MEGS^{2}: Memory-Efficient Gaussian Splatting via Spherical Gaussians and Unified Pruning
Authors: Jiarui Chen
Yikeng Chen
Yingshuang Zou
Ye Huang
Peng Wang
Yuan Liu
Yujing Sun
Wenping Wang
Venue: ICLR 2026 Poster (2026)
Keywords: 3D vision
3D construction
novel view synthesis
3D Gaussian Splatting
lightweight 3DGS
3DGS compression
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: MEGS² is a memory-efficient 3D Gaussian Splatting framework that reduces rendering VRAM via spherical Gaussians and unified pruning.

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a dominant novel-view synthesis technique, but its high memory consumption severely limits its applicability on edge devices. A growing number of 3DGS compression methods have been proposed to make 3DGS more efficient, yet most only focus on storage compression and fail to address the critical bottleneck of rendering memory. To address this problem, we introduce MEGS², a novel memory-efficient framework that tackles this challenge by jointly optimizing two key factors: the total primitive number and the parameters per primitive, achieving unprecedented memory compression. Specifically, we fully replace the memory-intensive Spherical Harmonics with lightweight, arbitrarily oriented and prunable Spherical Gaussian lobes as our color representations. More importantly, we propose a unified soft pruning framework that models primitive-number and lobe-number pruning as a single constrained optimization problem. Experiments show that MEGS² achieves a 50% static VRAM reduction and a 40% rendering VRAM reduction compared to existing methods, while maintaining comparable rendering quality.

Published as a conference paper at ICLR 2026
MEGS²: MEMORY-EPHICIEN GAUSSIAN SPLATTING VIA SPHERICAL GAUSSIANS AND UNIFIED PRUNING
Jiarui Chen
Yikeng Chen 12+
Yingshuang Zou'
Ye Huang'
Peng Wang*
Yuan Liu
Yujing Sun
Wenping Wang 4 Adobe
HKUST
SZU
SYSU
'NTU
TAMU
NVIDIA GeForce RTX 3060 Laptop GPU
MediaTek Dimensity 9400+
Qualcomm Snapdragon 865
34.9fps
0
Ours: 117.4 3DGS(W
91.0 3DGS/w SHi;: FPS
Ours: 3DGS(w SHj; N/A
Figure I: We present MEGS?, a memory-efficient framework designed to solve the rendering mem- ory bottleneck of 3D Gaussian Splatting and enable high-quality, real-time rendering on edge de- vices. As demonstrated in our WebGL-based viewer, 3D Gaussian Splatting(3DGS with Spherical Harmonics (SH) exhibits low frame rates desktop GPU and fails run on some mobile plat- forms. In contrast, MEGS² achieves interactive frame rates across all tested devices, significantly expanding the applicability of 3DGS. The detailed result is in Appendix A.3 and A.4
ABSTRACT
3D Gaussian Splatting (3DGS) has emerged as a dominant novel-view synthe- sis technique, but its high memory consumption severely limits its applicability on edge devices. A growing number of 3DGS compression methods have been proposed to make 3DGS more efficient, yet most only focus on storage compres- sion and fail to address the critical bottleneck of rendering memory. To address this problem, we introduce MEGS? a novel memory-efficien framework that tackles this challenge by jointly optimizing two key factors: the total primitive number and the parameters per primitive, achieving unprecedented memory com- pression. Specifically, fully replace the memory-intee -intensvv Spherical Harmon- ics with lightweight, arbitrarily oriented and prunable Spherical Gaussian lobes our color representations. More importantly, propose unified soft prun- ing framework that models primitive-number and lobe-number pruning as a sin- gle constrained optimization problem. Experiments show that MEGS² achieves 50% static VRAM reduction and a 40% rendering VRAM reduction compared to existing methods, while maintaining comparable rendering quality.
INTRODUCTION
Although 3D Gaussian Splatting (3DGS) (Kerbl al.) 2023) has been rapidly replacing implicit neural radiance fields (NeRF) (Mildenhall et al.] 2020) as the dominant paradigm in neural rendering. thanks its fast reconstruction, real-time performance, and high-quality outputs, supporting its application across devices with varying constraints has become increasingly important. As a result,
Equual contribution. Corresponding authors

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compression of 3DGS (Bagdasarian et al. 2025) is gaining significantly more attention, with the goal of enabling real-world use cases on edge devices, such as mobile 3D scanning and previewing, virtual try-on, and real-time rendering in video games.
Nevertheless, existing compression methods only focus on storage compression rather than memory compression. While the former storage compression speeds up the one-time data transfer of 3DGS files, the rendering memory dictates whether the 3DGS rendering process can run smoothly on edge devices. This limits the applicability of 3DGS applications to low-end devices like cell phones. For example, some methods based on neural compression, vector quantization, and hash grid com- pression (Chen et al. 2024; Girish et al. 2024 Lee et al., 2024) achieve high storage compression rates. However, these methods require decoding the full Gaussian parameters from compressed state before rendering, even resulting in a larger rendering memory than the 3DGS methods without compression. Another branch of works, i.e., primitive pruning methods (Zhang et al. 2025b Fang Wang 2024), effective at reducing both storage and rendering memory due to the reduced prim- itives. However, these pruning methods still need to retain substantial number of 3D Gaussians for a reasomable rendering quality, which still consumes large rendering memory. How to further reduce the rendering memory of (GGS-baeed// rendering methods remains open question.
In this paper, we observe that the overall memory consumption for 3DGS rendering is intrinsically tied to two key factors: the total primitive count and the parameters per primitive. Thus, propose a novel framework that simultaneously reduces both factors, rather than only optimizing for the primitive numbers in previous methods ZZhang al. (2025b); Fang & Wang (2024). Specifically, the rendering VRAM can be divided into a static component and a dynamic component. The static part relates directly to the total number of Gaussian primitives loaded into the renderer, which is product of the primitive count and the parameters per primitive. On the other hand, the dynamic part, which consists of intermediate data like projected 2D Gaussian parameters and the tile-depth- gaussian key-value table, is also related to the primitive count in the specific camera viewpoint. This inherent relationship underscores that to reduce the overall memory footprint, we need reduce both the number of primitives and the per-primitive parameter size.
To address the memory bottleneck, we must reduce both primitive counts and per-primitive param- eters. We first analyze the limitations of using Spherical Harmonics (SH) for color representation. While effective for low-frequency lighting, SH functions are inherently global and require many high-order coefficients to represent localized, high-frequenc' details like sharp highlights. This sults in low parameter utilization and makes them difficult to compress due to the varying parameter counts across different degrees. While SG-Splatting (Wang al., 2024a) pioneered using Spheri- cal Gaussians (SG) to mitigate these issues, they relied on a hybrid model combining SG with SH. Advancing in this direction, introducefully standalone, arbitrarily a more memory memory-efficient alternative. Such representation excel at modeling view-dependent signals with very few parameters, and their complexity can be flexibly controlled by the number of lobes. This inherent locality and sparsity make it a convenient and effective choice for compression.
and prunable SG as
Based on the SG-based representation, we further introduce a novel unified soft pruning frame- work. We leverage the favorable properties of Spherical Gaussians to dynamically prune redun- dant lobes for each primitive, thereby compressing the per-primitive parameter count. To achieve globally optimal memory footprint, our framework models the two traditionally separate pruning pruning and per-primitive lobe pruning-as a single constrained opti- mization problem, with the total parameter budget serving as unified constraint. Extensive ex- periments demonstrate that the proposed b achieves an excellent balance between rendering quality and VRAM efficiency.
Overall, our contributions can be summarized as follows:
Advancing beyond the hybrid approach in SG-Splatting (Wang et al.) 2024a), introduce fully standalone color representation by replacing Spherical Harmonics entirely with arbitrarily- oriented and prunable Spherical Gaussians. This significantly reduces the per-primitive parameter count and thus lowers rendering VRAM with minimal impact on quality.
We propose a unified soft pruning framework that models both primitive-count and lobe-count as a memory-constraije existing staged or hard-
optimization problem, which yields superior performance compared to hard- pruning methods.
We achieve unprecedented memory compression for 3DGS, surpassing both vanilla and state- the lightweight methods. Our method delivers over an 8x static VRAM compression and
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nearly 6x rendering VRAM compression compared to vanilla 3DGS, while maintaining improving rendering quality. Furthermore, it still achieves 2x static VRAM compression and a 40% rendering VRAM reduction over the SOTA method, GaussianSpa, with comparable quality,
RELATED WORK
2.1 EVOLUTION OF SPLATTING-BASED SCENE REPRESENTATIONS
While Neural Radiance Fields (NeRF) (Milennhal et al. 2020) and their variants (Barron et al. 2021 Müller et al., 2022 Barron et al. 2023) achieved excellent quality in novel view synthesis, their slow rendering speeds precluded real-time applications. 3D Gaussian Splatting (3DGS) (Kerbi) et al. 2023) overcame this limitation by introducing differentiable rasterizer for 3D Gaussians, enabling unprecedented real-time performance with high visual fidelity. The success of 3DGS, how- ever. highlighted its primary challenge: a substantial memory and storage footprint. This has directly motivated the body of work on 3DGS compression and pruning that we discuss subsequently.
2.2 MEMORY ANALYSIS IN 3DGS RENDERING
In most 3DGS compression studies (Bagdasarian et al. 2025), the rendering memory footprint has been thoroughly analyzed or compared. Memory usage can be conceptualized into two main components: a static portion, consisting of the total parameters of all loaded Gaussian primitives, and a dynamic portion, representing the runtime overhead. The dynamic overhead, which is highly dependent on a renderer's s implementation, includes the storage of preprocessed 2D Gaussian at- tributes-an an overhead that scales significantly with the number of rendering channels, as seen in multi-channel applications like Feature Splatting (Qiu et al.) (024). Additionally, it includes data structures required by tile-based renderers, an overhead first discussed and optimized by FlashGS (Feng et al. 2024) for large-scale scenes.
2.3 PRUNING TECHNIQUES FOR 3DGS
Pruning unimportant primitives is a natural and widely explored approach for 3DGS compression. Some work has focused on refining adaptive density control strategies to achieve more efficient representations (Kheradmand et al., 2025 Cheng et al. [024 Liu et al., 2024a Pateux et al. 2025]
Mallick et al. 2024 Kim et al. 2024).
has concentrated defining better importance metrics to decide which primitives to remove, with notable examples including LP-3DGS (Zhang al. 2024), mini-splatting (Fang & Wang. 2024), and Reduced3DGS (Papantonakis et al. 2024).
the latter of which also involves spherical harmonic pruning. A recent noteworthy development is GaussianSpa (Zhang al., 2025b), which models pruning as a constrained optimization problem, offering a novel perspective and good compatibility with other pruning techniques. While effective reducing the number of primitives, these methods generally achieve limited compression ratio and are therefore often used as an initial step within larger, integrated compression pipeline.
2.4 OTHER COMPRESSION SCHEMES FOR 3DGS
Besides pruning, researchers have applied common compression techniques like vector quantization. scalar quantization, neural, and hash grid compression to 3DGS (Girish et al., 2024 Lee et al, 2024
Fan et al. 2024) Lee al. 2025 Liu al. 2024b] Navaneet al. 2024 Xie al. 2024 Shin al.
2025 While methods with entropy encoding (Chen et al.) 2024 2025 Liu et al. 2025a Sa Wang al. 2024bb achieve the highest storage compression ratios, they are often limited to structured (anchor-based) Gaussian representations (Lu et al.) 2023] Ren et al. 2024 ZZhag et al. 2025a). These techniques may drastically reduce file size, but they fail to deliver comparable reduction in rendering memory. This is because they cannot render directly from compressed state; instead, they must first decode the full Gaussian parameters. The resulting memory footprint is, therefore, never smaller than that required for uncompressed Gaussian primitives, and the decoding process itself, particularly when involving neural networks, can introduce significant additional memory overhead.
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(A) Color Representation: Spherical Gaussian
(B) Memory- Optimization
Goussian Attributes
Vanilla 3DGS
Spherical Gaussian
Overall Objective
pusition
Surfaco
Lobes
min Clo, e, 6),
5 lotal SUCRLAT
lols + pele
r
rotation
color
Origin var.
scale
qpacity
40
BGB
R2 S
dhappos 5,
Proxy
Aur As
6, S
(C) Post-processing Procedure
3D03
Nax
Opucity Vecter
Slurpnzs Vodter
Updatic
truning
Proximal Operation Flow
Qpucity
Lobe Fruning
Alax
Disorbution
Sharpness Dettriutton
Color Compemation deg
Figure 2: Overview our proposed MEGS². (A) In Section 3.2 we first replace the Spherical
Hanmonics with arbitrarily-orienn and prunable Spherical Gaussians. (B) In Section B.3.1 formulate the compression as a memory-constrained optimization problem, which is solved using an LDMM-isspiied approach (Section 3.3.2.. (C) In Section 3.3.3] near-invalid primitives and low-
sharpness lobes are removed, and a color compensation term (Eq 12) is introduced to recover the energy of the removed lobes.
3 METHOD
We present MEGS?, (emory--fficcinn Gausian Splatting via Spherical Gaussian and Unified Prun- ing, novel Gaussian compression framework designed from VRAM-centrie perspective. Our method strikes a balance between rendering quality and memory footprint by simultaneously opti- mizing both the number of Gaussian primitives and the average parameters per primitive.
3.1 PRELIMINARIES FOR 3D GAUSSIAN SPLATTING
3D Gaussian Splatting (3DGS) (erbb al. 2023
represents
using set of 3D Gaussian
primitives. Each primitive defined by a center E R a covariance matrix Σ E R3333 a scalar opacity o. and view-dependent color model. The color is determined by function c(v) S² R3 which maps a viewing direction v to an RGB color. In the original work, this function is modeled using Spherical Harmonics (SH).
To render an image, these 3D primitives are projected onto the 2D image plane. The final pixel color computed by alpha-blending the primitives that overlap with the pixel in front-to-back order, according to the equation: C = Σ; Go; (1--)) Here, the rendering opacity as a product of the primitive's scalar opacity o and its 2D Gaussian value at the pixel location.
3.2 SPHERICAL GAUSSIANS
Although the original Gaussian Splatting (3DGS) utilized Spherical Harmonics (SH) for color modeling, discussed in Section[]] [I SH functions suffer from limitations in parameter efficiency and the ability to model local and high-frequency signals.
In contrast, Spherical Gaussians (SG) offer more compact representation, where the parameter count can be controlled by adjusting the number of lobes. As we demonstrated in Figure a three- lobe SG requires only about half the parameters of 3rd-order SH while achieving comparable expressive power and superior figh-freuuncc detail capture because, in most cases, we only need to model highlights. Furthermore, shown in Table 2 SG is more amenable pruning than SH.
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SG was first introduced in real-time rendering to support all-frequency shadows from both point lights and environment lights (Wang et al., 2009). Specifically, lobe of SG has the form
Glv; s, a) ((xx-)))
(1)
where jr S is the unit-length lobe axis, 8 E IR+ controls the sharpness, and a e R is the RGB amplitude vector, with v E S being the viewing direction.
We use the sum of all lobes of SG for color modeling and introduce a diffuse term to model the direction-independeer component. The view-dependent color c(v) is thus computed as:
c(v) = + Σ G(v; 8i,
where c(v) E IR3 is the final color from the viewing direction v E S co E R$ is the direction- independent diffuse color, and the sum is over n all lobes of SG, with jr, E S being the lobe axis, 8, E R+ the sharpness, and a, E R$ the RGB amplitude for the i-th spherical lobe.
Choice of bebitrrarily-orientte SG lobes It is partieularly notable that the lobe axes of different SG are not constrained to be orthogonal, nor is any regularization term introduced to enforce or- thogonality. Instead, each SG lobe allowed to have an arbitrary direction. This flexibility grants the SG model a higher degree of freedom, leading to greater representation capability compared to models with fixed orthogonal axes. As shown in Figure (in Appendix), SG-Splatting (Wang al.) 2024a) with fixed orthogonal axes demonstrated significant rendering performance degradation, fur- ther underscoring the importance of supporting arbitrarily oriented SG lobes.
3.3 UNIFIED SOFT-PRUNING FRAMEWOR
Benefiting from the property of Spherical Gaussians to control the number of parameters with the number of lobes, they provide an ideal object for pruning. Given that most Gaussian primitives in scene require only few lobes effectively model their color, and that the number of Gaus- sian primitives is itself often redundant, we propose a novel unified soft pruning framework. In the framework, we first redefine the pruning of both Gaussian primitives and spherical lobes unified optimization problem with a total memory-overhead constraint (Section 3.3.1). To handle the non-differentiable nature of this constraint, we introduce an ADMM- DonM--is (Boyd et al., 201T) algorithm to efficiently solve the problem (Section 3.3.2). After optimization, the model enters post-processing procedure where the final primitives and spherical lobes are removed, with render- ing quality subsequently recovered through color compensation strategy and minor fine-tuning (Section 3.3.3
3.3.1 PROBLEM FORMULATION
We unify primitive count pruning and spherical lobe pruning into a memory-constrained optimiza- tion problem. The Lo norm of the opacity vector represents the number of active primitives, as primitives with zero opacity do not contribute to rendering. Similarly, the Lo norm of the sharp- ness vector denotes the number of active spherical lobes, since lobes with zero sharpness exhibit no view-dependent effect and their color can be directly added the diffuse term co. Building upon GaussianSpa (Zhang et al.] (025b)'s 's sparsification framework and our analysis, extend the pruning objective from primitive count optimization to total memory budget control, formalized as:
min L(o, s, +), s.t. Pollollo + +p S 0,5,9
(3)
total memory construint
where E N means opacity vector for N Gaussian primitives, E N means flattened sharpness vector for N Gaussians, ④ E I N3 (other Gaussian variables), Po = 11 and P. = 7 count base parameters for a single Gaussian primitive and a single SG lobe respectively, and k the total parameter budget. L(o. s, θ) is the reconstruction loss function.
3.3.2 MEMORY-CONSTRAINED OPTIMIZATION
In this subsection, we aim to solve the constrycd optimization defined in Section 3.3.1 Due to the introduction of non-differentiable component (the Ton norm) into the constraint,
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Algorithm 1 Memory-consttrined optimizing
Require:
Initial parameters: 09, "000 s
Proxy variables: 6 =0" 8 sd
Dual variables: X X
Learning rate 1). penalty do, ds. budget rt
Ensure:
Optimized parameters: 0x ook s
1: for k: = 0 to K 1 do
Gradient Step:
@ -- 0* VaL( ot sty
3:
4: o ++ o 7 VoL(O of sty
60 6º +b)]
5: gh+11 + s* VeL(O+ 41 : (s 5² + +b))
Proximal Step:
7: (ō*+1, .g+++) t prox, (o++1 2o. s X5))
8:
Dual Update:
9:
A X + (o++1 ottl)
10: X + x + (s - (t11)
11:
end for
12: Return OK ok s
directly optimize it with regularized stochastic gradient descent. Instead, we adopt an ADMM- inspired approach by introducing proxy variables ö and š for and respectively. This decompo- sition strategy enables us to split the original optimization problem into two tractable subproblems:
min L(o, 8, +) + 2 (pollo - ö + Aa.12 + Pal|s 5 >² 0,8,0
(4)
min h (pollo ó + 2011 + p=l|s |s - 3 Ao 1?3)
(5)
where h(.,-) -) is an indicator function enforcing the sparsity constraint:
0 if pollolio + P=lls|lu S
h(o,s) +00 otherwise
(6)
and the proxy variables are updated via the proximal operator:
(ō. = prox, (o + >o X2).
Here, Xo and Ag denote the corresponding Lagrange multipliers for the constraints. The specific algorithm flow can be found in Algorithm 1
and the detailed derivation is in Appendix A.1.1
Proximal Operator Implementation The proximal operator projects o and onto the constraint Pollo + Pslls < compatible with any importance metric. To simplify the design and leverage importance criteria from prior primitive-pruning research, we choose to factorize the projection:
ő prox, (o + Xo), | < Ko
prox, (s + A₂), |ا1| K₂
(9)
For opacity projection, Zhang et al. 2025b) has established multiple proximal operators that are selected based on scene characteristics. Our approach reuses these operators (See Appendix A.2).
For sharpness projection, project the attribute onto the constraint space by retaining only the N2 most important spherical lobes. This retention based on a dynamic range metric, which quantifics the view-dependent color change contributed by the i-th lobe:
D; max - min(c:) |a -1--e-e- -2s))
(10)
v
where is the RGB amplitude for the i-th spherical lobe, and K2 controls the total number of active lobes. The Kg elements corresponding to the highest D; values in (s + As) are preserved.
Discussion:
Sequential vs. Unified Pruning While sequential pruning tackles memory reduction in a two-stage process-firt reducing primitive count, then per-piimitive parameters our unified framework jointly optimizes both factors single problem. This approach finds better orf between primitive count and per- permiiimieive complexity, thus avoiding the sub-optimal solutions often found by sequential methods. For detailed experimental validation, please refer to Section 4.4.2

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3.3.3 POST-PROCESSING PROCEDURE
After completing the constrained optimization process, obtain large number of primitives and spherical lobes with near-zero opacity and sharpness values, respectively. As the hard constraints are enforced on the proxy variables rather than the original ones, these near-zero values must still be pruned to achieve an actual memory and storage benefit. We address this with a three-step pose-proess strategy: first, we remove Gaussian primitives whose opacity falls below a certain threshold. Then, for spherical lobes, we remove those with negligible sharpness and introduce simple color compensation method to minimize the average per-view color variation and mitigate performance degradation caused by their removal.
This compensation is achieved by finding an optimal term, Aco, that minimizes the integral of the squared color difference over all view directions on the unit sphere:
min / ((co + Aco) - (Co + G(v; 81, a;))) dv Aoo
(11)
Solving equation yields an exact compensation term that preserves the color the removed lobe:
e 20,
Aco = a; 28;
(12)
The diffuse term of the parent primitive is then updated with this value:
c - + Aco
(13)
Finally, after removing some primitives and spherical lobes, we continue to fine-tune for a small number of steps to recover the rendering quality. The detailed derivation is in Appendix(A.I] Fig- ure presents the distribution of the spherical lobes after post-procesd post-processing procedure across scenes.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets and Metrics Following 3DGS (Kerbl al. 2023). we evaluate the rendering perfor- mance on real woold datasets, including all indoor scenes and outdoor scenes in Mip-NeRF360 (Barron al. 2022), two scenes from Tanks Temples (Knapitsch et al., 2017h, and two scenes from Deep Blending (Hedman et al. 2018). Additionally, we measure the VRAM overhead during the Gaussian loading (static) and rendering stages on these scenes.
Baselines On all datasets, we benchmark our method against four categories of baselines: (1)
vanilla 3DGS (Kerbl et al.] (2023) and SG-Splatting (Wang et al. 2024a): (2) lightweight 3DGS methods based on primitive pruning: LP-3DGS (Zhang et al. 2024). ) Mini-Splatting (Fang & Wang
2024), MaskGaussian (Liu et al. 2025b) and GaussianSpa Zhang et all 2025b (3) lightweight
3DGS methods based on pruning spherical harmonic coefficients: Reduced3DGS (Papantonakis et al. 2024); (4) 3DGS compression methods utilizing orthogonal techniques such as neural com-
pression and vector quantization: CompactGaussian (Lee al. 2024). EAGLES (Girish et al.
2024 LightGaussian (Fan et al. 2024). LocoGS (Shin et al. 2025) and MesonGS (Xie 2024)
Experimental Setup To ensure fair comparison, follow the core training procedures estab- lished by 3DGS (Kerbl et al. 2023) and GaussianSpa (Zhang et al., 2023b). We evaluate rendering quality using PSNR, SSIM, and LPIPS, and measure efficiency via statie and rendering VRAM con- sumption. For a detailed description of our specific implementation, as well as the definitions and measurement procedures for both static and rendering VRAM, please refer to Appendix A.2
4.2
MAIN RESULTS AND ANALYSIS
The quantitative results are summarized in Table I comparing approach with several existing lightweight 3DGS methods. Overall, our method achieves comparable or slightly superior render- ing quality to the current state-of-the-ar lightweight 3DGS methods, while demonstrating signifi- cantly lower VRAM overhead across all existing methods. Compared to vanilla 3DGS, our method achieves 0.4 dB PSNR improvement the DeepBlending dataset and 0.012 I.PIS reduction
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Table I: Quantitative comparison across three datasets. Best results are in red region second
best are in orange region. Memory (VRAM) values are in MB. HQ denotes the version prioritizing high rendering quality, and LM denotes the version prioritizing lower VRAM consumption.
Mip-NeRF 360
Tanks& Tamks&Temples
DeepBlending
Method
VRAM SSIM LPIPSI Stat. Rend.
VRAMJ Stat. Rend
VRAM Stat. Rend.
PSNR1
PSNR SSIM LPIPS!
PSNR*
SSIM; LPIPS)
3DGS
27.48
0.813
0.217
648
1717
23.68
0.849
0.171
370
1021
29.71
0.902
0.242
582
1569
SG-Splatting
27.27
0.813
0.218
416
1327
23.47
0.840
0.177
229
822
29.57
0.901
0.247
357
983
Reduced3DGS
27.21
0.810
0.225
191 493
23.51
0.839
0.187
90
252
29.60
0.902
0.248
121 340
ConpactGaussian
27.08
0.798
0.247
267
838
23.32
0.831
0.201
177
469
29.79
0,901
0.258
187
532
EAGLES
27.23
0.810
0.240
23.37
0.840
0,200
333
965
29.86
0.910
0.250
510
1550
LightGaussian
27.13
0.806
0.237
290
640
23.44
0.832
0,202
168
437
LocoGS
27.28
0.809
0.231
609
994
23.62
0.846
0,182
458
732
30.05
0,905
0.247
643
1040
MesonGi--FT
26.98
0.801
0,233
1336
23.32
0.837
0.193
783
29.51
0.901
0.251
656
1202
LP-3DGS
27.12
0.805
0.239
420
1239
23.41
0.834
0,198
251
769
Mini-Splatting
27.40
0.821
0.219
125
477
23:45
0.841
0.186
72
253
30.05
0.909
0.254
89
324
MaskGaussia
27.43
0.811
0.227
271
799
23.72
0.847
0,181
132
517
29.69
0.907
0.244
156
501
Spu
27.56
0.874
0.215
448
23.73
0.857
0.162
336
30.00
0.912
104
372
Ours(HQ)
27.54
0.824
01.119
265
23.45
0.853
0.159
51
211
30.17
0.912
0.233
54
243
Ours(LM)
27.21
0.814
0.227
224
23.27
0.851
0.167
37
163
30.01
0.908
0.246
33
193
the Tanks & Temples dataset. Furthermore, it achieves more than an 8x compression rate for static VRAM and nearly 6x compression rate for rendering VRAM across all datasets. Even when com- pared to the current state-of-the-ar lightweight 3DGS method, GaussianSpa (Zhang et al. (0255), still achieve nearly 2x compression rate for static VRAM on the Mip-NeRF360 dataset and reduce rendering VRAM by approximately 40% with comparable rendering quality.
Analysis on Existing Compression Methods Many recent methods achieve high storage com- pression but fail to reduce VRAM. Techniques based on hash grid compression, vector quantization or neural networks (e.g., CompactGaussian (Lee al. 2024). EAGLES (Girish et al.) (024)) must first decompress their parameters into renderable state. This requirement, coupled with the decod- ing process itself, results VRAM footprint that can exceed even vanilla 3DGS (Appendix, Tab. 3.. Similarly, HAC++ (Chen et al., 2025). SOTA method designed specifically for anchor-based 3DGS, is not a general solution and offers minimal VRAM reduction for the same reason. In con- trast, our method achieves superior perceptual quality (SSIM/LPIPS) with 50-60% less VRAM and a 1.5-1.7x rendering speedup.
Analysis on Pruning-base based Methods Pruning-basee methods (hhag al. 2025b Fang & Wang 2024 IZhang et al.] (024) effectively reduce VRAM but face a bottleneck, as aggressively reducing primitive count degrades quality. Our method overcomes this by not only pruning prim- itives but also reducing per- per-primitive costs. When compared to methods that also compress color, such as Reduced3DGS (Papantonakis al.) (2024) which prunes SH coefficients, our advantage is twofold: the superior amenability of our Spherical Gaussians to pruning, and our more effective unified soft pruning framework, validated in Section 4.4
4.3 QUALITATIVE RESULTS
Figure presents a qualitative comparison of MEGS² against baselines, including GaussianSpa and 3DGS on various scenes. In Bicycle and Truck, our method accurately recovers specular reflections on smooth surfaces and mirrors, details that other methods fail fully capture. In Bonsai, MEGS2 faithfully captures high-contrast lighting in both contours and brightness. Furthermore, MEGS² de- livers a cleaner and more complete reconstruction in Playroom. Notably, MEGS² achieves these high-quality results with VRAM footprint of only 50-60% compared to GaussianSpa, demonstrat- ing a substantial enhancement in memory efficiency. These visual results indicate that our Spherical Gaussian based representation has stronger capability for fitting local view- depedeennddnt signals than Spherical Harmonics, leading to sharper and more photorcalistic images. More qualitative results in Appendix, Figure
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Published as a conference paper at ICLR 2026
Ground Truth
MIRY
Ours
250MB 301MB
19RMB
194MB
Gaussian
3DGS
3042MB
1272WB
1279MG
Figure 3: Qualitative results on the Bicycle, Bonsai, Kitchen, Playroom and Truck scenes comparing to previous methods and the corresponding ground truth images from test views. The rendering VRAM consumption for the corresponding method annotated at the bottom of each image.
4.4 ABLATION STUDIES
In this section, we conduct a series of ablation studies to validate the effectiveness of our proposed designs and answer two key questions: 1. the replacement of spherical harmonics with spherical Gaussians necessary? And why not use the orthogonal-axis spherical variants (fixed-axis SG)? (Section 4.4.1) 2. Is our proposed unified soft pruning framework effective? And does it offer significant advantage over simply combining existing pruning methods? (Section 4.4.2)
4.4.1 ABLATION ON COLOR MODELING
To validate our color model, we replace the spherical harmonics (SH) in 3DGS with our arbitrarily- oriented spherical Gaussians (SG) while keeping training settings identical. Our SG model achieves a superior quality-VRAM trade-off (see Figure 4 Furthermore, we show that the fixed-axis SG from SG-Splatting (Wang et al.) 2024a) struggles to capture complex view-dependent effects, lead- ing to sharp quality drop (about 0.6 dB PSNR). This rigidity also makes it incompatible with our lobe-pruning strategy.
4.4.2 ABLATION ON PRUNING STRATEGIES
We evaluate unified soft pruning framework against several ablations and baselines, with re- sults in Table 2 First, replacing soft prun- ing with a hard-pruning variant (soft - hard) that only optimizes opacity results in a signif- icant performance drop. Second, performing primitive and lobe pruning sequentially (unified + sequential) is inferior to our joint optimiza- tion, confirming the advantage proach. We also found that removing our color compensation harms performance (w/o color comp.). Finally, we test two naive baselines. Combining GaussianSpa with Reduced3DGS leads to severe quality degradation, showing that existing SH pruning metrics fail on fewer primitives. Simply replacing SH with SG in GaussianSpa is also outperformed by method. This demon- strates that our pruning strategy not only reduces memory but also acts as a regularizer, improving rendering quality. Visual comparison are in Appendix, Figure 9
Table 2: Ablation studies for different pruning
strategies on Mip-NeRF360 dataset.
Method
PSNR SSIMI
LPIPS
VRAM,
Spa+Redu.
26.05
0.776
0.280
402
Spa(SH + SG)
27.01
0.808
0.230
339
soft - hard unified + seq.
27.23
0.814
0.228
288
27.33
0.818
0.222
328
unified ap-
w/o color comp.
27.46
0.822
0.213
265
full model
27.54
0.824
0.209
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Published as a conference paper at ICLR 2026
1180MB KG: 260M
BG: 2.70M LPPPS: 0.219
LPIPS: 8.218
27.6
LPIPS:
27.4
PSNR (dB)
RG: 2.72M
VRAM: 1289NBB
LPIPS: 0.217
27.2
AG: 2.72M
LPPES:: 0.219
27.0
VRAM: 964MB 2.71M
LPIPS: 0.223
VRAM:
Arbitrarily-oriented (Ours, lobe= 1,2331,2.3):
Spherical Harmonics (SH, 1,2,))
Fixed-axis SG (a.k.a. Ortho-axis SG. lobe=3):
26.6
10
20
30
40
50
Number of Parameters for Color Modeling
Figure Comparison of different 3DGS color representations Mip-NeRF360 dataset.
Lobe Typa
O-Lobes
1.183
1.183
1-Lobes
0.7
2-Lobes
1.328
1.225
3-Lobes
23.1%
1.267
Numbbrr of Causlans
27.8%
0.5
19.7%
17.8%
1.224
22.7%
1.072
19.7%
27.3%
16.0%
16.3%
22.5%
27.8%
21.7%
0.3
20.6%
23.7%
25.3%
27.7%
40.8%
98.4%
25.1%
48.7%
43.6%
31.8%
0.
Bicycla
Stump
Gunden
Flowers
Ti Trechill
Bunooi
Counter
Kitchen
Room
Scemes
Figure Distribution Gaussian lobes across scenes.
We report the distribution of Gaussians using 0 to 3 lobes for each scene in the Mip-NeRF 360 dataset. The value above each bar represents the average lobe count per Gaussian.
CONCLUSION
We introduced MEGS?, framework that addresses the 3D Gaussian Splatting memory bottle- neck by jointly optimizing primitive count and per-primitive parameters. Our approach combines a lightweight spherical Gaussian color representation with unified soft pruning method achieve state-of-the-art memory compression. MEGS² shifts the focus of 3DGS compression towards ren- dering VRAM efficiency, paving the way for high-quality rendering on edge devices.
Limitation Our method focuses on compressing static memory for broad, renderer-agnostic ap- plicability. We leave the optimization of implementation-speeiee dynamic VRAM for future work, and note that the model's performance on highly complex highlights warrants further investigation.
ACKNOWLEDGMENTS
This work is supported by the donation from Kerry Group Limited in "30-for-30" Talent Acquisition Campaign of HKUST.
ETHICS STATEMENT
Our work presents no direct ethical concerns. The primary application of our method for novel view synthesis from captured data.
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REPRODUCIBILITY STATEMENT
To ensure reproducibility: (1) While not included with this submission, our full project (including the WebGL renderer, training, and evaluation scripts) will be released GitHub upon publication. (2) All experimental details, including our VRAM measurement protocol, are provided in Appendix A.2 (3) Our data and preprocessing follow the official 3DGS implementation. (4) Derivations and proofs for our core algorithms are in Appendix A.1.I and A.1.2
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A APPENDIX
A.1 ALGORITHMIC DERIVATION
A.1.1 DERIVATION FOR MEMORY-CONSTRAINED OPTIMIZING
This appendix provides the detailed derivation of Algorithm | algorithm solves the memory-constrained optimization problem with Lo norm constraints using an ADMM-inspired approach.
presented in the main text. The
Problem Formulation Consider the constrained optimization problem:
min o,s,8
8(o,8, +)
(A.1)
s.t. Pollollo + Polls|lo S
where L is the loss function, sents the memory budget. Augmented Lagrangian Formulation
||
denotes the Lg norm (count of non-zero elements), and k. repre-
Introduce proxy variables ö, reformulate the problem:
min 0,5,0,6,5
L(o,s, 9)
(A.2)
S.t. 0= O, 8=8
Po 0olōoloo + + 1 S
The augmented Lagrangian function is constructed
0,0,,, =[ s, 9)
Xo (o 6) + +11 (s 5)
(A.3)
5
+ 2 (pollo- o x||² Ps is 3 ()
where Ao and A are Lagrange multipliers, and 8 0 the penalty parameter.
ADMM Alternating Minimization three subproblems:
The ADMM framework decomposes the optimization into
Primal Variables Update
With proxy variables and multipliers fixed, update the primal variables:
(0 A+1 s arg min L(o, +)
0,0,5
(A.4)
8
Pollo 6* + x112 + palls s* +
2
Using gradient descent approximation:
θ ++ = th v /VeL(e".0'.
o ++1 t 7 VoL(ok ot s + 00 - + +xa s t+1 k n v. *+1 s*) + 1s 5 + x
(A.5)
where 8, = -po, 8, Sp..
Proxy Variables Update
With primal variables and multipliers fixed, update the proxy variables:
(6++1 ((t1)) = arg min ((6, + 2 6,8
[pollo - ô +g12 + Pt1 - 8 XMIl (A.6)
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where h(ō,8) 3) is the indicator function enforcing the sparsity constraint:
0
if Pol + p=1 S
(
(A.7)
+oo
otherwise
The solution given by the proximal operator:
k (++,,sht1 gh+t)) - prox, ( + 2o s + x
(oth
(A.8)
Dual Variables Update
Update the Lagrange multipliers:
A X + (o*+1-6tt) 6
(A.9)
A X + (s =
A.1.2 DERIVATION FOR COLOR COMPENSATION
/ ((co + Aen) (co + Mc, a:)))² dv
min Ang g
(A.10)
where G(v; ,4, 8, ness, and a E R$ is the RGB amplitude vector, with v E S being the viewing direction.
1e (Mv-)1, 3 is the unit-length lobe axis, s R+ controls the sharp-
Simplifying the objective function:
min (Aco Gl, dv Arg 83
(A.11)
Expanding the squared term:
min (Ac
2AcgG(v: S,, a.) (( dv
(A.12)
Acg
g2
Taking derivative with respect to Aco and setting to zero:
2 ( (Aco G(y)
(A.13)
Solving for the optimal Aco: Sgp G(vi ai)dv Jga dv
Aco
Gly; a:)d
(A.14)
4x Jga
Evaluating the spherical Gaussian integral. Without loss of generality, align μ with the z-axis:
J G(v: 8,, a:) dv = a₁
2n
(cos - 1) sin d0 dō
2Ta
(.000 sin 0 d0
(substitute = cos 0, du sin 0d0)
2Ta;
(<((--1) du
-2s
2Ta;
8;
Substituting back:
1 - e 20r
e
28:
Aco
2Ta
04
(A.15)
4m
23;
The diffuse term is updated as:
d Co Aco
(A.16)
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A.2 EXPERIMENTAL DETAILS
Implementation Details Our experiments are conducted on single NVIDIA RTX 3090 GPU (24GB). For a fair comparison, we adopt the same hyperparameters and optimizer used by vanilla 3DGS (Kerbl et al. 2202)) on all datasets. In terms of the hyperparameters for our unified soft prun- ing framework, we conduct the memory-constrained optimization for a total of 10,000 iterations, ap- plied at an interval of 50 iterations. We set the penalty parameter to 0.0005. For the post-processing procedure defined in Aection[3.3.3 we set the sharpness threshold to 1 to prune spherical lobes with negligible contribution. Regarding the training strategy, we employ the same densification, pruning. and fine-tuning procedures as GaussianSpa, conducting densification training. sparse training, and fine-tuning for the same number of steps. To ensure the accuracy of our VRAM measurements, we reproduce 3DGS, GaussianSpa (Zhang et al. 2025b), EAGLES Girish et al.
2024), CompactGaus- sian (Lee et al. 2024). LightGaussian (Fan et al. 2024), LocoGS (Shin al., 2025). ). MesonGS (Xie)
et al. 2024), and Reduced3DGS (Papantonakis et al. (2044) for direct VRAM measurement. For methods, we utilize both the rendering quality metrics and primitive counts re- ported in the GaussianSpa paper. We then estimate their corresponding VRAM consumption based on our own VRAM measurements for 3DGS and GaussianSpa. EAGLES cannot be trained on a single NVIDIA RTX 3090 GPU with 24GB on some scenes in Mip-NeRF360, we do not report VRAM consumption. For fairness, MesonGS uses the version that continues fine-tume aie-ttunig after com- pression, and LocoGS utilizes point clouds from COLMAP for initialization maintain consistency with other methods.
other pruning-basee
Evaluation Details For rendering performance, report the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and learned perceptual image patch similarity(LPIPS)) For VRAM consumption, we report both static VRAM overhead and rendering VRAM overhead. The former refers to the VRAM required to load all 3DGS primitives into the renderer and dequantize or decode them into a ready-to-rent tonderr state. This is not necessarily equal to the file size, as some methods might represent Gaussian attributes in a more compact or fower-preciste restoring them to full, 32-bit precision Gaussian attributes before rendering. The latter denotes the peak VRAM usage during the rendering process, which is typically larger than the static VRAM due to the introduction of intermediate variables (e.g., projected 2D Gaussian attributes, key-value tables for tile-based rendering) during rendering. We measure this value across all test viewpoints and report the average. To exclude the overhead introduced by the framework itself and the impact of memory fragmentation,
data format but still require
note that the memory allocation and management of the official 3DGS implementation's 's renderer are almost entirely handled by PyTorch. Therefore, all VRAM-related metrics are reported using the values provided by the PyTorch framework.
Proximal Operators Implementation As mentioned in the main text, for opacity projection, we reuse the two proximal operators from GaussianSpa (Zhang et al. 20256). These operators select Ko primitives to preserve based on different importance criteria. The first is a Magnitude- Based Selection, which sorts the input vector (o+ + X and preserves the Ko elements with the highest magnitudes. The second operator employs an Importance-Basse
Selection, adopting the impor- tance score metric from MiniSplatting (Fang & Wang 2024). The importance score I, for the i-th Gaussian is the sum of its blending weights across all intersecting camera rays seen during training:
1;
Wij
(A.17)
where the blending weight wyy is the product of the Gaussian's opacity ag its projected 2D value G?D and the accumulated transmittance Tiş along the j-th ray. Here, K is the total number of inter- sected rays. The operator then preserves the Ko primitives with the highest importance scores. While GaussianSpa selects the specific operator based on scene characteristies (c.g., indoor outdoor), we simplify the overall framework by consistently adopting the Importance-Bart Based Selection as our unified approach across all scenes.
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A.3 MORE QUANTITIVE RESULTS
Table 3: Average parameter costs for the color model per Gaussian primitive. Storage and Rendering costs are measured the number float32 parameters. Decode Overhead indicates whether method introduces significant additional VRAM during caleulating color. The values of our method are calculated on the DeepBlending dataset.
Method
Storage
Rendering
Decode Overhead
3DGS
48
48
No
EAGLES
1
48
Yes
ConpactGaussian
< 1
> 32
Yes
ours w/o lobe-pruning
24
24
No
ours
9.7
9.7
No
Table 4: Comparison of anchor-based 3DGS and specialized SOTA compression schemes the Mip-NeRF360 dataset. The best result shown in bold.
Method
PSNRT
SSIM
LPIPS)
VRAM
FPSt
Scaffold-GS
27.74
0.811
0.226
612
123
HAC+ (highrate)
27.81
0,811
0.231
637
115
HAC++ (lowrate)
27.60
0.803
0.253
514
132
Ours
27.54
0.824
0.209
265
200
Table 5: Mip-NeRF360 Indoor per scene results. Best results are in red region second best are
in
orange region
Memory (VRAM) values are in MB.
Scene
Method
PSNRT
SSIM
LPIPS.
Statie VRAM..
Rendering VRAM.,
3DGS
32.23
0.940
0.205
393
798
Reduced3DGS
31.44
0.933
0.214
66
276
Bonsai
GaussianSpa pa
31.76
0.943
0.198
146
420
Ours(HQ)
31.95
0,943
0.192
44
266
Ours(L.M)
31.51
0.937
0.205
28
227
3DGS
29.09
0.906
0.201
393
Reduced3DGS
28.57
0.899
0,212
67
331
Counter
GaussianSpa
28.98
0.911
0.191
175
518
Ours(HQ)
28.78
0.906
0.195
44
326
Ours(LM)
28.35
0.892
0.220
25
272
3DGS
31,24
0.925
0.126
581
1197
Reduced3DGS
31.03
0.921
0.133
118
428
Kitchen
GaussianSpa
31.50
0.929
0.127
163
443
Ours(HQ)
31.51
0.927
0.127
51
309
Ours(LM)
31.27
0.923
0.133
36
275
3DGS
31.55
0.918
0.220
474
1007
Reduced3DGS
31.03
0,914
0.227
68
310
Room
GaussianSpa
31.61
0.922
0.211
141
401
Ours(HQ)
31.56
0.923
0.207
36
250
Ours(LM)
31.16
0.915
0.226
22
214
17

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Published as a conference paper at ICLR 2026
Table 6: Tanks& Temples per scene results. Best results are in red region second best are in
orange region
Memory (VRAM) values are in MB.
Scene
Method
PSNR
SSIM
LPIPS
Static VRAMI
Rendering VRAM.
3DGS
21.97
0.818
0.198
397
773
Reduced3DGS
21.74
0.804
0.219
74
227
Train
GaussianSpa
21.81
0.826
0.197
164
339
Ours(HQ)
21.52
0.820
0.197
49
208
Ours(LM)
21.32
0.818
0.205
35
160
3DGS
25.39
0.881
0.143
752
1268
Reduced3DGS
25.28
0.875
0.154
105
276
Truck
GaussianSpa
25.65
0.887
0.126
115
448
Ours(HQ)
25.37
0.886
0.121
54
214
Ours(LM)
25.23
0.883
0.130
39
167
Table 7: Mip-NeRF360 Outdoor per results. Best results in red region
second best
in
orange region
Memory (VRAM) values are MB.
Scene
Method
PSNR1
SSIM
LPIPS
Statie VRAM
Rendering VRAM
3DGS
25.20
0.764
0.210
1794
2955
Reduced3DGS
25.10
0.763
0,219
314
686
Bicycle
GaussianSpa
25.43
0.779
0.222
240
491
Ours(HQ)
25.34
0.782
0.206
63
248
Ours(LM)
25.16
0.771
0.226
48
205
3DGS
26.61
0.769
0.217
1585
2487
Reduced3DGS
26.75
0.778
0.218
318
663
Stump
GaussianSpa
27.07
0.802
0.207
222
460
Ours(HQ)
27.19
0.803
0.198
60
233
Ours(LM)
27.04
0.797
0.213
47
198
3DGS
27.36
0.863
0.108
1494
2449
Reduced3DGS
27.14
0.858
0.116
303
684
Garden
GaussianSpa
27.22
0.854
0.140
227
460
Ours(HQ)
27.17
0.858
0.127
78
273
Ours(I.M)
26.62
0.846
0.143
63
240
3DGS
21.49
0.602
0.338
1066
1716
Reduced3DGS
21.37
0.598
0.345
221
497
Flowers
GaussianSpa
21.60
0.624
0.332
209
451
Ours(HQ)
21.65
0.625
0.331
66
255
Ours(LM)
21.37
0.609
0.344
48
203
3DGS
22.58
0.633
0.328
1192
1947
Reduced3DGS
22.41
0.630
0.337
241
563
Treehill
GaussianSpa
22.84
0.655
0.312
226
471
Ours(HQ)
22.74
0.649
0.313
59
233
Ours(LM)
22.44
0.640
0.335
43
188
18

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Published as a conference paper at ICLR 2026
Table 8: DeepBlending per scene results. Best results are in red region
second best are in
orange region
Memory (VRAM) values are in MB.
Scene
Method
PSNR
SSIMT
LPIPS
Static VRAMI
Rendering VRAM.
3DGS
30.00
0.903
0.244
678
1197
Reduced3DGS
29.96
0.904
0.248
90
276
Playroom
SaussianSpa
30.48
0.914
0.239
137
320
Ours(HQ)
30.77
0.914
0.229
52
218
Ours(LM)
30.58
0.912
0.243
30
167
3DGS
29.41
0.902
0.239
1129
1941
Reduced3DGS
29.24
0.901
0.247
152
404
DrJohnson
GaussianSpa
29.50
0.910
0.239
184
424
Ours(HQ) 29.56
0.909
0.237
56
269
Ours(LM) 29.45
0.904
0.250
37
219
Table 9: Real-time rendering performance comparison of different methods across various desktop and mobile devices in Bicycle scene. All tests were conducted using a WebGL-based viewer, which we modified from the repository (Kwok & Ye (023) support both Spherical Gaus- sians and 3rd-order Spherical Harmonics. Performance measured in FPS. "cannot render" indicates that the device's browser crashed showed a black screen, while "render error" sigmifies incorrect rendering, such as color display issues. The best result is shown in bold.
Method
RTX3060
Dimensity 9400+ Snapdragon 8+ Gen 1 Snapdragon 888
3DGS
26.3
6.6
cannot render render error 120.9
connot render render error 60.1
GaussianSpa
165.0
31.4
Ours
165.0
91.0
Table 10: Storage Size Comparison. We compare the storage size (MB) of our method against pruning-based methods (MaskGaussian, GaussianSpa) and the compact Gaussian approach (EA- GLES). Our method not only surpasses pruning baselines but also achieves superior storage effi- ciency compared EAGLES, even without employing explicit compression techniques.
Storage on MipNeRF 360 (MB)
Storage on DeepBlending (MB) J
Method
MaskGaussian GaussianSpa
271
156
115
104
EAGLES
54
52
Ours (HQ)
55
54
Ours (LM)
40
33
Table 11: Quantitative comparison on the Shiny Blender Real dataset. Bold figures indicate the best results.
PSNR 1
SSIM
LPIPS J
Method
Meun
Garden
Sodan
Toycar
Mean
Garden
Sedun
Toycur
Meun
Garden
Sodan
Toycar
Ref-NeRF
23.62
22.01
25.21
23.65
0.646
0.720
0.633
0.264
0.251
0.234
0.231
ENVIDR
23.00
21.47
24.61
22.92
0.606
0.561
0.707
0.549
0.332
0.263
0.387
0.345
3DGS
23.85
21.75
26.03
23.78
0.662
0.571
0.771
0.637
0.230
0.248
0.206
0.237
24.15
22.53
26.23
23.70
0.661
0.778
0.597
0.292
0.254
0.225
0.396
GShader
23.46
21.74
24.89
23.76
0.647
0.576
0.728
(1633
0.254
0.274
0.259
0.239
R3DG
21.98
21.92
21.18
22.83
0.619
0.556
0.643
0.657
0.349
0.354
0.380
0.312
3DGS-DE
26.32
0.230
0.231
Ref-Gaussian
24.61
22.97
26.60
24.27
0.685
0.617
0.777
0.660
0.246
0.256
0.245
0.256
Ours
24.44
23.01
25.93
24.38
0.695
0.631
0.788
0.666
0.230
0.256
0.175
0.259
19

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Published as a conference paper at ICLR 2026
Table 12: Comparison of training time across different datasets and hardware contigurations.
We report the training duration (in minutes and seconds) for MEGS on both RTX 3090 and RTX 4090 GPUs, and compare it with GaussianSpa and 3D Gaussian Splatting (3DGS) on an RTX 4090.
Ours
GaussianSpa
3DGS
Dataset
Scene
RTX 3090
RTX 4090
RTX 4090
RTX 4090
Bicycle
37min51s
23min20s
27min57s
25min28s
Stump
34min09s
22min34s
26min 18s
20min48s
Garden
38min23s
22min56s
29min21s
24min06s
Flowers
39min 2s
26min57s
30min 13s
17min08s
Mip-NeRF360
Treehill
37min34s
24min53s
28min38s
18min43s
Bonsai
51
28min19s
44min 14s
16min36s
Counter
51min22s
32min06s
51min25s
19min20s
Kitchen
50min33s
33min
49min36s
22min59s
Room
41min05s
28min53s
44min41s
20min23s
Train
28min39s
19min08s
28min45s
1mmin06s
Tanks& Temples
Truck
29min59s
20min22s
27min03s
13min21s
Playroom
33min01s
22min33s
31min32s 36min26s
18min21s
DeepBlending
DrJohnson
38min28s
24min57s
24min31s
Table 13: Comparison of the number of Gaussian primitives. Gaussians (in millions) across three benchmark datasets.
We report the total number of
Method
Mip-NeRF360
Tanks Temples
DeepBlending
3DGS
2.718
1,568
2.461
LP-3DGS
1,866
1,116
Mini-splatting
0,559
0.320
0.397
MaskGaussian
1.205
0.590
0.694
GaussianSpa
0.528
0.447
0.409
Ours(HQ)
0.611
0.618
0.598
Ours(LM)
0.462
0.437
0.411
27.00
26.00
PSNR (dB)
25.00
65%
68%
Kg 70%
Figure 6: Sensitivity analysis pruning hyperparameters. We investigate the impact varying the lobe budget K, (x-axis) and the primitive budget K, (represented by different colored lines) on rendering quality (PSNR) and memory consumption on the garden scene. The values annotated in the boxes indicate the specific rendering VRAM usage for each configuration.
20

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Published as a conference paper at ICLR 2026
A.4
MORE QUALITATIVE RESULTS
Ground Truth
Ours
326MB
218MB
Gaussian
518Ma
3201I
3DGS
Figure 7: More qualitative results on Train, Room, Kitchen, Counter and Playroom scenes com- paring to previous methods and the corresponding ground truth images, The rendering VRAM consumption for the corresponding method is annotated at the bottom of each image.
Success Cases
Failure Cases
Specular Highlights
Dense Transparency
poo
Figure 8: Visualizations of success and failure cases under challenging material conditions. The left column demonstrates successful reconstructions of scenes containing specular highlights and transparency. Conversely, the right column illustrates specific failure cases where we observe reconstruction artifacts under conditions of highly specular highlights dense transparency.
21

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Published as a conference paper at ICLR 2026
Spa Redu.
384MB
401MB
499MB
388MB
soft -hard
258MB
276MB
366MB
288MB
unified -se..
302MB
319MB
400MB
326MB
w/ color
comp.
248MB
255MB
326MB
250MB
full model
248MB
255MB
326MB
250MB
Figure 9: Qualitative ablation results the Bicycle, Flowers, Counter and Room scenes. The rendering VRAM consumption for the corresponding method is annotated at the bottom of each image.
90.9 fps
90 9
fbs 9
91 0 fps
91.0 f
Figure 10: Real-time rendering performance of MEGS various scenes, showcased via WebGL- based viewer on OnePlus Ace Ultra with Media Dimensity 9400+. The live frame rate (FPS) for each view is annotated in the imagery.
22

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Published as a conference paper at ICLR 2026
Figure 11: Real-time rendering performance of MEGS on various scenes, showcased via a WebGL- based viewer on lenovo laptop with NVIDIA GeForce RTX3060 Laptop GPU. The live frame rate (FPS) for each view is annotated in the imagery.
57
2p.2.pp
Figure 12: Real-time rendering performance of MEGS on various scenes, showcased via a WebGL- based viewer Huawei MatePad Air with Qualcomm Snapdragon 888. The live frame rate (FPS) for each view is annotated in the imagery.
116.8 8.6.8.fo
83.6 fps
114.0 Tps
69.6 fps
Figure 13: Real-time rendering performance of MEGS² various scenes, showcased via WebGL- based viewer on RedMi K60 with Qualcomm Snapdragon 8+ Gen 1. The live frame rate (FPS) for each view is annotated in the imagery.
23

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Published as a conference paper at ICLR 2026
Ground Truth
3DGS
3DGS with SG
GaussianSpa
GaussianSpa with SG
Ours
Figure 14: Visual comparison in the Playroom scene. "With SG" indicates the variant where Spherical Harmonics (SH) replaced by Spherical Gaussians (SG). As observed in the figure, the speckled texture stems from the Ground Truth, which exhibits actual shadow regions alongside inherent image roughness that complicates training. While most methods smooth this detail entirely, both GaussianSpa and MEGS² (Ours) manage rough reconstruction.
A.5 THE USE OF LARGE LANGUAGE MODELS (LLMS)
The article was refined using Gemini 2.5 Pro / Gemini 2.5 Flash / DeepSeek RI to improve phrasing for natural English expression and correct grammatical errors.
24


Reviews:
[{"review_id": "ScOvqOnPqG", "reviewer": "Reviewer_zwHD", "summary": "This paper proposes memory efficient gaussian splatting method by combining Spherical Gaussian and ADMM-inspired unified pruning. Noticed from related paper SG-Splatting, they found fixed orthogonal axes of SG lobes limit rendering performance, and they make it arbitrary oriented. Inspired from GuassianSpa, they optimize proxy of opacity and sharpness parameters simulatenously to sparsify the values. At the end of optimization of proxies, they prune primitives based on significance of the proxies. They found sequential pruning (prune gaussians first then prune parameters) shows poorer trade-off between primitive count and primitive parameters than unified pruning (prune gaussians and parameters together). To recover rendering quality degradation caused by pruning, they further finetune diffuse color based on color loss. Their experiment results show comparable rendering quality of their method against state-of-the-art method but significant save in VRAM, tested on standard benchmarking datasets.", "questions": "* The first contribution claim in lines 97–99 makes it sound as if the authors are the first to propose substituting Gaussian Splats with Spherical Gaussians, which is not true, as this was already introduced by SG-Splatting. The authors should instead highlight the distinction between **fixed axes** and **arbitrarily oriented axes** in Spherical Gaussians. Emphasizing the reduction of per-primitive parameter count and VRAM savings is also inappropriate, since these advantages originate from SG-Splatting.\n* The second contribution claim in lines 100–102 also seems inaccurate. The memory-constrained problem and its solution were originally introduced by GaussianSpa, and the authors simply extend that method by incorporating the sharpness of Gaussians to retain those with better expressive capacity. The authors should revise the statement of contribution 2 to accurately reflect their original findings.\n* The VRAM reduction of the proposed method is primarily attributed to the smaller number of parameters used for encoding view-specific color and pruning. To provide supporting evidence for the pruning aspect, I suggest that the authors present both VRAM consumption and the number of Gaussians in Table 1. This will help readers better understand the pruning performance compared to pruning-only baselines such as LP-3DGS, Mini-Splatting, and GaussianSpa.\n* The qualitative results in Figure 3 do not fully support the authors’ claim that \"MEGS² delivers a cleaner and more complete reconstruction in Playroom\" (line 415). When examining the ceiling area in the top center of the rendered images, MEGS² produces a somewhat speckled texture, whereas GaussianSpa and 3DGS yield cleaner textures. Additionally, the light switch in the bottom-left area appears blurrier in MEGS² compared to GaussianSpa.\n* I am wondering about the effect of using arbitrarily oriented Spherical Gaussians. Please include an ablation study to analyze this effect. For example, the authors could test:\n  1. The proposed method with **Spherical Harmonics (SH)**\n  2. The proposed method with **fixed-axis Spherical Gaussians**\n* Furthermore, I am curious why **GaussianSpa (SH → SG)** shows a quality drop (27.01dB in Table 2) compared to the original GaussianSpa (27.56dB in Table 1). This raises doubts about the justification for using Spherical Gaussians.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 1, "contribution": 1, "strengths": "This paper addresses the limited color representation capacity of SG-Splatting and further introduces opacity- and sharpness-based pruning, along with energy recovery of removed lobes, to reduce VRAM usage with maintaining high reconstruction quality.", "weaknesses": "I think the major issue is the inaccurate contribution claim of this paper. As discussed in Questions 1 and 5, the first contribution claim seems exaggerated, and their findings are not well supported by ablation experiments. Similarly, the second contribution claim is also inaccurate, as discussed in Question 2. Moreover, poor presentation of the method, such as missing information on model choices, makes it difficult for readers to fully understand their approach. Please refer to my Questions for detailed comments.\n\nPresentation issue:\n* I could not find the authors’ conclusion regarding model selection.\n  1. In lines 306–311, the authors propose two distinct operators: **sharpness-based selection** and **range-based selection**. Which one is ultimately adopted in the final model and why? I could not find a statement clarifying this choice.\n  2. Similarly, in lines 785–787, which criterion was finally chosen and why?: **magnitude-based selection** or **importance-based selection**?\n* I could not identify the setting difference between the last two rows in Table 1. Please provide distinct method names (e.g., ours-setting_A and ours-setting_B) and explain them clearly in the appropriate section of the main text.\n* Please include a citation for ADMM at line 234.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "O9VJnWL4eB", "reviewer": "Reviewer_1h2z", "summary": "The paper presents MEGS^2, a 3D Gaussian Splatting framework that cuts both primitive count and per-primitive parameters to fix the rendering VRAM bottleneck. \nThe paper replaces SH colors with lightweight, arbitrarily oriented spherical Gaussians (SG) so most Gaussians use only a few lobes. \nThe paper’s method formulates primitive and lobe pruning as one memory-constrained optimization, reaching ~8× static and ~6× rendering VRAM compression over vanilla 3DGS, still close to SOTA quality. \n\nThe paper backs its claims with extensive experiments on standard datasets (MipNeRF 360, etc), with qualitative evaluations. \nAblation studies show different pruning scheme is outperformed by the method presented by the paper, unified soft pruning.", "questions": "- The authors argue SGs are “more amenable to pruning.” Can the authors quantify how many lobes are actually used per Gaussian at convergence, per scene?\n- The optimization looks more complex than sequential pruning: how sensitive is it to the pruning budget? Do small changes lead to big quality swings?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper provides following strenghts:\n- Unlike storage-only methods, it reduces static and rendering VRAM (50% static, 40% rendering vs SOTA), so it runs on edge/mobile. \n- Usage of SG color model is prunable. SGs keep high-freq/view-dependent details with about half the params of SH and allow lobe-wise pruning.\n- Unified soft pruning works better than sequential. ADMM-style optimization over opacity and sharpness finds a better quality–memory trade-off than “prune primitives then prune SH.”", "weaknesses": "The paper suffers from following weaknesses:\n- Optimization pipeline is complex. Needs proxy variables, proximal projection, then post-processing + fine-tuning, so it’s harder to integrate than simple importance-pruning. \n- As stated in the limitation section, the paper mostly focuses on renderer-agnostic static memory. Paper explicitly leaves renderer-specific dynamic VRAM optimization “for future work,” so some engines may still hit a ceiling.\n- As authors noted, performance on “highly complex highlights” needs more study, so scenes with extreme view-dependent effects may degrade first.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KXU3lc5gd7", "reviewer": "Reviewer_FDsv", "summary": "The paper presents MEGS2, a memory-efficient framework for 3D Gaussian Splatting (3DGS) that targets the rendering VRAM bottleneck rather than only the storage footprint. The authors propose two key ideas: (1) replacing high-order Spherical Harmonics (SH) color modeling with Spherical Gaussians (SG) for compact, local view-dependent representation, and (2) a unified soft pruning framework that simultaneously prunes redundant Gaussian primitives and per-primitive SG lobes under a total memory constraint, formulated as a constrained optimization problem solved by an ADMM-inspired method. Experiments show the effectiveness of the proposed method. The approach is further validated on both desktop and mobile devices, showing strong frame rates in a WebGL viewer.", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- Replacing SH with arbitrarily oriented Spherical Gaussians is an effective solution to reduce per-primitive parameters. The formulation is physically intuitive and well integrated into the 3DGS framework.\n- The paper provides detailed quantitative and qualitative results across multiple datasets and baselines.\n- The WebGL-based real-time rendering results demonstrate real usability on mobile platforms. \n- The method’s ability to generalize across devices and maintain reasonable quality is a clear step toward deployable Gaussian-based rendering.", "weaknesses": "- Several important parameters are not explicitly described or justified. The choice of these hyperparameters may significantly affect performance and reproducibility.\n- Scenes with highly specular highlights or dense transparency are acknowledged as future work but should be illustrated. The method’s performance on dynamic or large-scale scenes remains unknown.\n- While the authors promise code release, the current text lacks explicit training schedules, optimization hyperparameters, and hardware runtimes, which hinders replication.\n- The convergence and optimality properties of the ADMM-inspired procedure are not formally analyzed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QdMLZsQ60A", "reviewer": "Reviewer_RnYG", "summary": "This paper presents a memory-efficient Gaussian Splatting framework that replaces high-dimensional spherical harmonics with lightweight spherical Gaussian lobes. It also introduces a unified pruning strategy for both Gaussian primitives and spherical Gaussians, resulting in improved pruning performance due to its regularization effect. Experimental results demonstrate that the proposed framework achieves substantial reductions in both static and dynamic VRAM usage while providing superior rendering quality.", "questions": "- Could you compare the proposed pruning method with other pruning metrics from existing approaches, such as CompactGaussian [CVPR'24], EAGLES [ECCV'24], and LightGaussian [NeurIPS'24]?\n\n-  Could a lower degree of spherical harmonics be more efficient than spherical Gaussian lobes? It could be helpful to compare the diverse SH degrees against the proposed representation.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper effectively analyzes the key factors of large VRAM consumption and mitigates them using the proposed techniques.\n\n- The use of spherical Gaussian verifies to be an efficient alternative for color representation, achieving meaningful parameter reduction while maintaining a comparable representation capacity to spherical harmonics.\n\n- Experimental results show that the proposed method outperforms various 3DGS baselines in rendering quality while requiring only minimal VRAM consumption.", "weaknesses": "- The lack of technical novelty needs to be enhanced. The main performance gain for the memory reduction originates from the replacement of spherical harmonics with spherical Gaussian lobes. However, spherical Gaussians have already been applied to 3DGS in prior work, such as SG-Splatting. \n\n- Pruning solely based on the opacity value may lead to performance limitations, since the contribution of Gaussians is also influenced by other attributes, such as scales. \n\n- Despite the low VRAM consumption, this representation requires a relatively high storage demand compared to compact Gaussian approaches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dvCWs3dzKd", "reviewer": "Reviewer_vVWJ", "summary": "This paper proposed a memory-efficient Gaussian Splatting method for edge devices. It uses spherical Gaussians to replace the original spherical harmonics for better efficiency. It also involves a pruning process to eliminate the redundant Gaussian points. Experimental results show the proposed method can reduce VRAM compared to the original 3DGS.", "questions": "No code attached. I think it would be better if the authors released the code. \n\nHave the authors try 4DGS? In my opinion, it is quite easy to adapt the proposed method for 4DGS.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper is well-organized and easy to follow. Language is good.\n2. The proposed method can render comparable results to 3DGS.\n3. The presentation is good. The demonstrated effect is impressive.", "weaknesses": "1. In the abstract, \"Experiments show that MEGS2 achieves a 50% static VRAM reduction and a 40% rendering VRAM reduction ...\", this sentence confuses me. What's static VRAM and rendering VRAM? There's no clear claim.\n\n2. In lines 60-62, the authors claim, \"However, these methods require decoding the full Gaussian parameters from a compressed\nstate before rendering, even resulting in a larger rendering memory than the 3DGS methods without compression.\".  I am wondering if the proposed method doesn't need to decode the compressed Gaussian parameters for rendering? I did not find a clear description.\n\n3. The authors claim the proposed spherical Gaussian is their contribution. However, the spherical Gaussian was already proposed by [1]. The authors do not present a clear discussion of it. I did not find a detailed description for arbitrarily-oriented SG. How to achieve it? Why is it better than orthogonal? Is there any theoretical analysis?\n\n4. The proposed memory-constraint optimization is too straightforward. Please elaborate on the contribution. The formulation did not claim the relation between opacity $o$ and sharpness $s$.\n\n5. To the proposed post-processing procedure, the authors claim they have a three-step strategy. However, I only find two: (1) removal according to low opacity, (2) by sharpness. Moreover, I do not think it is novel. It is also straightforward. There's no specific design in it.\n\n6. As for experiments, the authors do not compare with state-of-the-art GS compression methods, like LocoGS[2], speedy-splat[3], LightGaussian[4], maskGaussian[5].\n\n\n7. There's no visual comparison for the ablation study. I think it would be better to present it. There are no Gaussian numer comparisons. \nIt would be better to show the training costs and storage memory. There are no detailed VRAM analyses for each Gaussian attribute.\n\n\n\n\n\n\n[1] Wang Y, Chen S, Yi R. Sg-splatting: Accelerating 3d gaussian splatting with spherical gaussians[J]. arXiv preprint arXiv:2501.00342, 2024.\n\n[2] Shin S, Park J, Cho S. Locality-aware gaussian compression for fast and high-quality rendering[J]. arXiv preprint arXiv:2501.05757, 2025.\n\n[3] Hanson A, Tu A, Lin G, et al. Speedy-splat: Fast 3d gaussian splatting with sparse pixels and sparse primitives[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 21537-21546.\n\n\n[4] Fan Z, Wang K, Wen K, et al. Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps[J]. Advances in neural information processing systems, 2024, 37: 140138-140158.\n\n[5] Liu Y, Zhong Z, Zhan Y, et al. Maskgaussian: Adaptive 3d gaussian representation from probabilistic masks[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 681-690.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)