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Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting

Xiaobiao Du, YuAn Wang, Hao Li, Bosheng Wang, Xun Sun, Xin Yu

arXiv:2606.30017v1cs.CV

TL;DR

Mobile 3D Gaussian Splatting is hindered by the storage and inference overhead of high-order spherical harmonics. Flux-GS compresses radiance and prunes Gaussians using multi-view guidance, achieving competitive visual fidelity with real-time mobile rendering and reduced resource use.

  • Problem

    High-order spherical harmonics impose substantial storage and memory-bandwidth costs, limiting efficient 3D Gaussian Splatting deployment on mobile devices.

  • Method

    Flux-GS combines Monte Carlo specular-energy aggregation, attribute-conditioned SH enhancement, and multi-view alpha-based densification and pruning.

  • Results

    Flux-GS achieves competitive fidelity and real-time rendering speed with lower storage costs, fewer Gaussian primitives, and fast mobile inference.

  • Takeaways & Limitations

    Flux-GS provides a scalable, lightweight representation for real-time 3D scene rendering on resource-constrained mobile hardware.

  • Takeaways & Limitations

    Reducing to first-order SH loses capacity for highly complex mirror-like reflections, while initial training still requires peak memory comparable to standard 3DGS.

Abstract

from arXiv · show

Recent advances in 3D Gaussian Splatting have demonstrated unprecedented success in novel view synthesis. However, the substantial inference and storage overhead driven by high-order Spherical Harmonics (SH) are primary bottlenecks for mobile platforms. In this paper, we present Flux-GS, a real-time Gaussian Splatting method designed to achieve high-fidelity rendering with significantly reduced overhead for resource-constrained mobile platforms. We first propose a Monte Carlo Specular Energy Aggregator, sampling third-order radiance residuals and aggregating specular energy into a compact latent space. In this way, our method effectively preserves visually salient lighting features in lower-order bands without expensive distillation or pre-training. To mitigate the high-frequency details lost during compression, we introduce an Attribute-Conditioned SH Enhancement module. This module predicts Gaussian-aware offsets based on intrinsic Gaussian attributes, which enhance the first-order SH representation prior to inference, without extra inference costs. Furthermore, the original single-view gradient-based densification is prone to producing excessive Gaussians and overfitting to a certain view. We address these limitations by proposing a Multi-view Alpha-based Densification and Pruning strategy. By leveraging multi-view guidance, we ensure multi-view structure consistency and the precise removal of redundant primitives. Extensive experiments demonstrate that Flux-GS achieves substantial parameter reduction while maintaining competitive visual quality, offering a robust and scalable solution for real-time mobile rendering. Code: \textcolor{magenta}{\href{https://xiaobiaodu.github.io/flux-gs-project/}{https://xiaobiaodu.github.io/flux-gs-project/}}.

1 Introduction

Flux-GS targets mobile 3D Gaussian Splatting by addressing third-order Spherical Harmonics overhead and redundant Gaussian primitives. It combines compact radiance representation and fewer primitives to preserve competitive visual quality for real-time mobile rendering.

  • Motivation: Third-order Spherical Harmonics impose prohibitive storage and memory-bandwidth costs when applied to millions of Gaussians.The coefficients capture complex, view-dependent radiance but increase rasterization-phase overhead.
  • Motivation: Single-view gradient-based densification aggressively creates redundant primitives, increasing training time, storage footprints, and inference latency.The approach is also prone to overfitting to a particular view, limiting suitability for edge devices.
  • Flux-GS: Flux-GS is a mobile real-time Gaussian Splatting method designed for high-fidelity rendering with significantly fewer parameters.The framework combines a Monte Carlo Specular Energy Aggregator with additional technical innovations described in the introduction.
  • Results: Flux-GS achieves rendering quality comparable to existing baselines with fewer Gaussian primitives and faster inference than Mobile-GS.These claims are presented as outcomes illustrated in Fig. 1.
  • Results: Extensive experiments show that Flux-GS provides a robust, scalable solution for mobile real-time rendering while maintaining competitive visual quality.The method uses a powerful first-order SH representation to support high-fidelity 3DGS deployment on mobile devices.

2 Related work

Related work advances 3D Gaussian Splatting through faster training, Gaussian pruning, and compression. These approaches regulate primitive counts, improve multi-view consistency, remove insignificant Gaussians, and reduce storage overhead using quantization, entropy coding, structured representations, and factorization.

  • Gaussian Splatting: Mini-Splatting limits primitive count through importance-driven densification, while 3DGS-MCMC models training dynamics as a Markov Chain Monte Carlo process.These methods target training acceleration in 3D Gaussian Splatting.
  • Gaussian Splatting: Revisiting Densification introduces loss-driven densification, MVGS regulates training across views, and Taming 3DGS enforces a user-defined Gaussian budget.MVGS is described as improving multi-view consistency through multi-view regulated training.
  • Gaussian Pruning: Gaussian pruning methods remove redundant primitives using importance scores derived from ray contributions, opacity, gradient information, transmittance, or Hessian-based sensitivity.The cited examples include EAGLES and PUP 3DGS.
  • Gaussian Compression: Gaussian compression research uses scalar and vector quantization, entropy coding, anchor-based structures, and factorization techniques to reduce storage overhead.Vector quantization benefits from redundancy across Gaussian attributes, enabling compact encoding.

3 Methodology

Flux-GS reduces Gaussian appearance modeling to first-order spherical harmonics while retaining view-dependent detail through compact Monte Carlo energy latents. It also uses attribute-conditioned enhancement and multi-view alpha-guided densification and pruning to improve fidelity and control the primitive count.

  • Monte Carlo Specular Energy Aggregator: Flux-GS restricts appearance modeling to first-order spherical harmonics, requiring only 4 coefficients per channel.A Monte Carlo Specular Energy Aggregator compresses high-order radiance energy into a lower-order subspace without pretraining or distillation.
  • Monte Carlo Specular Energy Aggregator: Monte Carlo aggregation preserves sparse view-dependent specular energy by encoding residual magnitude and direction instead of absolute photometric residuals.The method samples directions on the sphere, evaluates third-order versus second-order SH radiance residuals, and stores compact latents Emag and Edir.
  • Attribute-Conditioned SH Enhancement: The compact high-frequency latents are mapped to first-order SH parameters, dynamically allocating restricted SH capacity to visually salient scene features.The mapping jointly adjusts Gaussian geometry and low-order appearance while avoiding expensive distillation.
  • Attribute-Conditioned SH Enhancement: Attribute-conditioned enhancement predicts SH offsets from normalized first-order coefficients, opacity, scales, position, and rotation, then adds them to the original coefficients.The enhanced coefficients are defined as c^out_i = c'_i + Δc_i.
  • Multi-view Alpha-based Densification and Pruning: Multi-view alpha-based densification and pruning evaluates Gaussian contributions across sampled views to improve geometry modification and avoid single-view overfitting.The strategy clones or splits Gaussians that consistently contribute to high-error regions and moderately prunes non-contributing or artifact-inducing Gaussians.

4 Difference with Mobile-GS

Flux-GS differs from Mobile-GS by replacing distillation with Monte Carlo energy aggregation, enabling attribute-conditioned SH enhancement without dynamic view-dependent computation. It also uses multi-view alpha-guided densification and pruning to improve structural consistency and remove redundant Gaussians.

  • Energy Aggregation vs. Distillation-based SH: Flux-GS replaces Mobile-GS’s teacher-student SH distillation with a Monte Carlo Specular Energy Aggregator that maps third-order radiance energy into a compact low-order subspace.This preserves high-frequency signal without requiring a distillation process.
  • Zero-Cost SH Enhancement: Flux-GS predicts view-independent SH offsets from Gaussian attributes instead of using Mobile-GS’s view-dependent neural enhancement strategy.The module recovers high-frequency details without adding dynamic computation to the rendering pipeline.
  • Multi-view vs. Single-view Consistency: Flux-GS replaces Mobile-GS’s single-view gradient densification and opacity- and scale-based pruning with multi-view alpha-weighted error guidance.This strategy addresses excessive Gaussian primitives, single-view overfitting, and redundant primitive removal.

5 Experiments

Experiments on a Snapdragon 8 Gen 3 smartphone show that Flux-GS reduces storage and training costs while preserving competitive visual quality and mobile rendering efficiency. Ablations and SH decomposition further validate the contributions of its compact representation and component design.

  • Experimental Setup: Experiments use offscreen benchmarking on a commercial Snapdragon 8 Gen 3 smartphone, averaging FPS across consecutive warmed-up runs to reduce display and initialization effects.The protocol removes the 120 FPS screen limit and UI overhead.
  • Quantitative Evaluation and Mobile Performance: Flux-GS is evaluated against 3DGS, Speedy-Splat, C3DGS, LocoGS, and Mobile-GS on Mip-NeRF 360, Tanks and Temples, and Deep Blending.Rendering-efficiency comparisons use a Mobile-GS variant without its inference MLP.
  • Quantitative Evaluation and Mobile Performance: Flux-GS achieves the lowest memory footprint, compresses entire scenes to very low storage costs, and converges several times faster than Speedy-Splat and Mobile-GS.The reduced storage requirement is described as suitable for resource-constrained edge devices.
  • Qualitative Results: Qualitative comparisons show that Flux-GS preserves high-frequency details and sharp structural edges while reducing Gaussian primitives relative to high-fidelity 3DGS and avoiding Speedy-Splat’s blurring.The comparison covers rendering quality, Gaussian number, and storage costs.
  • Ablation Study: The full model provides the best reconstruction-quality and efficiency trade-off, with competitive PSNR, lower storage costs, fewer Gaussian points, and the fastest mobile FPS.The ablation progressively disables MC-SEA, enhanced SH offset ∆c, and Multi-view Alpha-based Densification and Pruning.
  • Analysis of Spherical Harmonic Decomposition: SH decomposition shows that 0th-order SH reconstructs diffuse color and global illumination, whereas 1st-order and ∆1st-order components capture high-frequency structural variation and refine local context.These results validate the enhanced first-order SH representation.

6 Conclusion · A Preliminary

Flux-GS targets real-time rendering on resource-constrained mobile platforms by reducing the memory overhead of high-order Spherical Harmonics. It combines compact radiance-energy representation, zero-overhead SH refinement, and multi-view densification and pruning with the Gaussian Splatting framework.

  • 6 Conclusion: Flux-GS addresses high-order Spherical Harmonics memory overhead for real-time rendering on resource-constrained mobile platforms.The method is presented as a mobile-oriented Gaussian Splatting approach.
  • 6 Conclusion: The Monte Carlo Specular Energy Aggregator compresses radiance energy into a low-order subspace, while Attribute-Conditioned SH Enhancement refines base SH without inference overhead.The conclusion identifies these modules as complementary components of Flux-GS.
  • 6 Conclusion: Multi-view Alpha-based Densification and Pruning eliminates redundant Gaussians as part of Flux-GS’s mobile rendering design.The supplied conclusion passage identifies this strategy as the method’s additional contribution.
  • A Preliminary: A 3D Gaussian represents scene geometry and appearance using anisotropic Gaussian primitives for high-quality novel view synthesis.The representation includes 3D position, covariance, opacity, and color-related parameters.
  • A Preliminary: Rendering projects Gaussians into image space, depth-sorts them near-to-far, and computes pixel colors through alpha blending.The formulation uses accumulated transmittance and Gaussian opacity contributions.
  • A Preliminary: Gaussian Splatting provides a differentiable, efficient, and compact representation for complex scenes suited to real-time rendering.Its rendering process remains compatible with optimization through differentiable image formation.
  • A Preliminary: Spherical Harmonics represent directional appearance with an orthonormal basis whose expansion is truncated at a finite maximum degree L.The truncated representation contains (L + 1)2 coefficients.
  • A Preliminary: Low-order SH, such as L ≤2, are preferred for compactness and numerical stability, whereas higher-order SH capture high-frequency view-dependent effects during training.This tradeoff motivates reducing SH overhead while preserving directional detail.

B Additional Implementation

Flux-GS is trained for 30k iterations without pretraining or distillation, converting third-order SH to first-order SH after an initial 3k-iteration phase. The implementation specifies compact MLP modules, multi-view densification settings, Mobile-GS quantization, and RTX 4090 training.

  • Training: Flux-GS trains for 30k iterations without pretraining or distillation, using third-order SH for the initial 3k iterations before first-order conversion.The Monte Carlo Specular Energy Aggregator performs the conversion from the previous third-order SH representation.
  • Monte Carlo Specular Energy Aggregator: The Monte Carlo aggregator samples K = 2048 uniform points on a unit sphere and is applied once to obtain the low-order SH representation.Two MLPs with one hidden layer of 64 neurons perform the first-order SH mapping.
  • Attribute-Conditioned SH Enhancement: The Attribute-Conditioned SH Enhancement module uses a 4-layer ReLU MLP with hidden neurons (128, 64, 32, 12).The implementation also uses two one-hidden-layer MLPs with 64 neurons for first-order SH mapping.
  • Multi-view Alpha-based Densification and Pruning: The multi-view densification strategy samples 6 cameras, using τ + = 0.1 and τ −= 0.01 as reconstruction-loss thresholds.The importance and pruning quantile is set to Q+ τ = 0.1.
  • Quantization and Training Environment: Flux-GS uses the same quantization method as Mobile-GS and is trained on an RTX 4090 GPU.The authors state that code will be released to foster reproducibility.

C Additional Experiments · C.1 Monte Carlo K Sampling Point Analysis · C.2 Camera Count in Multi-view Alpha-based Densification

The additional experiments show that increasing Monte Carlo sampling points improves PSNR until saturation at K = 2048, while more camera views improve reconstruction quality and reduce Gaussian counts. These results support efficient, high-fidelity multi-view densification and sampling choices.

  • C.1 Monte Carlo K Sampling Point Analysis: PSNR improves monotonically as K increases from 64 to 2048.The experiment evaluates K ∈{64, . . . , 4096}.
  • C.1 Monte Carlo K Sampling Point Analysis: Lower sampling rates, K < 2048, produce sub-optimal integral approximations because Monte Carlo estimator variance remains higher.Increasing sampling density significantly reduces estimator variance.
  • C.2 Camera Count in Multi-view Alpha-based Densification: Increasing input camera views from 2 to 6 yields a significant PSNR improvement in multi-view densification.The ablation varies camera counts from 2 to 12.
  • C.2 Camera Count in Multi-view Alpha-based Densification: As camera count increases, the total Gaussian count drops significantly, indicating improved representation efficiency.The results show a synergistic relationship between viewpoint coverage and representation efficiency.
  • C.1 Monte Carlo K Sampling Point Analysis: K = 2048 marks the saturation point for reconstruction fidelity as Monte Carlo sampling density increases on Mip-NeRF 360.Performance follows an upward trend before saturation.
  • C.2 Camera Count in Multi-view Alpha-based Densification: PSNR steadily improves and stabilizes with additional views while Gaussian primitives decrease significantly.This demonstrates densification that avoids aggressive primitive growth while maintaining high-fidelity reconstruction.

C.3 Densification and Pruning Quantile Analysis

The quantile analysis shows that densification and pruning thresholds control the trade-off between Gaussian count and rendering quality. Larger densification quantiles reduce primitives but worsen results, whereas larger pruning quantiles retain more primitives and improve PSNR; the selected pruning quantile is Q−τ = 0.1.

  • Densification quantile analysis: Larger densification quantiles hinder Gaussian splitting and cloning, producing fewer primitives and worse rendering performance.The analysis evaluates densification quantile Q+τ on the Mip-NeRF 360 dataset.
  • Pruning quantile analysis: Larger pruning quantiles reduce pruning, yielding more primitives and better PSNR results.The analysis evaluates pruning quantile Q−τ on the Mip-NeRF 360 dataset.
  • Pruning quantile analysis: Smaller pruning quantiles prune more aggressively, remove more Gaussians, and consequently harm rendering performance.This reflects the compactness–quality trade-off in the pruning strategy.
  • Pruning quantile analysis: The selected pruning quantile is Q−τ = 0.1, providing a better trade-off between model compactness and rendering quality.The value is chosen based on the quantile analysis.

C.4 Per-Scene Results … D.1 Discussion

Flux-GS maintains competitive rendering quality with reduced training overhead and favorable subjective evaluations, while compressing appearance into first-order SH and improving multi-view efficiency for mobile and cross-platform rendering.

  • C.4 Per-Scene Results: Flux-GS achieves comparable per-scene performance to 3DGS and other lightweight Gaussian Splatting variants on Mip-NeRF 360.The evaluation covers Tables 6–8, while Table 9 compares training time.
  • C.4 Per-Scene Results: Flux-GS uses substantially less training time while retaining competitive rendering performance on Mip-NeRF 360.Training-time comparisons are reported in Table 9.
  • C.5 User Study: Thirty volunteers rated novel-view synthesis videos across Mip-NeRF 360, Tanks & Temples, and Deep Blending in a comparison with Mobile-GS, Speedy-Splat, and quantized 3DGS.The study evaluated subjective rendering quality using videos produced by each method.
  • C.5 User Study: The user-study results indicate a clear preference for Flux-GS in subjective rendering quality.Figure 9 summarizes the comparison against previous advanced methods.
  • D Discussion and Limitations: Flux-GS addresses mobile hardware constraints by compressing appearance representation from third-order to first-order Spherical Harmonics.This reduces the memory-bandwidth and storage overhead associated with traditional 3DGS.
  • D.1 Discussion: The Monte Carlo Specular Energy Aggregator preserves first-order directional moments, energy magnitude, and direction of high-frequency specular residuals in a compact latent space.The approach avoids expensive distillation and heavy pre-training typically used by lightweight methods.
  • D.1 Discussion: Attribute-Conditioned SH Enhancement predicts Gaussian-aware offsets to improve the first-order SH representation without extra inference costs.The mechanism is presented as part of the method’s compact appearance representation.
  • D.1 Discussion: Multi-view Alpha-based Densification and Pruning improves multi-view consistency, mitigates single-view overfitting, and reduces the primitive budget while preserving structural integrity.Stratified camera sampling provides multi-view guidance for scalable mobile and WebGL-based rendering.

D.2 Limitations · D.3 Future Works

Flux-GS remains limited by reduced capacity for highly complex specular reflections and by an initially expensive third-order SH training phase. Future work targets lower storage, low-bandwidth streaming, dynamic-scene support, and temporal redundancy handling on mobile platforms.

  • D.2 Limitations: First-order SH loses some capacity to model highly complex, mirror-like specular reflections that third-order SH can naturally capture.The limitation remains despite preservation of visually salient lighting features through the Monte Carlo Specular Energy Aggregator.
  • D.2 Limitations: Flux-GS optimizes full third-order SH for the initial 3,000 iterations before transitioning to the lower-order representation.Thus, the final model is lightweight, but the initial training phase remains computationally expensive.
  • D.2 Limitations: The final model is remarkably lightweight despite the initial third-order SH optimization phase.This passage contrasts final-model efficiency with the cost incurred during early training.
  • D.3 Future Works: Multi-view guidance in codebook-based compression or entropy coding could further reduce storage requirements.The proposed direction builds on Flux-GS’s existing reduction in primitive count and SH footprint.
  • D.3 Future Works: Further storage reduction could enable instantaneous streaming over low-bandwidth networks.The passage identifies low-bandwidth streaming as a potential benefit of stronger compression.
  • D.3 Future Works: Extending Flux-GS to dynamic scenes, including 4D Gaussian Splatting, is proposed for mobile platforms.The stated goal is to support dynamic-scene rendering on mobile devices.
  • D.3 Future Works: Adapting Multi-view Densification to temporal redundancies could enable real-time mobile playback of volumetric content.The passage connects temporal redundancy handling with future real-time playback capabilities.
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