Mobile-GS: Real-time Gaussian Splatting for Mobile Devices
Authors: Xiaobiao Du
Yida Wang
Kun Zhan
Xin Yu
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Vision
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-quality rendering across a wide range of applications.
    However, its high computational demands and large storage costs pose significant challenges for deployment on mobile devices. 
    In this work, we propose a mobile-tailored real-time Gaussian Splatting method, dubbed Mobile-GS, enabling efficient inference of Gaussian Splatting on edge devices.
    Specifically, we first identify alpha blending as the primary computational bottleneck, since it relies on the time-consuming Gaussian depth sorting process. 
    To solve this issue, we propose a depth-aware order-independent rendering scheme that eliminates the need for sorting, thereby substantially accelerating rendering.
    Although this order-independent rendering improves rendering speed, it may introduce transparency artifacts in regions with overlapping geometry due to the scarcity of rendering order. 
    To address this problem, we propose a neural view-dependent enhancement strategy, enabling more accurate modeling of view-dependent effects conditioned on viewing direction, 3D Gaussian geometry, and appearance attributes. 
    In this way, Mobile-GS can achieve both high-quality and real-time rendering.
        Furthermore, to facilitate deployment on memory-constrained mobile platforms, we propose first-degree spherical harmonics distillation, a neural vector quantization technique, and a contribution-based pruning strategy to reduce the number of Gaussian primitives and compress the 3D Gaussian representation with the assistance of neural networks. 
    Extensive experiments demonstrate that our proposed Mobile-GS achieves real-time rendering and compact model size while preserving high visual quality, making it well-suited for mobile applications.

Published as a conference paper at ICLR 2026
MOBILE-GS: REAL-TIME GAUSSIAN SPLATTING FOR MOBILE DEVICES
Xiaobiao Du 1,3
Yida Wang"
Kun Zhan 3 Xin Yu²*
University of Technology Sydney Adelaide University
3 Li Auto Inc.
Ground Truth
3DC8
29
Speady-Spiat
28.0
3DGS
Storage MB
27.5
100 MB
27
AdR. Gaussian
GB
26.5
Beai-time Randening
Mobile
SortFreedS
Mobile-Co GS (Ours)
FPS
Figure 1: Mobile-GS is the first real-time Gaussian Splatting method that can reach 116 FPS render- ing speed in the 1600 x 1063 resolution for Bicycle on the mobile equipped with the Snapdragon Gen 3 GPU shown in (a). We evaluate rendering quality, storage costs, and inference speed on RTX 3090 Ti GPU in (b) and (c). Our Mobile-GS integrates depth-aware ieler-independent render- ing, compression, and distillation techniques deliver comparable rendering quality compared with the original 3DGS, while substantially reducing the storage requirements to 4.8 MB and achieving 1098 FPS on the unbounded scene, thereby enabling efficient deployment on mobile devices.
ABSTRACT
3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high- quality rendering across wide range of applications. However, its high compu- tational demands and large storage costs pose significant challenges for deploy- ment mobile devices. In this work, we propose mobile-tailored real-time Gaussian Splatting method, dubbed Mobile-GS, enabling efficient inference Gaussian Splatting edge devices. Specifically, as the primary computational bottleneck, since it relies on the time-consuming Gaussian depth sorting process. To solve this issue, we propose a depth-aware order-independent rendering scheme that eliminates the need for sorting, thereby substantially accelerating rendering. Although this order-independent render- ing improves rendering speed. it may introduce transparency artifacts in regions with overlapping geometry due to the scarcity of rendering order. To address this problem, we propose neural view-dependent enhancement strategy, en- abling more accurate modeling of view-dependent effects conditioned on view- ing direction, 3D Gaussian geometry, and appearance attributes. In this way, Mobile-GS can achieve both high-quality and real-time rendering. Furthermore, facilitate deployment troduce first-order spherical harmonics distillation, a neural vector quantization technique, and contribution-basl pruning strategy to reduce the number of Gaussian primitives and compress the 3D Gaussian representation with the assis- tance of neural networks. Extensive experiments demonstrate that proposed Mobile-GS achieves real-time rendering and compact model size while preserving high visual quality, making well-suited for mobile applications. Project Page: https://iaaoiiaodu githt.. www.bblle/mobbeeeeeoooe gs-pjeeect
first identify alpha blending
nemory-ccnstraiin mobile platforms,
also in-
"Corresooniigg author; xin.yu@adde add.aide.cluuuuuuu

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Published as a conference paper at ICLR 2026
1200
1000
Bicycle 0.8
6.3
0151
Ktohen
4.1
4.8
807
971
400
0.8
431
357
146
Bloydte
Garden
Preproceesing IB Sorting
Resherization
B 3DGS
3DGS w/o sorting
Figure 2: Sorting as the primary performance bottleneck. Left: Runtime analysis of the orig- inal 3DGS highlights that the sorting operation incurs a significant computational overhead during inference. Right: Removing the sorting step substantially accelerates 3DGS, achieving several-fold speedup compared the original implementation.
INTRODUCTION
Neural Radiance Field (Mildenhall et al., 2021) is the first to leverage volume rendering for high- quality novel view synthesis (Barron al.] 2021
Wang al. 2025). which has been applied to 2024a b) and relighting (Wu et al. (023). 3D
practical applications, like self-driving Du et al.
Gaussian Splatting (3DGS) (Kerbl al. 2023) is recently introduced technique for high-quality 3D reconstruction that represents scenes as set of anisotropic 3D Gaussian primitives. In contrast traditional mesh- or voxel-based representations (Sitzmann et al., 2019 [Shrestha al., [021] Tsalicoglou et al. 2023 Liu et al., 2020), Gaussian splatting leverages the continuous and differen- tiable nature of 3D Gaussians, enabling photorealistic rendering, precise novel view synthesis, and high-fidelity reconstruction. However, deploying Gaussian splatting mobile platforms for real- time rendering remains challenging. The computational overhead of rendering tens of thousands of Gaussians, especially with the view-dependent effects, exceeds the capabilities of most modern mobile GPUs. This limitation highlights the pressing need for efficient solutions to enable real-time Gaussian splatting resource-consrrained platforms, such smartphones and AR headsets.
Several lightweight 3D Gaussian Splatting methods, such as Scaffold-GG -SG (Lu et al. 2024), Mini- Splatting (Fang & Wang 2024), SplatFacto (Tancik al. 2023), and C3DGS (Niedermayr al., 2023) improve efficiency through pruning and compact representations. However, these methods rely on the traditional alpha blending, which requires a sorting process render 3D Gaussians in the near-to-far order. We observed that this sorting process is the primary computational bottleneck, as shown in Fig 2 impeding real-time rendering on mobile devices. Therefore, to achieve real- time rendering performance on such platforms, there are several critical factors: (1) Order-free rendering: eliminating the consuminn sorting process; (2) Quantization: compressing 3D Gaussians to reduce memory and bandwidth consumption; (3) Fewer Gaussian points: reducing
number of primitives while preserving visual quality.
In this work, we propose a real-time Gaussian Splatting method tailored for mobile devices, named Mobile-GS. As shown in Fig. our proposed Mobile-GS can reach 116 FPS rendering speed on the mobile device with Snapdragon 8 Gen 3 GPU, demonstrating real-time rendering speed mo- bile devices. Our proposed Mobile-GS contains the following key components: (1) Depth-aware Order-independent Rendering: To circumvent the computationally intensive sorting process in- herent in traditional alpha blending, we propose a depth-aware order-independent rendering tech- nique, enabling faster rendering. To be specific, we discard the original alpha blending paradigm, which relies on the sorted 3D Gaussians. We propose a depth-aware weighting strategy that order- independently blends all related 3D Gaussians the pixel. This strategy explicitly decreases the weight of the far 3D Gaussians and increases the significance of the near ones, enabling real- real-time performance, avoiding the sorting process. Although the proposed depth-aware order-independent rendering facilitates real-time rendering, the unordered blending can introduce transparency arti- facts. To mitigate this, we further propose neural view-dependent enhancement strategy that lever- ages neural network conditioned on 3D Gaussian attributes and viewing direction to further capture view biew-dependent information. In this way, the rendering quality can be significantly improved, es- pecially for view-de dew-deeendentt effects. (2) First-order Spherical Harmonies Distillation: Since the original 3DGS uses the third-order spherical harmonic (SH) function to represent appearance, it in- troduces numerous parameters and increases the storage burden. Therefore, introduce spherical
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Published as a conference paper at ICLR 2026
harmonic distillation technique to distill the first-order SH parameters under the guidance of the pre- trained teacher model, thus achieving faster rendering speed and lower storage usage. (3) Neural Vector Quantization To deploy 3DGS mobile devices, the quantization process is necessary to largely reduce storage usage and improve rendering speed. Herein, we introduce a neural vec- tor quantization technique quantize 3D Gaussian parameters grouped by K-means and compress the distilled SH features using lightweight neural decoders, thereby substantially minimizing over- all storage costs. (4) Contribution-based Pruning: We also propose a contribution-based pruning strategy to prune redundant Gaussians according to their opacity and scale attributes. We reckon that the Gaussian with low opacity and scale indicates an insignificant contribution. With our pruning strategy, we can remove numerous Gaussians and further decrease storage costs.
Extensive experiments are conducted to qualitatively and quantitatively validate the effectiveness of our proposed Mobile-GS. We demonstrate that our method, deployed on mobile devices, achieves real-time rendering speed. Notably, our proposed Mobile-GS achieves high-quality and visually pleasing novel view synthesis, comparable to the original 3DGS, demonstrating that our approach can reliably reconstruct and render high-fidelity views. Our method consistently surpasses previous lightweight approaches, achieving state-of- state-of-the-arr the-art rendering speed and visually pleasing quality.
2 RELATED WORK
3D Gaussian Splatting. The recent 3D Gaussian Splatting (3DGS) technique (Kerbl et al. 2023 along with its numerous variants (Fang& & Wang 2024; Du et 2026 Lin et al. 2025 Bulo et al. 2025 Feng et al. 2025), employs anisotropic 3D Gaussians to represent scenes and leverages tile-based differentiable rasterizer to render novel views. MVGS Du et al. 2026) is the first to pro- pose multi-view learning to enhance the multi-view constraint of 3DGS in the optimization stage, which significantly improves the holistic rendering performance. Scaffold-GS (Lu et al.) 2024) introduces hierarchical scaffold structure reduce the number of Gaussians for high-quality ren- dering, while maintaining visual fidelity. Mini-Splatting (Fang & Wang. 2024) focuses on pruning and densification strategies to produce highly compact Gaussian structures. These methods facili- tate high- resolutioo rendering while preserving high visual fidelity. Subsequently, more and more methods (Chen et al., 2024b 2025 Wang al. 2024b) Liu et al. 2024) are proposed to increase the compression ratio for more lightweight representations. However, these approaches require render- ing Gaussians in a particular order, typically determined by depth through a sorting process. This depth-sorting process introduces multiple challenges, including increased implementation complex- ity and the potential for visual artifacts, such as abrupt texture variations and popping artifacts, as discussed in (Radl et al. 2024). In particular, we found that the computational overhead introduced by sorting is very serious, which significantly impedes real-time rendering on mobile devices.
Order Independent Transparency. Modeling transmittance remains longstanding challenge in computer graphics, as it is essential for rendering accurate and semm-transpprent structures such as flames, smoke, and clouds. Traditional methods to achieve this either successively extract depth layers, known as depth peeling (Bavoil & Myers 2008). or store and sort fragment lists using A- buffers (Carpenter 1984). Several approaches have proposed to circumvent explicit sorting by ap- proximate compositing, known as Order- arder-Inddeendent Transparency (OIT). Similar to depth peeling, b-buffer methods similarly have different depth layers but store and accumulate only the first k layers in single rendering pass (Bavoil et al., 2007). Another line of approach, stochastic transparency, commonly used in Monte Carlo rendering, samples fragments based on their depth and opacity, producing visually plausible results given a sufficiently high sampling rate (Enderton et al. 2010)- Recently, plenty of sort-free 3DGS works (Hou et al.] [025] Kheradmand et al. [202]] Hahlbohm jet al.) 20255 Sun al. (222)) have been proposed to achieve fast rendering without sorting, The representative work is SortFreeGS (Hou et al. 2025), which enhances the rendering speed via
C Wby + MN c,),((d,)
(1)
Whg + E (d,)
where w(d))) = exp exp(-ord) is a weighting function. Whgs os and 33 represent learnable param- eters, while d, denotes depth. In Eq. [ both the numerator and denominator summations, and since addition is commutative, these terms can be computed in any order. However, these methods cannot be directly employed in edge devices due to the large storage and significant inference delay.
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Published as a conference paper at ICLR 2026
3DGS
C= E7 q477)
Near to sorting
Tile based rendering
Cost up to 1/2 render time
Alpha blending
Mobile-GS
3D Goussion
Tile
Mi v/ d idft exolsmex de
Pixel
3D Gaussians
Parallel Per-Geussian blending
Depth-aware order-independent rendering
Figure 3: Rendering pipeline of proposed Mobile-GS compared with 3DGS. In the inference stage, different from 3DGS, our proposed method eliminates the tile-based rendering and the 3D Gaussian sorting process typically required for accurate alpha blending. Instead, the color of each 3D Gaussian for its related pixels in parallel and accumulate the color value for each pixel. Then, composite the foreground and background color in single pass. To further improve performance and maintain visual quality, we propose a depth-aware order-independent rendering strategy that replaces the original sorting-dependent alpha blending.
first compute
Gaussian Compression and Pruning. Recent researchers have explored compressing Gaussian representations through techniques such as vector quantization (Wang et al. [024bb Liu al. 2024
Papantonakis et al. 2024] Xie et al.] (2024) and entropy encoding (Chen et al.] 2024a Niedermayr 2023). Among these, LocoGS (Shin et al. (2025) introduces locality-aware strategy that com- presses all Gaussian attributes into compact local representations. There is another line of research
about Gaussian pruning technique eliminate Gaussians. LODGE (Kulhanek al. 2025 intro-
duces depth-aware 3D smoothing filter with importance-basee pruning to maintain LOD visual fidelity. Mausaann (Liu al. 2025) leverages a musked-rasterizationn technique dynamically assess the contribution of each Gaussian and prune them with low contribution. GaussianSpa (Zhang) al.] 2025) proposes a pruning technique to gradually restrict Gaussians to the target sparsity con- straint and keep rendering quality. While effective in reducing redundancy, these methods often suffer from significant rendering quality degradation and still incur considerable storage overhead. To address these limitations, propose
efficient compression framework that preserves essential Gaussian features while achieving compact attribute representation, making it particularly suitable for deployment constrae-c mobile devices.
3
METHODOLOGY
3.1
DEPTH-AWARE ORDER-INDEPENDEN RENDERING
Rendering Insight": Onder-independent rendering enables efficient Gaussian compositing.
Traditional alpha blending typically requires a depth-sorting procedure, wherein 3D Gaussians are composited near-to-far order to correctly accumulate color. Although this sorting-basee mech- anism ensures that Gaussians closer to the camera have more significant contribution to the final image, it incurs considerable computational overhead, particularly detrimental latency-sensitive and resource-comstrained equipment like mobile devices.
To address these limitations, we propose a depth-aware order-independent rendering strategy tai- lored for mobile devices as illustrated in Fig. 3 Our rendering mechanism differs fundamentally from conventional alpha blending. To be specific, our rendering strategy eliminates the need for depth sorting by introducing learnable, view-conditioned weighting scheme. The rendered pixel color C computed as a weighted color accumulation from 3D Gaussians, defined as:
Di C;
C (1 T)
+Tebg
0:30%
where c; and Cbg denote the RGB and background color of the i-th 3D Gaussian, respectively. T' I - ar) represents the global transmittance to differentiate the foreground and background.

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Published as a conference paper at ICLR 2026
2062
Rotation
Neunstenhanceed
vew-Ceepandeet
opacity
Camera
Layr Perosproon (MLP))
Figure 4: Overall illustration and visualization of view-dependent opacity modeling. Left: We leverage an MLP fed with 3D Gaussian scale, rotation, spherical harmonics, and the vector of the camera toward the 3D Gaussian as input to predict a view-dependent opacity. Right: We display that our Mobile-GS removes redundant opacity and keeps important Gaussians with high opacity.
0g = o; exp (- - Az;) means the alpha obtained from the opacity o, w; is a depth-aware weight that modulates the contribution of each 3D Gaussian based on its scale and position related to the camera. Specifically, we utilize the inverse depth to reduce the contributions of the distant 3D Gaussians. Moreover, we increase the weight of the Gaussian with larger scale. The weighting term wy can be determined as:
WA of +
Φi
+ exp( Smaz d;
(3)
de
where d; and Smaz mean the depth and the maximum scale of the i-th 3D Gaussian in the camera coordinate system. 6i means the view-dependent ger-Gaussian parameters, modulating the contri- bution of each Gaussian, which will be described in detail later. This formulation offers two key advantages. First, by removing the dependency on sorting, the proposed method enables efficient and parallel accumulation of Gaussian contributions, resulting in significantly faster rendering. It is crucial for real-time rendering on mobile hardware. Second, the weighting depth-awar modula- tion allows more flexible contribution modeling, enabling 3D Gaussians to dynamically adapt to complex scene structures, without discarding information from distant Gaussians.
Neural View-dependent Enhancement: Although our depth-aware indeeennneeennentt rendering significantly reduces computational costs, it results in a slight degradation in rendering quality. Specifically, due to the absence of strict depth-based compositing, objects that are spatially oc- cluded or partially overlapped may exhibit undesired transparency effects. To address this issue and enhance the fidelity of the rendered images, we propose neural view-dependent opacity enhance- ment strategy that incorporates explicit view-dependent information into the 3D Gaussian attributes.
As depicted in Fig. 4 we design lightweight multi-layer perceptron (MLP) that predicts the view- dependent opacity scalar for each Gaussian. This predicted opacity aims to modulate the visibility of Gaussians in a view-aware manner, thereby compensating for the drawback of order-free ren- dering and improving rendering quality. The input to the MLP consists of both the geometric and appearance-related features of each 3D Gaussian. To be specific, for a given 3D Gaussian posi- tion 14, we compute its direction from the camera center and normalize the final result to obtain the Camera-Gaussian vector P.
,t-,, || where t, means the center of the v-th training camera. This vector encapsulates the relative orientation between the camera and the Gaussian center, providing critical view-dependent cues. To further enrich the input representation, we incorporate additional geometric and appearance-related per- Gaussin attributes, including the scale 8; E R3, rotation pa- rameter r; E SO(3), and spherical harmonic coefficients Y, which describe the anisotropic shape and view-dependent color of the Gaussian. By feeding these composite feature vectors into the MLP, the model learns to predict a scalar that adaptively modulates the contribution of each Gaus- sian based on both its intrinsic attributes and the current viewing direction. Therefore, the neural enhanced view- riew-dependent o, and opacity o can be formalized as:
= (P,, Fr. Yi), Φ: = ReLLUMMLP (F)). o, = (FL.. where (-)) means the Sigmoid function. MLP,, MLP,, and MLP,, denote the MLP functions for feature F, weight & and opacity o. Specifically, view-dependent á acts as a depth attenuation factor, adaptively scaling the influence of each Gaussian based on its distance to the camera. The view-conditioned opacity o, serves as a corrective factor in the rendering pipeline, allowing the system dynamically suppress the transparency of the occluded regions. As result, our Mobile- GS effectively mitigates the transparency artifacts in aappthuuu scenarios, leading to higher rendering quality and better preservation of scene geometry.
(4)
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Published as a conference paper at ICLR 2026
3.2 DISTILLATION AND QUANTIZATION
*Compressed Insight": First-order SH and quantization enable efficient compression.
First-order Spherical Harmonies Distillation: Inspired by LightGaussian (Fan al. 2024).
employs a distillation strategy to project third-order spherical harmonics (3 x 16 coefficients) onto a second-order representation (3 coefficients) for efficient rendering. Different from that, in this work, we introduce first-order spherical harmonics (3 x 4 coefficients) distillation framework that encourages a more compact model to approximate the directional radiance of powerful teacher model via Casill TPT Epep C Cpl where P denotes the set of pixels. Ctee and C represent the rendered pixel colors from the teacher and student, respectively. Except for that, we also propose a scale-invariant depth distillation loss to impose depth supervision from the teacher model through:
Caepth (D, Dreay
Σ PEP
(log(D,) - log(() ) |
Σ PEP
(log(D,)) - logg(Drmm)
P
8 for training stability. We do not use the strict restriction like L1/L2 loss since the depth from the teacher and student may have a slight difference, and the depth of the teacher model is not always reliable.
where depth Deca D + E and we set E as 1e-
Neural Vector Quantization: To efficiently compress the per-Gaussian attribute vectors while pre- serving high rendering fidelity, we propose neural vector quantization (NVQ) scheme tailored for 3D Gaussian splatting. Unlike traditional vector quantization methods that operate on the entire at- tribute vector using a single codebook, our method adopts sub-vector decomposition strategy (Lee) eet al. 2025) that enhances representation flexibility and compression efficiency. Specifically, given Gaussian attribute vector z RKL we partition it into K clusters {z1, #2, ........,,,..................... zK of length L by K-Means (Hamerly & Elkan 2003). The 3D Gaussian attributes in each cluster 2 E R are quantized using their own codebook t* E IR. RxL with B codewords, where denotes the num- ber of codewords per subspace. This multi-codebook quantization reduces the memory footprint of each codebook, mitigates codeword collisions, and simplifies lookup operations during inference. To further compress the final quantized attributes for storage, we apply Huffman coding to encode sequences at the end of training. This entropy-based compression technique significantly reduces the bitstream size without compromising runtime performance, enabling the deployment of our method on storage-constrained devices.
To further reduce the storage burden associated with per-Gaussian SH coefficients, we decompose the learned SH feature Y into diffuse component ha E R and a view-dependent component h, E R2 via the proposed neural vector quantization, and model them using lightweight multi-layer perceptrons (MLPs). This design eliminates the need to store high-dimensional SH coefficients directly for every Gaussian, instead leveraging compact neural functions to reconstruct SH features at inference time. The final SH features for rendering are computed as:
fa = MLP MLLPahhahha hw), fo = MLP,, hw),
where MLP, and MLP. o are separately parameterized neural networks predicting diffuse and view- dependent spherical harmonics components, respectively. Both MLPs are quantized to 16-bit preci- sion to minimize storage overhead while retaining representation capability. In the inference stage, we only use these MLPs to decode the SH features once as described in Eq. 6 This factorization further reduces memory costs for mobile devices.
3.3 CONTRIBUTION-BI BASED PRUNING
"Pruning Insight": Gaussians with larger opacity and scale have more important contribution.
pruning To reduce redundant Gaussian primitives during training, we adopt a contribution-
mechanism that jointly considers opacity and spatial scale statistics. At each iteration 4, we compute the per-primitive opacity values o, and the maximum scale Smax (g) across dimensions. A quantile threshold 7 is applied to identify low-contributing Gaussian candidates:
(1) opucity
{g E E < {+(o)}, C {g E EG 8max (g) Q (Smax)))

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Published as a conference paper at ICLR 2026
C where (-(-) denotes the 7-quantile operator, and Gprine Cppciity n n scale is the set of Gaussians selected as pruning candidates iteration t. Instead of immediately removing candidates, we aceu- mulate pruning votes for each Gaussian. Let V2{t) denote the accumulated vote count for Gaussian g at iteration initialized vg O. The update rule is
((t)
V ( [ E Cprnnel: 9new G E [[V > -pprune u]}:
(8)
where 1[:] is the indicator function. Iprnne and are the pruning interval and a vote threshold. A Gaussian is permanently pruned its accumulated votes exceed a threshold in every pruning interval. The updated Gaussian set is Inew- This strategy mitigates noisy fluctuations in opacity or scale during early training and progressively eliminates Gaussian primitives that consistently exhibit low contribution (low opacity) and negligible geometric extent (low scale).
3.4 IMPLEMENTATION
Training Loss: In this work, we optimize our proposed Mobile-GS with the rendering loss same as the original 3DGS (Kerbl et al. 2023), which utilizes C1 and LDSSIM:
Libb XL Cg)) + (1 A)LussiM(C, Cg),
(9)
where À balance the contributions of the L1 and LDSSIM loss function. It is typically set as 0.8. Therefore, the total loss can be computed via:
C = Ligh + Adisui Edisiil + AdepthhCeppil
(10)
where Adistill and Adeph balance the weight between the rendered image loss, the distillation loss, and the depth loss. In our experiments, we empirically set Adisill and Adepth as I and 0.1, respectively.
RTX 3090 GPU using PyTorch. We develop custom CUDA Kernels for the adaptation of our proposed depth-aware order-independent rendering. We utilize Mini-Splatting (Fang & Wang 2024) as the teacher model the distillation stage. The main difference between our Mobile-GS with Mini-Splatting is that it uses traditional al- pha blending and does not have a quantization process. We train our method with 60k iterations. We initialize MLP o to output é as 1, thereby stabilizing the training process. At the 35k-th iteration, we launch the proposed neural vector quantization as shown Eq, (6 To improve holistic rendering per- formance, we adopt multi-view training (Du et al. 2026) for more powerful multi-view constraints to 3D Gaussian primitives. Additional implementation details can be found in the appendix.
Training Details: We train our proposed Mobile-GS
To evaluate the efficiency of our method on resource- constrreed devices, we implement our approach using Vulkan 2.0, a modern, cross-platfone graphics and compute API. Vulkan offers low-overhead, perfomeefor access GPU hardware and is well-suited for mo- bile and embedded platforms due to its explicit control over rendering and memory management. This implementation enables fair and consistent comparison of the real-time rendering perfor- mance and computational overhead on mobile GPU architectures.
Deployment Mobiles:
4 EXPERIMENTS
4.1 QUANTITATIVE AND QUALITATIVE RESULTS
In our experiments, compare our proposed Mobile-GS against several state-of-the-art methods, including 3DGS (Kerbl et al. 2023), LightGaussian (Fan et al., 2024]. AdR-Gaussian (Wang et al. 2024ah. SortFreeGS (Hou et al. (025). Speedy-Splat (Hanson al. 2025), C3DGS (Lee et al, 2024). GES (Ye et al. 2025), and LocoGS-S (Shin et al. 2025]. As shown in Table we display quantitative results of our Mobile-Gi -SS compared with these state-of-the-ar methods across the three representative datasets. These methods cannot achieve high-quality novel view synthesis in terms of rendering performance and efficiency, while our proposed method can achieve comparable per- formance compared with 3DGS. It indicates that our Mobile-GS delivers high-quality novel view synthesis performance and provides a more flexible solution for mobile deployment. It is attributed to our proposed neural view-dependent enhancement strategy and a series of compression techniques that empower riewddependent information perception and improve rendering efficiency. In addition to the quantitative comparisons, also present the qualitative results. As illustrated in Fig. our
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Table Quantitative comparisons of state-of-the-art 3D reconstruction methods on real-world datasets. We evaluate and report performance on three commonly used datasets, such as Mip-NeRF 360 (Barron al. 2022), Tank& &eepless (Knapitsch et al. 2017). and Deep Blending (Hedman et al. 2018). We highlight the best results among the lightweight 3DGS methods.
Duteet
Metica
PENR
SS0M?
PSNRT
3P5T
PSNR1
LPIPS.
Mara Krnkk
2011
0:815
01:14
339.
174
11.14
0.181
521 9M1
70.41
0.907
697.3 MB
214
LileCon Sen Ruceal 2014
27.8
0.811
0.344
60
227
22.01
0.883
0.264
28.74
0.556
0.325
48.2 MB
271
Gassin
SunFreett
Partes
2702
0.775
0.367
351.4
23.8:
0.254
47:5MB
0.352
724.2 2B4
793
Speedy Salr
2005
3667
0.781
0.266
MM
401
23.08
0.531
0.341
0244MB
537
29:11
0.844
0.309
71. MB
463
CSb65
La 222
2783
0.797
0.347
30/6
184
23.33
0.31
0.000
318
14
0.60
0.258
24.7 MB
109
M-NNb-GTTom
0.907
11235
33.09
0.356
2
1379
20.93
6.966
MB
3DGS
SortFreeGS
Speedy-Snlat
Mobile-GS (Ours)
Ground Truth
325 MB7400
336.M1/74
32. 7MB/480
3.3MB/1248
Storager ageriPs
278 MB/431
291 MB/758
23. 9MB433
3,6MB/133
Storago/EPS
396 MBX337
436 Mavess
37.5MBX458
A.MB 107
Storage/FPS
Figure 5: Qualitative comparisons of existing methods and our proposed Mobile-GS. We dis- play the storage cost and FPS per scene to better demonstrate the performance of our method. We extract close-ups to highlight the differences.
Table 2:
Evaluation on the mobile device Table 3: Ablation Study of the proposed com- ponents. We report results on the Mip-NeRF 360 dataset. The inference speed FPS is evalu- ated on the Desktop RTX 3090 GPU.
with Snapdragon 8 Gen 3 GPU. 3DGS* Mini- Splatting* and SortFreeGS* mean the quantized version through Huffman encoding.
Method
PSNR FPS** Storage 1 Training 1
Method
PSNR
FPS
Storage
3DGS
27.01
0.5
Mobile
MinitSplatting
27.02
36.9
w/o independent
Spoody-Splat
26.92
79.5 MB
0.4
w/o viow-dopendent
26.68
1227
4.4 MB
HAC
26.98
11.8 MR
0.7 h
w/o neural quantization
27.33
841
121 MB
LocoGS-S
27.02
8.5 MB
0.8
w/ Oth-order SH distill.
27.04
1219
3.6 MB
C3DGS
27.03
14
30.6 MB
0.6
w/ 2nd-order SH distill.
27.13
917
7.3 MB
GES
26.98
18
29.4 MB
0.7 h
w/ 3rd order S11
27.15
841
9.6 MB
SoctPreeG8*
26.74
24
64.3 MB
1.3
wro dopth Eq.3
27.03
1167
4.5 MB
Mobile-GS (Ours)
27.12
127
4.6 MB
1.5
wro scall in Eq.3
27.08
1171
4.5 MB
proposed Mobile-GS can achieve sharper and more consistent novel view synthesis quality compa- rable to 3DGS, even better than 3DGS in the view-dependent effects. These quantitative and quali- tative results demonstrate that our Mobile-GS can achieve high-quality novel view synthesis results, especially in scenes with complex geometry and lighting. This is because our proposed Mobile- GS is integrated with the proposed view-dependent enhancement to improve view-dependent rendering and facilitate the learning process of 3D Gaussians toward the complex scene structures.
Evaluation on Mobile: To validate the real-time performance on the edge device, we deploy our proposed Mobile-GS on a mobile device equipped with the Snapdragon 8 Gen GPU for the eval- uation. For fair comparison, we also quantize 3DGS and Mini-Splatting for the deployment and evaluation. As depicted in Table 2 our proposed method outperforms these state-of-the-as meth- ods in terms of rendering quality, speed, and storage costs.. These results demonstrate that our Mobile-GS is the most suitable for real-time rendering on mobile devices, compared with existing ofte-offtthears methods. It is attributed to our proposed depth-aware order-independent rendering.
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quantization, and pruning techniques, which eliminate the need of the 3D Gaussian sorting process, simultaneously render all 3D Gaussians, and significantly compress Gaussian parameters. Although our proposed Mobile-Gr GS requires more training time, its high-quality rendering and real-time infer- ence speed make it more suitable for mobile devices compared to existing methods.
Runtime Analysis: As illustrated in Fig. further provide detailed runtime analysis of our proposed Mobile-GS evaluated on four representa- tive scenes, covering both indoor and outdoor en- vironments from the Mip-NeRF 360 dataset. The reported runtime accounts for all essential compo- nents involved in the rendering pipeline, including the lightweight MLPs used for view-dependent ef- fects. Despite the inclusion of MIPs, which are of- ten regarded computationally demanding, design introduces minimal overhead. This demon- strates that Mobile-GS maintains a favorable balance between computational efficiency and model performance, ensuring real-time efficiency without compromising visual fidelity.
Blyell
033
016
Macher
WHE7s Eaproceninn
Figure 6: Runtime analysis of Mobile-GS.
4.2 ABLATION STUDY
As shown in Table we conduct ablation studies to demonstrate the effectiveness of the proposed components. When we replace the proposed depth-awar aware order-independent rendering with the orig- inal alpha blending. although the PSNR metric is slightly improved, the rendering speed is reduced significantly. It demonstrates the critical role of the proposed order-independent rendering for bet- ter rendering efficiency. When we do not employ the proposed view-dependent enhancement, the rendered visual quality deteriorates dramatically. It is because our proposed neural view-dependent enhancement strategy can effectively mitigate the problem of depth ambiguities in overlapping ge- ometry introduced by order-free rendering. When we do not utilize the proposed neural vector quantization, the storage cost increases dramatically, indicating its necessity for mobile deptoyment. When we do not leverage the proposed spherical harmonics (SH) distillation, the rendering speed is reduced, and the storage cost is increased, showing its importance for lightweight rendering. When remove the Gaussian depth or scale in our weighting function, the rendering quality degrades, which demonstrates the significance of these two attributes for our rendering formulation. These re- sults collectively validate the effectiveness of our components and demonstrate that each component is essential to achieving high-fidelity and real-time rendering on mobile devices.
Neural View-dependent Enhancement: further demonstrate the effectiveness of the proposed neural view-dependent enhancement strategy, we present visual comparisons of its ablation as illustrated in Fig. the absence of this proposed strategy, the rendered images suffer from noticeable transparency artifacts, particularly in regions with overlapping ge- ometry or depth ambiguity. These artifacts are pri- marily caused by the order-independent rendering mechanism, which does not account for the cover- age of each 3D Gaussian. In contrast, when the pro- posed neural view-dependent enhancement strategy is incorporated, these transparency artifacts are sig- nificantly reduced. This improvement is attributed that we use the neural network to model the relationship between the 3D Gaussian attributes and view-dependent lighting effects. This leads to more consistent and realistic rendering across viewpoints.
dependent
depandent
Figure Evaluation of the proposed neu- ral view-dependent enhancement strategy. "w/o" and "w/" mean the removal and the integration of the proposed neural view- dependent enhancement strategy.
Our proposed contribution- based pruning strategy removes redun- dant Gaussian primitives by jointly considering their opacity and scale attributes. To evaluate its effectiveness, we conduct detailed ablation studies, summarized in Table 4 The results clearly indicate that pruning based solely on either opacity or scale leads to a substantial degradation in performance, highlighting the limitation of using single criterion. In contrast, our pruning strategy leverages the complementary nature of these two attributes: opacity reflects the visibility of a Gaus- sian, while scale captures its spatial influence. By integrating both factors, method achieves
Contribution- based Pruning:

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Published as a conference paper at ICLR 2026
Table Ablation study of pruning strate- Table 5: Hyperparameter analysis about the prun- ing threshold. We employ our Mobile-GS without our contribution- pruning as the baseline and ad- just the pruning threshold to find a suitable trade-off.
gies. We leverage our Mobile-GS without
pruning as the baseline and leverage pruning
only opacity, scale, and both attributes.
Method
Baseline
Opacity
Scale
Opacity & Scale
Threshold
Baseline
0.1
0.2
0.4
0.6
Nom. x10
0.56
0.43
0.45
0.47
Nom. x10
0.56
0.55
0.47
0.34
0.18
PSNR
27.22
26.84
26.87
f FPS
27.22
27.15
27.12
25.85
FPS
135
132
164
Table 6:
Adaptivity of the proposed contribution-based pruning. Our pro- posed contribution-base
Table Analysis of the codebook size. We analyze different codebook sizes on the Mip- pruning can be
NeRF 360 dataset to find more balanced trade- off. The smaller codebook size means fewer storage costs.
applied in MaksGaussian (Liu et al., (202)) and Mlii--Splatinn for further Gaussian pruning.
Method
MaskGaussian
prune.
Mini-Splatting
prunc.
Codebook size
2
210
2
PSNR
27.24
27.16
27.41
27.38
PSNR t
25.52
26.83
27.12
27.15
Nom. x 100%
1.21
0.84
0,58
0.47
Storage |
3.84 MB
4.2 MB
4.6 MB
7.9 MB
more balanced pruning decision, allowing us discard a large portion of redundant primitives while maintaining high rendering fidelity. This design not only reduces memory and computational over- head but also demonstrates that effective pruning can be achieved with minimal loss of precision, thus striking a favorable trade-off between efficiency and accuracy.
In our proposed contribution- based pruning strategy, we introduce a predefined threshold to iden- tify Gaussians with low contribution. A larger threshold results in more aggressive pruning and consequently fewer Gaussian points. We evaluate various threshold values, as displayed in Table using Mobile-GS without pruming as the baseline and applying egy on top of it. We analyze thresholds ranging from 0.1 to 0.6 and observe that a threshold of 0.2 provides the best balance between rendering quality and computational efficiency. Accordingly, we adopt 0.2 as the pruning threshold our method. As shown in Table our contribution-basee pruning strategy can be seamlessly integrated with various existing GS pruning methods further reduce the number of Gaussian points. In particular, it can significantly reduce Gaussian points without substantial performance degradation.
contribution-bast based pruning strat-
Neural Vector Quantization:
Our proposed neural vector quantization employs K E-maans clustering encode Gaussian parameters into a compact codebook. The codebook size directly influences both rendering quality and storage cost. We evaluate a range of codebook sizes from 2 to 2 as depicted in Table 7 When the codebook is too small, the PSNR degrades drastically due insufficient representational capacity. Conversely, an excessively large codebook requires substantially more storage. Based on this trade-off, we select a codebook size of 2 which offers a favorable balance between lightweight storage and high-quality rendering.
5 CONCLUSION
In this work, we propose Mobile-GS, the first Gaussian Splatting method specifically designed for real-time rendering on mobile devices. To address the computational bottlenecks inherent in tradi- tional 3D Gaussian Splatting, we propose series of innovative techniques, including depth-aware order-independent rendering, neural view-dependent opacity enhancement, first-order spherical har- monics distillation, neural vector quantization, and contribution-basel pruning. These innovations jointly enable Mobile-GS to achieve high-quality novel view synthesis while dramatically reduc- ing memory, storage usage, and computational overhead. Extensive experiments demonstrate that Mobile-GS delivers rendering quality comparable to the original 3DGS, yet with a significantly smaller storage footprint and faster efficiency, achieving up 127 FPS on the modern mobile GPU.
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ACKNOWLEDGMENTS
This research funded in part by ARC-Discovery grant (DP220100800 XY) and ARC-DECRA grant (DE230100477 to XY). We thank all anonymous reviewers and ACs for their constructive suggestions.
REPRODUCIBILITY STATEMENT
We ensure the reproducibility of our work by detailed implementation descriptions and publicly available codes. The main paper and appendíx provide implementation details. To facilitate the reproducibility, we will release all codes, including training and evaluation code, upon acceptance of the paper.
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A LLM USAGE STATEMENT
In this work, we employed a Large Language Model (LLM) solely for language polishing purposes. Specifically, the LLM used to refine grammar, improve readability, and ensure consistency in tone and style across the manuscript. Importantly, the LLM was not used for generating novel ideas, conducting analysis, or contributing the scientific content of this work. All research design, implementation, and results presented herein are original contributions of the authors.
B PRELIMINARY: 3D GAUSSIAN SPLATTING
3DGS is a Gaussian- rendering approach that leverages anisotropic 3D Gaussians to represent scenes for high-quality real-time rendering. Each 3D Gaussian G, is parametrized by (H4, E, o,, Y,). The mean E IR'S specifies the 3D position the Gaussian in world space. The covariance matrix E, E 3R333 usually symmetric and positive semi-definite, describes the anisotropic spatial extent and orientation of the Gaussian cllipsoid. M; can be decomposed into and Tú, where represents the scale of 3D Gaussians and denotes the rotation. The appearance of each Gaussian controlled by its color C; and an opacity factor o E [0, 1], where the color is represented by the spherical harmonic coefficients Y,
To render an image. each Gaussian is first projected onto the image plane via standard perspective camera model. In this process. 3DGS has a depth-sorting process to render the 3D Gaussians in order. It ensures the rendering of 3D Gaussians from near to far order. After the sorting process, the final pixel color can be computed by the alpha blending:
II a.). a; = exp (-) Auf M An:))
-GG Go,T, T, C Σ
A
(11)
where 7' denotes the transmittance in the alpha blending. Ar; = z;-M; denotes the positional offset. N represents the number of 3D Gaussians. Overall, Gaussian Splatting provides differentiable, compact, and efficient method to represent and render complex scenes, making it especially suitable for real-time applications and gradicnt- based optimization in neural rendering pipelines.
C ADDITIONAL IMPLEMENTATION DETAILS
Neural View-dependent Enhancement. To effectively capture view-dependent appearance varia- tions, we design a lightweight three-layer multilayer perceptron MLP, with progressively decreas- ing neuron counts 256, 128, and 64 per layer. This MLP extracts discriminative features from the 3D Gaussian primitives and predicts both the opacity and auxiliary feature 6. We apply a Sigmoid activation function to the opacity output to constrain it within the range [0, 1]. while a ReLU activation is employed for é to enhance its representational flexibility. The network is trained jointly with the 3D Gaussian primitives for 30k iterations, ensuring that the learned features are tightly coupled with the underlying Gaussian representation.
Neural Vector Quantization. For the efficient compression of Gaussian attributes, we adopt a neural vector quantization scheme. Specifically, the attributes are partitioned into five groups of sub-vectors, each of which is clustered using K-means. The resulting discrete codes are then fur- ther compressed via Huffman entropy coding reduce storage redundancy. To refine the quantized representations, we employ compact MLP modules associated with each group. These MLPs are in- tentionally designed with single hidden layer consisting 64 neurons, striking balance between training efficiency and representational accuracy. This lightweight design significantly accelerates training while maintaining high-quality reconstruction the Gaussian attributes.
Distribation-based Pruning. To remove redundant Gaussian primitives and improve efficiency, we introduce distribution-based pruning strategy. We set the interval Iprune to 1000 and v to 0.6. Pruning is applied only during the initial 25k iterations to prevent excessive removal in later stages of training, where finer details are critical. Furthermore, we employ a redundancy identification threshold 0.2 to selectively discard Gaussian primitives with marginal contributions. This design allows the model to retain representation capacity while substantially reducing unnecessary primitives in early training.
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Table Comparison with different sorting-free methods. SortFreeGS* means its quantized ver- sion. We report metrics on the Mip-NeRF 360 dataset for the mobile equipped with the Snapdragon 8 Gen 3 GPU. FPS* means the rendering speed on the mobile.
Method
Rendering
Weighting
PSNR 1
Storage
FPS
SoriFreeGS*
Stylliy soo.rrd))
w(d.)) expl (od,)
26.74
64.3 MB
Mlg a.)(d
GES
C
We
Wo(x) Σ (1dd < +
27.02
29.4 MB
24
Ours
C= (1
EW siants Ten
2y of + + expl a0))
27.12
MB
127
2in1 9ean
D DISCUSSION AND LIMITATIONS
D.1 DIFFERENCE WITH SORTING-FRER METHODS
Although SortFreeGS (Hou et al. 2025) GES YY et al. 2025). and our proposed Mobile-GS em- play sorting-free rendering, Mobile-G's GS serves as a more comprehensive and efficient rendering method depicted in Table 8 It is worth noting that SortFreeGS* refers to the quantized version of SortFreeGS, as the original method does not include a quantization stage. In terms of PSNR, storage cost, and rendering FPS, Mobile-GS consistently achieves superior performance over prior sorting-free techniques. This improvement stems from our integrated design that incorporates quantization, pruning, and view-dependent enhancement mechanism. With respect to rendering formulations, GES follows a formulation similar to SortFreeGS, whereas our method adopts trans- mittance proxy enriched with rien-dependent modulation to more effectively capture the underlying 3D scene structure.
The Gaussian weight computation also differs substantially across these approaches. SortFreeGS leverages the Gaussian depth to modulate its contribution but does not account for the Gaussian scale, which we find to be critical. GES, on the other hand, relies on a two-stage rendering. first renders depth image using conventional volume rendering and then filters out distant Gaussians by comparing their depths against the rendered depth map for later sorting-free rendering. This two-stage rendering pipeline relies on precise depth rendering and increases computational load, so is not well-suited for mobile deployment. scale attributes of each Gaussian to compute importance weight, reflecting the intuition that farther Gaussians should have lower contribution, while larger Gaussians typically provide more meaningful rendering evidence.
contrast, Mobile-GS exploits both depth and
Theoretically, A key challenge for sorting-free methods is the potential order ambiguity in regions where geometry overlaps. SortFreeGS attempts address this by introducing additional spherical harmonics parameters to model view-dependent opacity. However, this design incurs significant overhead and unfavorable for practical mobile usage. Our Mobile-GS resolves this limitation by enhancing the view-dependent effect through Iearnable parameter ó, predicted by a lightweight MLP conditioned on Gaussian attributes. This formulation achieves high-quality rendering without introducing prohibitive computational or memory burden. Overall, Mobile-GS is carefully tailored to minimize resource consumption, reduce Gaussian parameter storage, and maintain real-time ren- dering performance on mobile hardware.
D.2 DISCUSSION
Mobile-GS a Gaussian- based method that can achieve real-time rendering on mobile and resource- constrained platforms without significantly sacrificing rendering quality. The proposed depth-aware order-independent rendering replaces traditional alpha blending with a sorting-free scheme, sub- stantially improving runtime efficiency. Combined with neural view-dependent enhancements and spherical harmonics distillation, our approach maintains visual fidelity even under complex scenes. To address memory limitations, neural vector quantization strategy is employed, improving stor- age efficiency and enabling large-scale scene representations to be deployed on mobile devices with limited memory.
Experimental results demonstrate that Mobile-GS achieves a compelling balance among rendering speed, storage footprint, and visual quality. It consistently outperforms existing lightweight Gaus-
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Table Per-scene PSNR results of state-of-the-art novel view synthesis methods on Mip-NeRF
360 dataset (Barron et al. 2022). The best results are highlighted.
Method
blcycle
garden
stump
flowers
treehill
counter
kitchen
room
bonsal
3DGS (Kerbl al. 2023 Speedy-Splal (Hanson et al. 2025
25.23
27.38
26.55
21.44
22.49
28.70
30.32
30.63
31.98
24.78
26.70
26.79
21.21
22.57
28.28
29.91
30.99
31.29
Mobile-GS (Ours)
24.91
26.65
26.82
21.41
22.77
28.82
30.47
30.95
31.25
Table 10: Per-scene SSIM results of state-of-the-art novel view synthesis methods on Mip- NeRF 360 dataset (Barron et al. 2022). The best results are highlighted.
Method
blcycle
garden
stump
flowers
treehill
counter
kitchen
bonsal
3DGS (Kerbl al. 2023) Speedy-Splat (Hanson et al. 2025
0.765
0.864
0.770
0.602
0.633
0.907
0.925
0,918
0.940
0,704
0.815
0.765
0,561
0.590
0.878
0.894
0.905
0.927
Mobile-GS (Ours)
0.740
0.823
0.777
0.593
0.643
0.905
0.920
0.924
0.936
Table 11: Per-scene LPIPS results of state-of-the-art novel view synthesis methods on Mip- NeRF 360 dataset (Barron al. 2022). The best results are highlighted.
Method
bicycle
garden
stump
Boowrs
treehill
counter
kitchen
room
bonsai
3DGS (Kerbl al. 2023
0.211
0.108
0.217
0.339
0.329
0.201
0.127
0.220
0.205
Speady (Hanson cral. 2025
0.333
0.213
0.288
0.419
0.463
0.260
0.198
0.260
0.231
Mobile-GS (Ours)
0.270
0.180
0,250
0,356
0.354
0.195
0.132
0.194
0.187
sian Splatting methods across multiple benchmarks, highlighting the effectiveness of our proposed components, including depth-aware order-independent rendering, neural view-dependent enhance- ment, spherical harmonics distillation, neural vector quantization, and contribution-base pruning.
D.3 LIMITATIONS
Despite its advantages, Mobile-GS contains several limitations: (1) Training Cost and Complexity: Although inference is fast, training Mobile-GS remains computationally intensive due to the pro- posed components (e.g., spherical harmonics distillation, neural vector quantization, neural view- dependent enhancement). Additionally, the model requires pretraining on desktop GPUs before mobile deployment, limiting its accessibility for real-time data acquisition and retraining on the device. (2) Scene Generalization: While Mobile-GS performs well on standard benchmarks, is optimized per-scene and does not generalize across scenes without retraining. This limits its imme- diate usage in applications requiring dynamic scene capture or rendering in unseen environments, such real-time AR reconstruction. (3) Quantization Degradation: Although the proposed neu- ral vector quantization is highly effective in compressing Gaussian attributes, there still remains a trade-off between compression ratio and reconstruction quality, especially for fine- fine-grained appear- ance details. Excessive quantization may introduce minor color shifts or blurring artifacts in highly textured regions.
E ADDITIONAL QUALITATIVE AND QUANTITATIVE RESULTS
As shown in Table 9 10 and we provide detailed per-scene quantitative results, between our proposed Mobile- GS. 3DGS [erbl et al., 2023). SortFreeGS (Hou et al.) 2025). and Speedy- Splat (Hanson et al. 2025). The results indicate that Mobile-GS achieves performance comparable to these state-of-the-art methods and even surpasses them in several scenes. Additional qualitative results novel view synthesis are presented Fig. 8, where Mobile-GS demonstrates superior rendering quality. This improvement is primarily attributed to proposed neural view-dependent enhancement strategy, which facilitates better fitting and adaptation of 3D Gaussians toward view- dependent effects. It enhances the representation capacity of 3D Gaussian properties, thus achieving better results. These results further demonstrate that our proposed Mobile-GS not only supports real-time rendering mobile devices but also maintains high-quality novel view synthesis.
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Published as a conference paper at ICLR 2026
3DGS
SortFreeGS
Speedy Splat
Mobile-CS (Ours)
Ground Truth
www.ww
www
Figure & Additional visual comparisons of Speedy-Splat, SortFreeGS, 3DGS, and our pro- posed Mobile-GS. We highlight the close-up for better differentiation.
Mip-NeRF 360
Tanks&Temples
Deep Blending
3DGS 19%
3DGS 11%
3DGS 25%
LocoGS-S 10%
Mobile-GS (Ours)
LocoGS-S 11%
Mobile-GS (Ours) 59%
LocoGS-S
Mobile-GS (Ours)
64%
79%
Figure 9: User study of rendering quality between proposed Mobile-GS, 3DGS (Kerbl et al. 2023), and LocoGS-S (Shin et al. 2025). The higher rate means more users like it.
F USRER STUDY
As illustrated in Fig. 9 we conduct user study to evaluate the rendering quality of our proposed Mobile-GS. To be specific, we train and render on the publicly available datasets, including Mip- NeRF 360 (Barron al. 2022). Tank&Temples (Knapitsch et al. 2017]. and Deep Blending (Hed- man et al. 2018). with 3DGS (Kerbl et al., (023). LocoGS (Shin et al., (025). and our proposed Mobile-GS. For a fair comparison, we also quantize 3DGS. total of 30 volunteers participated in the study, rating the quality of videos involving novel view synthesis produced by each method. These results suggest that most of the participants preferred the renderings produced by our Mobile- GS, indicating higher visual quality. This preference is largely attributed to our tailored design for mobile platforms, incorporating neural view-dependent enhancement and neural vector quantiza- tion, while 3DGS often exhibits floaters and rendering artifacts. These results further demonstrate that Mobile-GS delivers visually appealing rendering results and performance, especially under resource-constrained mobile environments.
G ADDITIONAL MOBILE TESTING
For more comprehensive analysis, further conduct detailed evaluations mobile device equipped with a Snapdragon Gen GPU, As summarized in Table [2, we report both the cold- start FPS (measured immediately launch) and the steady-state FPS (measured after thermal equi-
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Published as a conference paper at ICLR 2026
Table 12: Steady-state FPS evaluation the mobile. The larger FPS means faster rendering speed.
Method
3DGS
Spoedy-Splat
SonFreeeGS
Mobile-GS (Ours)
Cold-start FPS
8
19
127
Steady-state HPS
Table 13: Power draw measurement the mobile. We measure different Vulkan operators and report their power (W) on the mobile with the Snapdragon Gen 3 GPU.
Preprocessing
Sorting
MLP
Rasterization
Total
3DGS*
1.64
2,09
0
2.16
5.89
SonFFreeGS*
1.78
0
0
2.25
4.03
Mobile-GS
0.17
0.24
0.83
librium). This allows to clearly analyze the performance change over time and the impact of thermal throttling mobile rendering. On mobiles, FPS drops over time because of thermal throt- tling, power limits, GPU clock downscaling, and NPU/CPU frequency limits. can find that our Mobile-GS can still achieve 74 Steady-state FPS, which demonstrates the effectiveness of our Mobile-GS for real-time rendering on mobiles.
We further report the power-consumption measurements the mobile device, summarized in Table 13 Using the Qualcomm Trepn Profiler, we measure the power draw (W) of the Vulkan operators and MLP when running the Mip-NeRF 360 dataset, and compare our Mobile-GS with 3DGS* and SortFreeGS*. Here, 3DGS" and SortFreeGS* denote their quantized variants adapted for deployment Snapdragon Gen mobile GPU. The results show that Mobile-GS achieves the lowest power consumption among all methods, highlighting the practical efficiency and suitability of our approach for mobile deployment.
19


Reviews:
[{"review_id": "4XMLFPjqDQ", "reviewer": "Reviewer_JUSS", "summary": "This paper proposes Mobile‑GS, a 3D Gaussian Splatting pipeline designed for real‑time rendering on mobile devices. The key contributions include: \n\n1) Depth‑aware order‑independent rendering (OIR) that removes near‑to‑far sorting by blending all Gaussians affecting a pixel with a depth/scale‑modulated weight; the weight includes an MLP‑predicted, view‑dependent factor ϕ. A small neural view‑dependent opacity/weighting module combats transparency artifacts that arise from dropping strict alpha compositing.\n\n2) Compression for mobile: (i) first‑degree SH distillation from a teacher (Mini‑Splatting) to reduce color parameters, (ii) neural vector quantization using sub‑codebooks plus tiny decoders for diffuse/view‑dependent SH components, and (iii) contribution‑based pruning guided by opacity and maximum scale.\n\n3) Implementation & results: a Vulkan implementation on a Snapdragon 8 Gen 3 device; the method achieves 116–127 FPS at mobile resolutions with ~4–5 MB per‑scene storage, and >1,100 FPS on an RTX 3090 with similar or better quality than lightweight baselines. The paper identifies sorting as the desktop bottleneck and provides runtime breakdowns showing the MLP overhead is modest.", "questions": "1. Please clarify the exact computation of T in Eq. (2). If it is not the product in the definition (which requires sorting), what approximation is actually used and how is it implemented? Is it related to weighted blended OIT (e.g., a transmittance proxy from aggregated α)? A small derivation or pseudocode would help.\n\n2. What data structure replaces tile binning? Are you using screen‑space bounding ellipses with per‑pixel lists, hierarchical culling, or compute‑shader binning? Please quantify the cost of building these lists, especially on mobile.\n\n3. What ranges are enforced for 𝑑_𝑖 and 𝑠_max? Are terms clamped or normalized per‑tile/view? Could you share an ablation removing each term? \n\n4. What exact resolution(s) and camera path were used for Table 2? How long were runs and what was the steady‑state FPS after 5–10 minutes? Any power draw measurements?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "++ The paper starts from a concrete performance study showing that near‑to‑far sorting dominates 3DGS inference time. It then replaces sorting with a depth‑aware, order‑independent blend: per‑pixel colors are computed by normalizing a weighted sum over all contributing Gaussians, where the weights increase with proximity and scale and are modulated by a small learned, view‑dependent factor. \n\n++ Order‑independent blending can cause depth‑ambiguity/“see‑through” artifacts; the authors respond with a tiny opacity/weighting MLP that conditions on Gaussian geometry, SH appearance and view direction to predict 𝜙 and a view‑conditioned opacity. The ablation in Table 3 shows that removing this module causes a notable quality drop (e.g., PSNR from 28.45 → 28.06 on Mip‑NeRF360) while the runtime overhead is small.\n\n++ Three components—first‑degree SH distillation, neural vector quantization (NVQ) with sub‑codebooks and tiny decoders, and contribution‑based pruning—work together to shrink the footprint while keeping quality.", "weaknesses": "-- Eq. (2) uses a global transmittance 𝑇, then defines 𝑇, which is order‑dependent and index‑ambiguous under OIR; this needs a precise approximation/implementation \n\n-- Fig. 3 claims tile‑based rasterization is removed and “all Gaussians associated with a pixel” are blended, but the paper doesn’t detail how per‑pixel lists are built/cached on GPU (desktop or mobile).\n\n-- The proposed weight 𝑤_i (Eq. (3), pp. 4–5) blends squared, inverse‑squared and exponential terms whose dynamic ranges can differ by orders of magnitude. The paper shows an ablation for turning OIR on/off (Table 3), but not a component‑level analysis, nor a discussion of clipping/normalization. Without this, it’s hard to assess numerical stability, generalization to thin structures, and the sensitivity to scene scale.", "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": "b9aOIuGYEC", "reviewer": "Reviewer_Jsav", "summary": "This paper proposes a mobile-friendly 3D Gaussian Splatting pipeline that removes depth sorting via depth-aware order-independent rendering, then recovers quality with a lightweight, view-dependent enhancement MLP. It further compresses the representation using first-degree SH distillation, neural vector quantization, and contribution-based pruning to cut storage to a few MB while keeping fidelity. Experiments report >100 FPS on Snapdragon 8 Gen 3 and >1k FPS on RTX 3090 with competitive quality versus 3DGS and recent lightweight baselines.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- This paper claims per-tile sorting as the dominant bottleneck and introduces a simple, parallelizable order-independent blending scheme to remove it.\n- A small view-conditioned MLP effectively suppresses transparency/occlusion artifacts that arise from sorting-free compositing.\n- The compression stack (first-degree SH distillation + neural vector quantization + contribution-based pruning) is complementary and yields strong storage reductions with limited quality loss.\n- The evaluation is extensive, includes ablations/runtime breakdowns, and demonstrates impressive reported throughput on Snapdragon 8 Gen 3.", "weaknesses": "- The novelty relative to contemporary sorting-free methods (e.g., SortFreeGS, stochastic/OIT-style splatting) is incremental and would benefit from a deeper theoretical or empirical comparison.\n-  Some hyperparameters, such as pruning thresholds/schedules, codebook sizes and SH-order trade-offs need further analysis.\n- The related works on network design and pruning should be added.", "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": "lAsXiCjLlg", "reviewer": "Reviewer_sRjw", "summary": "Mobile-GS introduces the first real-time 3D Gaussian Splatting framework optimized for mobile GPUs by eliminating costly depth-sorting through a depth-aware, order-independent rendering scheme.\nCompared with the prior sorting-free Gaussian representation (SortFreeGS), Mobile-GS models view-dependent opacity and rendering weights in an implicit and lightweight manner, reducing the per-Gaussian parameter footprint. It further integrates spherical-harmonics distillation, neural vector quantization, and contribution-based pruning to enhance compactness and efficiency.\nThe method achieves ~125 FPS on a Snapdragon 8 Gen 3 device with only 4–5 MB of storage, while preserving visual fidelity comparable to full 3DGS.", "questions": "1. Pruning mechanism:\n This paper evaluates the contribution of each Gaussian primitive using scale and opacity, while prior works often rely on gradient-based importance or learnable pruning masks. Could the authors discuss the advantages and disadvantages of their design compared to these alternatives, particularly in terms of stability, computational efficiency, and adaptivity during training?\n2. Choice of teacher model:\n The framework employs a pre-trained teacher model for spherical-harmonics distillation. Could the authors clarify the motivation behind this choice and whether alternative teacher configurations (e.g., vanilla 3DGS) would impact performance or training cost?\n3. Potential use of the teacher model for initialization:\n Since the framework already depends on a teacher model, could this model also be leveraged to provide better Gaussian initialization or coverage, potentially reducing training time?\n4. Initialization source:\n Are the initial Gaussian primitives derived from COLMAP point clouds or the teacher model’s reconstructed points?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Real-time mobile performance: Demonstrates the first 3D Gaussian Splatting system achieving real-time rendering on mobile GPUs such as Snapdragon 8 Gen 3.\n2. Order-independent efficiency: The proposed depth-aware order-independent rendering removes the costly sorting step, significantly improving runtime without significant quality loss.\n3. Implicit view modeling: Replaces explicit per-Gaussian weights and opacity with a shared lightweight MLP, enabling stable training and reduced parameters compared with SortFreeGS.\n4. Compact design: Through spherical-harmonics distillation, neural vector quantization, and contribution-based pruning, the model compresses storage from hundreds of MB to only ~4–5 MB while maintaining comparable visual fidelity to 3DGS.", "weaknesses": "1. Extended training time: The use of a pre-trained teacher model for spherical-harmonics distillation doubles the overall training iterations, increasing computational cost.\n\n2. Complex weighting formulation: The depth-aware weighting term (Eq. 3) appears empirically designed and lacks clear theoretical justification or ablation on its components.\n\n3. Missing key baseline: The paper does not include a direct quantitative comparison with SortFreeGS in Table 2, which should serve as the most relevant baseline for this work.\n\n4. Incomplete dataset coverage (minor): Several Mip-NeRF 360 scenes are missing from the evaluation; including them would strengthen the completeness and reliability of the results.", "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": "BU6BMgscGx", "reviewer": "Reviewer_xVP3", "summary": "This paper proposes a novel method called Mobile-GS, which enables the deployment of the Gaussian Splatting (GS) method on mobile devices and achieves real-time rendering performance. The authors analyze the limitations of GS rendering and identify alpha blending as the main bottleneck. To address this issue, they propose a depth-aware, order-independent rendering scheme that eliminates the need for sorting. In addition, they introduce a neural view-dependent enhancement strategy to mitigate rendering artifacts. Post-processing techniques such as distillation and quantization are also employed to achieve efficient rendering on mobile devices.", "questions": "Please refer to Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The authors provide deep insights into the rendering strategy of 3DGS and effectively analyze its bottlenecks.\n2. The proposed depth-aware order-independent rendering approach is interesting and appears novel.\n3. The use of quantization and pruning methods successfully enables the deployment of 3DGS on mobile devices.", "weaknesses": "1. Do the authors analyze the precision or deviation of the depth-aware rendering theoretically? It would be helpful if they could provide a mathematical analysis or proof, along with additional exploratory experiments.\n2. The reviewer would like to know whether neural vector quantization and pruning affect rendering quality. Could the authors provide evaluation results rendered on mobile devices and compare them with other methods?\n3. What is the time cost of the proposed approach? It would strengthen the paper if the authors could provide this metric and compare it with existing methods.", "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": "16wMmJPhT1", "reviewer": "Reviewer_JkAA", "summary": "This paper aims to run 3D Gaussian Splatting representations on mobile devices. To this end, the authors first identified that sorting consumes a large portion of the computational cost. Therefore, the paper proposes a sort-free algorithm with lower computational costs compared to other existing sort-free methods. This is combined with view-dependent features to mitigate artifacts. To make the representation even more lightweight, the authors introduce color distillation, vector quantization, and pruning. The experiments cover both desktop GPUs and mobile devices, evaluated with both numerical metrics and user preference studies. With all these components, the results suggest that the proposed method increases FPS and decreases storage and peak memory, all while maintaining representation quality.", "questions": "- **Weighting term (Eq. 3)**: Could the authors provide the theoretical motivation or intuition behind the weighting term in Eq. 3? Including comparisons at the equation level would help readers better understand the context and the novelty of this component.\n- **Necessity of distillation**: Is the distillation step truly necessary for achieving strong performance? Since the proposed depth-aware, order-independent rendering pipeline could, in principle, be trained from scratch, it is unclear why the method depends on distillation rather than following the same training approach as the baselines.\n\nIf these points are adequately addressed, I would be willing to raise my score.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Strong performance. The proposed method shows strong performance over baselines and is well-evaluated on mobile devices. In addition, it provides thorough ablation studies that help the reader understand the role and effect of each component.\n- The paper provides a runtime analysis that identifies how expensive certain operations are, which operations are bottlenecks, and how these can be reduced for mobile devices. It also shows the resulting share of operations after removing sorting.\n- Beyond numerical metrics, the inclusion of a user study strengthens the performance claims from a user's perspective.", "weaknesses": "- The sort-free method seems to be a core part of this paper's novelty. However, a critical equation for the weighting term (Eq. 3) is not supported by or provided with an ample description of its underlying idea or theoretical grounding. The paper would also benefit from a direct comparison of the equations from different sort-free methods. Although there are descriptive paragraphs in the supplementary, the relationship between existing methods is not clearly shown at the equation level.\n- Although distillation is an important part of this method, the necessity of a teacher model is not fully justified, nor is the setup adequately explained. It's unclear why this method requires distillation unlike other methods. Since it requires much longer training, it would be helpful if the paper explained why the current comparison (directly comparing \"from scratch\" methods with the proposed method that additionally requires a teacher model) is fair.", "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)