手机也能跑3D高斯渲染了
3D高斯泼溅（3D Gaussian Splatting）是目前最逼真的新视角合成技术，但它的计算和存储开销巨大，尤其是高阶球谐函数（SH）部分，导致手机根本跑不动。这篇论文的Flux-GS方法用蒙特卡洛采样把高阶光照信息压缩进一个紧凑的隐空间，只保留低阶部分，再通过一个属性条件增强模块补回高频细节——这个模块不增加推理成本。此外，他们用多视角信息来指导高斯点的增删，避免生成过多冗余点。结果是在保持画质的前提下，参数大幅减少，手机端能实时渲染。它不是你明天就能用的App，但让手机跑3D高斯渲染从不可能变成了可能。
📄 原文摘要(英文)
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/}}.