance of 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, on the Mip-NeRF 360 dataset, OMG-XS achieves nearly a 50% reduction in storage compared to the small variant of LocoGS [53], the 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 100× compression compared to 3DGS, OMG maintains comparable visual quality. Especially, in the bicycle scene, OMG-XS achieves over 300× compression relative to 3DGS while accurately reconstructing details that 3DGS fails to represent, resulting in a blurry area (highlighted in red) in 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 3), OMG-XS retains high visual fidelity, demonstrating its effectiveness in extreme compression scenarios.

Computational efficiency. OMGachieves remarkable efficiency alongside high performance. As shown in Table 3, OMG shows superior scene fidelity with significantly fewer Gaussian primitives 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 1 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 a large neural field. By efficiently exploiting coarse spatial information through a tiny MLP, OMG achieves high computational efficiency.

4.3 Ablation study

Local distinctiveness (LD) scoring. OMG improves Gaussian pruning by incorporating LD scoring into the importance estimation. Without attribute compression, LD scoring enables high

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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 3), OMG with LD scoring still outperforms, leading to a significant performance improvement with a similar number of Gaussians. This effect becomes even more pronounced when the target Gaussian number is lower, demonstrating that LD scoring provides an effective approach for further reducing a sparse set of Gaussians.

OMG architecture. OMG leverages a 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 are removed, the model experiences the most substantial performance drop. This indicates that the two contributions are orthogonal, independently contributing to model efficiency and performance.

Sub-vector quantization. Table 4 shows the effectiveness of SVQ in terms of performancestorage efficiency. In addition, we replaced it with VQ and RVQ while keeping the proposed clever training strategy: K-means clustering is performed once prior to the final 1K iterations, after which only the codebook is updated. As shown in Table 5, VQ incurs a 13-18 × increase in codebook initialization time compared to SVQ, resulting in significantly higher training overhead due to the need for large codebooks ( 2 14 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-Gaussian attributes efficiently.

Generalization ability. The 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). To validate this generalizability, we applied OMG to 3DGS-MCMC [30], a method well-known for effective densification. As reported in Table 6, OMG preserves the original performance and reduce storage requirements, demonstrating its broader applicability.

5 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 inherently 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 a 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 3DGS-MCMC for large-scale scenes and incorporate the OMG representation, achieving superior performance while drastically reducing storage requirements (Table 7). 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), a 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 stateof-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 in real-time rendering on resource-constrained devices.

References

Lzma compression algorithm. https://www.7-zip.org/sdk.html .

Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5470-5479, 2022.

Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Zip-nerf: Anti-aliased grid-based neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 19697-19705, 2023.

Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 , 2013.

Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J. Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16123-16133, 2022.

Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In European Conference on Computer Vision , 2022.

Yihang Chen, Qianyi Wu, Mehrtash Harandi, and Jianfei Cai. How far can we compress instant-ngp-based nerf? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 20321-20330, 2024.

Yihang Chen, Qianyi Wu, Weiyao Lin, Mehrtash Harandi, and Jianfei Cai. Hac: Hash-grid assisted context for 3d gaussian splatting compression. In European Conference on Computer Vision , pages 422-438, 2024.

Yihang Chen, Qianyi Wu, Mengyao Li, Weiyao Lin, Mehrtash Harandi, and Jianfei Cai. Fast feedforward 3d gaussian splatting compression. In International Conference on Learning Representations , 2025.

Yihang Chen, Qianyi Wu, Weiyao Lin, Mehrtash Harandi, and Jianfei Cai. Hac++: Towards 100x compression of 3d gaussian splatting. arXiv preprint arXiv:2501.12255 , 2025.

Zilong Chen, Feng Wang, Yikai Wang, and Huaping Liu. Text-to-3d using gaussian splatting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 21401-21412, 2024.

Daniel Duckworth, Peter Hedman, Christian Reiser, Peter Zhizhin, Jean-François Thibert, Mario Luˇ ci´ c, Richard Szeliski, and Jonathan T Barron. Smerf: Streamable memory efficient radiance fields for real-time large-scene exploration. ACM Transactions on Graphics (TOG) , 43(4), 2024.

Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, and Zhangyang Wang. Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in Neural Information Processing Systems , volume 37, pages 140138-140158, 2024.

Guangchi Fang and Bing Wang. Mini-splatting: Representing scenes with a constrained number of gaussians. In European Conference on Computer Vision , pages 165-181, 2024.

Guangchi Fang and Bing Wang. Mini-splatting2: Building 360 scenes within minutes via aggressive gaussian densification. arXiv preprint arXiv:2411.12788 , 2024.

Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5501-5510, 2022.

Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 12479-12488, 2023.

Quankai Gao, Qiangeng Xu, Hao Su, Ulrich Neumann, and Zexiang Xu. Strivec: Sparse tri-vector radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 17569-17579, 2023.

Sharath Girish, Kamal Gupta, and Abhinav Shrivastava. Eagles: Efficient accelerated 3d gaussians with lightweight encodings. In European Conference on Computer Vision , pages 54-71, 2024.

Robert Gray. Vector quantization. IEEE Assp Magazine , 1(2):4-29, 1984.

Antoine Guédon and Vincent Lepetit. Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5354-5363, 2024.

Kang Han and Wei Xiang. Multiscale tensor decomposition and rendering equation encoding for view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4232-4241, 2023.

Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. Deep blending for free-viewpoint image-based rendering. ACM Transactions on Graphics (ToG) , 37(6):1-15, 2018.

Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In ACM SIGGRAPH , 2024.

David A Huffman. A method for the construction of minimum-redundancy codes. Proceedings of the IRE , 40(9):1098-1101, 1952.

Herve Jegou, Matthijs Douze, and Cordelia Schmid. Product quantization for nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence , 33(1):117-128, 2010.

Nikhil Keetha, Jay Karhade, Krishna Murthy Jatavallabhula, Gengshan Yang, Sebastian Scherer, Deva Ramanan, and Jonathon Luiten. Splatam: Splat track & map 3d gaussians for dense rgb-d slam. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 2135721366, 2024.

Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics (ToG) , 42(4):1-14, 2023.

Bernhard Kerbl, Andreas Meuleman, Georgios Kopanas, Michael Wimmer, Alexandre Lanvin, and George Drettakis. A hierarchical 3d gaussian representation for real-time rendering of very large datasets. ACM Transactions on Graphics (TOG) , 43(4), 2024.

Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Yang-Che Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. 3d gaussian splatting as markov chain monte carlo. Advances in Neural Information Processing Systems , 37:80965-80986, 2024.

Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG) , 36(4):1-13, 2017.

Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, and Eunbyung Park. Compact 3d gaussian representation for radiance field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 21719-21728, 2024.

Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, and Liefeng Bo. Compressing volumetric radiance fields to 1 mb. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4222-4231, 2023.

Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems , pages 15651-15663, 2020.

Xiangrui Liu, Xinju Wu, Pingping Zhang, Shiqi Wang, Zhu Li, and Sam Kwong. Compgs: Efficient 3d scene representation via compressed gaussian splatting. In Proceedings of the 32nd ACM International Conference on Multimedia , pages 2936-2944, 2024.

Yifei Liu, Zhihang Zhong, Yifan Zhan, Sheng Xu, and Xiao Sun. Maskgaussian: Adaptive 3d gaussian representation from probabilistic masks. arXiv preprint arXiv:2412.20522 , 2024.

Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 20654-20664, 2024.

Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. In International Conference on 3D Vision (3DV) , pages 800-809, 2024.

Saswat Subhajyoti Mallick, Rahul Goel, Bernhard Kerbl, Markus Steinberger, Francisco Vicente Carrasco, and Fernando De La Torre. Taming 3dgs: High-quality radiance fields with limited resources. In SIGGRAPH Asia , 2024.

Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision , page 405-421, 2020.

Arthur Moreau, Jifei Song, Helisa Dhamo, Richard Shaw, Yiren Zhou, and Eduardo Pérez-Pellitero. Human gaussian splatting: Real-time rendering of animatable avatars. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 788-798, 2024.

Wieland Morgenstern, Florian Barthel, Anna Hilsmann, and Peter Eisert. Compact 3d scene representation via self-organizing gaussian grids. In European Conference on Computer Vision , pages 18-34, 2024.

Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. , 41(4), 2022.

KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Abbasi Koohpayegani, and Hamed Pirsiavash. Compgs: Smaller and faster gaussian splatting with vector quantization. In European Conference on Computer Vision , pages 330-349, 2024.

Simon Niedermayr, Josef Stumpfegger, and Rüdiger Westermann. Compressed 3d gaussian splatting for accelerated novel view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 10349-10358, 2024.

Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, and Federico Tombari. Radsplat: Radiance fieldinformed gaussian splatting for robust real-time rendering with 900+ fps. arXiv preprint arXiv:2403.13806 , 2024.

Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, and George Drettakis. Reducing the memory footprint of 3d gaussian splatting. Proceedings of the ACM on Computer Graphics and Interactive Techniques , 7(1), 2024.

Zhiyin Qian, Shaofei Wang, Marko Mihajlovic, Andreas Geiger, and Siyu Tang. 3dgs-avatar: Animatable avatars via deformable 3d gaussian splatting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 5020-5030, 2024.

Kerui Ren, Lihan Jiang, Tao Lu, Mulin Yu, Linning Xu, Zhangkai Ni, and Bo Dai. Octree-gs: Towards consistent real-time rendering with lod-structured 3d gaussians. arXiv preprint arXiv:2403.17898 , 2024.

Daniel Rho, Byeonghyeon Lee, Seungtae Nam, Joo Chan Lee, Jong Hwan Ko, and Eunbyung Park. Masked wavelet representation for compact neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 20680-20690, 2023.

Sebastian Schwarz, Marius Preda, Vittorio Baroncini, Madhukar Budagavi, Pablo Cesar, Philip A Chou, Robert A Cohen, Maja Krivoku´ ca, Sébastien Lasserre, Zhu Li, et al. Emerging mpeg standards for point cloud compression. IEEE Journal on Emerging and Selected Topics in Circuits and Systems , 9(1):133-148, 2018.

Seungjoo Shin and Jaesik Park. Binary radiance fields. In Advances in Neural Information Processing Systems , volume 36, pages 55919-55931, 2023.

Seungjoo Shin, Jaesik Park, and Sunghyun Cho. Locality-aware gaussian compression for fast and high-quality rendering. In International Conference on Learning Representations , 2025.

Xiangyu Sun, Joo Chan Lee, Daniel Rho, Jong Hwan Ko, Usman Ali, and Eunbyung Park. F-3dgs: Factorized coordinates and representations for 3d gaussian splatting. In Proceedings of the 32nd ACM International Conference on Multimedia , pages 7957-7965, 2024.

Towaki Takikawa, Alex Evans, Jonathan Tremblay, Thomas Müller, Morgan McGuire, Alec Jacobson, and Sanja Fidler. Variable bitrate neural fields. In ACM SIGGRAPH 2022 Conference Proceedings , 2022.

Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. In International Conference on Learning Representations , 2024.

Henan Wang, Hanxin Zhu, Tianyu He, Runsen Feng, Jiajun Deng, Jiang Bian, and Zhibo Chen. End-to-end rate-distortion optimized 3d gaussian representation. In European Conference on Computer Vision , pages 76-92. Springer, 2024.

Qianqian Wang, Vickie Ye, Hang Gao, Jake Austin, Zhengqi Li, and Angjoo Kanazawa. Shape of motion: 4d reconstruction from a single video. arXiv preprint arXiv:2407.13764 , 2024.

Yufei Wang, Zhihao Li, Lanqing Guo, Wenhan Yang, Alex Kot, and Bihan Wen. Contextgs : Compact 3d gaussian splatting with anchor level context model. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, editors, Advances in Neural Information Processing Systems , volume 37, pages 51532-51551, 2024.

Shuzhao Xie, Weixiang Zhang, Chen Tang, Yunpeng Bai, Rongwei Lu, Shijia Ge, and Zhi Wang. Mesongs: Post-training compression of 3d gaussians via efficient attribute transformation. In European Conference on Computer Vision , pages 434-452, 2024.

Zeyu Yang, Hongye Yang, Zijie Pan, and Li Zhang. Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting. In The Twelfth International Conference on Learning Representations , 2024.

Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splatting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 19447-19456, 2024.

Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi. Soundstream: An end-to-end neural audio codec. IEEE/ACM Transactions on Audio, Speech, and Language Processing , 30:495-507, 2021.

Yangming Zhang, Wenqi Jia, Wei Niu, and Miao Yin. Gaussianspa: An" optimizing-sparsifying" simplification framework for compact and high-quality 3d gaussian splatting. arXiv preprint arXiv:2411.06019 , 2024.

Zhaoliang Zhang, Tianchen Song, Yongjae Lee, Li Yang, Cheng Peng, Rama Chellappa, and Deliang Fan. Lp-3dgs: Learning to prune 3d gaussian splatting. Advances in Neural Information Processing Systems , 37:122434-122457, 2025.

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A Difficulty in compressing small set of Gaussians

To assess the challenge of compressing a small number of Gaussians, we integrate Mini-Splatting [14] with existing methods [32, 53]. We trained LocoGS [53] with Mini-Splatting-based densification in an end-to-end manner. For Compact-3DGS [32], however, we observe that training it directly with Mini-Splatting-based pruning fails to converge, due to inaccurate attribute representations during training. Rather, we halt Mini-Splatting training early at 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 a 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.

B KNN approximation using Morton order

Table 9 presents a comparison between Morton-order-based approximation and KNN for LD scoring. The performance is nearly identical, demonstrating that Morton order effectively approximates KNN while achieving a 146 × speedup, even with the smallest model. While 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.

C Rendering speed on weaker devices

We have measured the FPS of our method on an NVIDIA RTX 4090 and a lower-end GPU, NVIDIA GTX 1080 Ti. As shown in Table 10, OMG achieves substantially higher FPS on both devices compared to LocoGS, primarily due to its drastically reduced number of Gaussians. Especially, OMG-XS show 1.5× 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 on lower-end GPUs.

D Implementation details

D.1 Mip-NeRF 360 dataset

All experiments were conducted using an NVIDIA RTX 4090. Our method was implemented within the Mini-Splatting [14] framework and trained for 30K iterations. At the 20K iteration simplification process, local distinctiveness scoring was incorporated where the factor λ was set to 2. The dimension of appearance features D was set to 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.

From 29K iterations (last 1K iterations), SVQ (Sub-Vector Quantization) was applied to per-Gaussian features. As mentioned in the paper, to 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 1K iterations. For SVQ, different bit allocations were assigned.

Scale: length 1, 2 6 codes for each sub-vector

Rotation: length 2, 2 9 codes for each sub-vector

Appearance features: length 2, 2 10 codes for each sub-vector

The length 1 SVQ applied to scale can be interpreted as scalar quantization, dynamically learning the quantization range with the codebooks. All codes in the codebook are stored with 16-FP precision.

This SVQ configuration was commonly applied across all variants from XS to XL. The model storage for each variant was determined only by the importance score threshold τ , 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 3DGS-MCMC [30] framework, and tuned it for the Zip-NeRF dataset, by training 3M Gaussians for 150K iterations with a densification interval of 1K iterations. Other hyperparameters are set to default values. For OMG training, the appearance features were introduced at 100K iterations, and from 140K iterations (last 10K iterations), SVQ was applied to per-Gaussian features. For SVQ, bit allocations were assigned as follows:

Scale: length 1, 2 7 codes for each sub-vector

Rotation: length 2, 2 10 codes for each sub-vector

Appearance features: length 2, 2 11 codes for each sub-vector

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 [1].

Both methods are applied losslessly, and we report the resulting storage changes in Table 11. 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

Weconducted experiments to analyze the storage requirements of OMG for representing each attribute, as 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 as well as 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 14 (T&T [31] and DB [23]).

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nOverall, 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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)\n\nQUESTIONS:\nOverall, 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.\n\nLIMITATIONS:\nThe 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).", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-14"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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?\n\nQUESTIONS:\nAddressed in Strengths and Weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-27"}