移動端3D高斯潑濺的蒙特卡洛能量聚合

摘要

3D高斯散射（3D Gaussian Splatting）的最新進展在新視角合成方面取得了前所未有的成功。然而，高階球諧函數（Spherical Harmonics, SH）所帶來的巨大推理和儲存開銷是行動平台的主要瓶頸。在本文中，我們提出Flux-GS，這是一種即時高斯散射方法，旨在為資源受限的行動平台實現高保真渲染，同時大幅降低開銷。我們首先提出蒙地卡羅鏡面能量聚合器（Monte Carlo Specular Energy Aggregator），透過採樣三階輻射殘差並將鏡面能量聚合至緊湊的潛在空間。透過這種方式，我們的方法能夠有效保留低階頻段中視覺顯著的照明特徵，無需昂貴的蒸餾或預訓練。為了解決壓縮過程中高頻細節的遺失，我們引入屬性條件化SH增強模組（Attribute-Conditioned SH Enhancement）。該模組基於高斯本質屬性預測高感知偏移量，從而在推理前增強一階SH表示，且不增加額外推理成本。此外，原始的單視圖梯度基緻密化方法容易產生過多高斯並過擬合特定視角。我們透過提出基於多視圖Alpha的緻密化與剪枝策略來解決這些限制。透過利用多視圖引導，我們確保多視圖結構一致性並精確移除冗餘基元。廣泛的實驗證明，Flux-GS在保持競爭力視覺品質的同時實現了顯著的參數減少，為即時行動渲染提供了穩健且可擴展的解決方案。程式碼：\href{https://xiaobiaodu.github.io/flux-gs-project/}{https://xiaobiaodu.github.io/flux-gs-project/}。

English

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: magenta{https://xiaobiaodu.github.io/flux-gs-project/{https://xiaobiaodu.github.io/flux-gs-project/}}.