Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting

Recent advances in 3D Gaussian Splatting have demonstratedunprecedented success in novel view synthesis. However, the substantialinference and storage overhead driven by high-order Spherical Harmon-ics (SH) are primary bottlenecks for mobile platforms. In this paper,we present Flux-GS, a real-time Gaussian Splatting method designedto achieve high-fidelity rendering with significantly reduced overhead forresource-constrained mobile platforms. We first propose a Monte CarloSpecular Energy Aggregator, sampling third-order radiance residualsand aggregating specular energy into a compact latent space. In thisway, our method effectively preserves visually salient lighting features inlower-order bands without expensive distillation or pre-training. To miti-gate the high-frequency details lost during compression, we introduce anAttribute-Conditioned SH Enhancement module. This module predictsGaussian-aware offsets based on intrinsic Gaussian attributes, which en-hance the first-order SH representation prior to inference, without extrainference costs. Furthermore, the original single-view gradient-based den-sification is prone to producing excessive Gaussians and overfitting to aa. Rotation Opacity Opacity Opacity b.Scale 7% 2% 4% 6%5%PositionRotation SH5%Rotation 35%18%23% 3DGS Third-orderScaleSH SH Rest52%13%SH ScalePosition Position81% 18%13% 18%61%↓ 26%↓ First-orderThird-order SH First-order SH Flux-GS Ground Truth Flux-GS SH RestFig. 2: Gaussian parameter distribution and Spherical Harmonic fidelityanalysis. a. Per Gaussian memory footprint across 3DGS variants. Flux-GS achievessignificant compression (61% and 26% reductions) by optimizing Spherical Harmonics(SH) coefficients and decoupling SH into the base and view-independent components.b. Qualitative comparison demonstrates that Flux-GS with only first-order SH canrender high-fidelity high-frequency details comparable to 3DGS.certain view. We address these limitations by proposing a Multi-viewAlpha-based Densification and Pruning strategy. By leveraging multi-view guidance, we ensure multi-view structure consistency and the pre-cise removal of redundant primitives. Extensive experiments demonstratethat Flux-GS achieves substantial parameter reduction while maintain-ing competitive visual quality, offering a robust and scalable solution forreal-time mobile rendering. Code: https://xiaobiaodu.github.io/flux-gs-project/.