OpenNovelty.org

Mobile-GS: Real-time Gaussian Splatting for Mobile Devices

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
3D Vision
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.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes Mobile-GS, a mobile-tailored Gaussian Splatting method combining depth-aware order-independent rendering, neural view-dependent enhancement, and a compression framework. It resides in the 'Order-Independent Rendering Techniques' leaf under 'Mobile-Optimized Rendering Acceleration', which contains only two papers total (including this one). This leaf is relatively sparse compared to denser branches like 'Model Compression and Efficiency' or 'Domain-Specific Applications', suggesting the paper targets a focused but less crowded research direction within the broader mobile rendering landscape.

The taxonomy reveals that Mobile-GS sits adjacent to several related branches: 'Computational Redundancy Exploitation' (temporal coherence, caching) and 'Mobile GPU Optimization' (shader-level techniques) are sibling leaves under the same parent, while 'Model Compression and Efficiency' (pruning, quantization) and 'Level-of-Detail and Scalability' (hierarchical representations) form neighboring top-level branches. The paper's order-independent rendering directly addresses the sorting bottleneck, distinguishing it from fragment-level pruning methods (excluded from this leaf) and general GPU rasterization optimizations (which belong under 'Hardware-Accelerated Rendering Systems'). Its neural enhancement strategy bridges rendering acceleration and quality improvement, connecting to the 'Rendering Quality Enhancement' branch.

Among thirty candidates examined, the depth-aware order-independent rendering contribution shows three refutable candidates out of ten examined, indicating moderate prior work overlap in this specific technique. The compression framework faces stronger overlap, with seven refutable candidates among ten examined, suggesting this aspect is less novel within the limited search scope. In contrast, the neural view-dependent enhancement strategy shows zero refutable candidates across ten examined papers, appearing more distinctive among the candidates reviewed. These statistics reflect a top-K semantic search plus citation expansion, not an exhaustive literature review.

Based on the limited search scope of thirty candidates, the work appears to combine established compression techniques with a less-explored order-independent rendering approach and a potentially novel neural enhancement strategy. The sparse taxonomy leaf (two papers) and moderate refutation rates suggest incremental novelty in rendering acceleration, with the neural enhancement offering the most distinctive contribution among the examined candidates. The analysis does not cover exhaustive prior work beyond top-K semantic matches and their citations.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
10
Refutable Paper

Research Landscape Overview

Core task: real-time Gaussian Splatting rendering on mobile devices. The field has evolved into a rich taxonomy addressing the unique constraints of mobile platforms—limited compute, memory, and bandwidth—while maintaining high visual fidelity. At the top level, the taxonomy organizes work into eleven branches: Mobile-Optimized Rendering Acceleration focuses on algorithmic and pipeline innovations tailored to mobile GPUs, such as order-independent techniques and fragment pruning; Model Compression and Efficiency targets reducing Gaussian primitive counts and memory footprints (e.g., Compressed 3D Gaussian[36], Reducing Memory Footprint[6]); Level-of-Detail and Scalability explores adaptive rendering strategies (FLoD[24], LODGE[25]); Streaming and Progressive Rendering handles bandwidth-constrained delivery (No Redundancy Streaming[20], PRoGS[7]); Hardware-Accelerated Rendering Systems leverage specialized units (Gaussian Blending Unit[40], GauRast[17]); Rendering Quality Enhancement addresses anti-aliasing and visual artifacts (Multi-Scale Anti-Aliasing[29]); Sparse-View and Few-Shot Reconstruction (FSGS[1], SparseGS[4]) and Dynamic Scene Reconstruction (4D Gaussian Splatting[3], Spacetime Gaussian[21]) tackle data-limited and temporal scenarios; SLAM and Real-Time Mapping integrates localization (RTG-SLAM[5], Gaussian Splatting SLAM[8]); Human Avatar Rendering specializes in facial and body capture (Human Gaussian Splatting[2], SplattingAvatar[30]); and Domain-Specific Applications span VR (Vr-gs[12], VRSplat[10]), medical imaging (Cinematic Anatomy[13]), and autonomous driving (SplatAD[9]). Several active lines of work reveal key trade-offs between rendering speed, memory usage, and visual quality. Mobile-Optimized Rendering Acceleration and Model Compression form a dense cluster, with many studies pursuing complementary strategies: some prioritize algorithmic efficiency (Sort-free Rendering[32], Mobile-GS[0]), while others emphasize aggressive compression (MobileGaussian[34], Lighthousegs[35]). Mobile-GS[0] sits squarely within the Mobile-Optimized Rendering Acceleration branch, specifically targeting order-independent rendering techniques to eliminate costly sorting operations on mobile GPUs. Compared to Sort-free Rendering[32], which also addresses rendering order dependencies, Mobile-GS[0] emphasizes practical deployment on resource-constrained devices. Meanwhile, works like GS-Cache[18] and Virtual Memory[48] explore memory hierarchy optimizations, highlighting an ongoing tension between real-time performance and the need to handle large-scale scenes. Open questions remain around balancing compression ratios with reconstruction quality, and whether hybrid approaches combining streaming, LOD, and hardware acceleration can achieve truly seamless mobile experiences across diverse application domains.

Claimed Contributions

Depth-aware order-independent rendering for mobile Gaussian Splatting

The authors introduce a rendering strategy that eliminates the computationally expensive depth-sorting process required by traditional alpha blending. Instead, they use a depth-aware weighting scheme that allows parallel accumulation of Gaussian contributions, enabling real-time performance on mobile devices.

10 retrieved papers
Can Refute
Neural view-dependent enhancement strategy

The authors propose using a lightweight MLP to predict view-dependent opacity for each Gaussian, compensating for transparency artifacts introduced by order-independent rendering. This network takes Camera-Gaussian vectors, scales, rotations, and spherical harmonics as input to adaptively modulate Gaussian visibility.

10 retrieved papers
Compression framework combining SH distillation, neural vector quantization, and contribution-based pruning

The authors develop a multi-component compression approach: distilling third-order spherical harmonics to first-order representations, applying K-means-based neural vector quantization with multiple codebooks, and pruning Gaussians based on joint opacity and scale criteria to minimize storage while preserving rendering quality.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Depth-aware order-independent rendering for mobile Gaussian Splatting

The authors introduce a rendering strategy that eliminates the computationally expensive depth-sorting process required by traditional alpha blending. Instead, they use a depth-aware weighting scheme that allows parallel accumulation of Gaussian contributions, enabling real-time performance on mobile devices.

Contribution

Neural view-dependent enhancement strategy

The authors propose using a lightweight MLP to predict view-dependent opacity for each Gaussian, compensating for transparency artifacts introduced by order-independent rendering. This network takes Camera-Gaussian vectors, scales, rotations, and spherical harmonics as input to adaptively modulate Gaussian visibility.

Contribution

Compression framework combining SH distillation, neural vector quantization, and contribution-based pruning

The authors develop a multi-component compression approach: distilling third-order spherical harmonics to first-order representations, applying K-means-based neural vector quantization with multiple codebooks, and pruning Gaussians based on joint opacity and scale criteria to minimize storage while preserving rendering quality.

Table of Contents

Mobile-GS: Real-time Gaussian Splatting for Mobile Devices | Novelty Validation