Problem
Research questions and friction points this paper is trying to address.
3D Gaussian Splatting
mobile devices
real-time rendering
static-dynamic scenes
resource-constrained
Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting
🤖 AI Summary
This study addresses the challenges of storage redundancy, high computational overhead, and real-time rendering bottlenecks encountered when deploying 3D Gaussian Splatting on mobile devices by proposing a unified lightweight framework. Methodologically, it introduces a Monte Carlo specular energy aggregator and a multi-view alpha pruning strategy to achieve efficient compression. Furthermore, a compact explicit 4D representation is constructed, integrating spherical harmonics enhancement, second-order Gaussian motion modeling, and depth-order certificate reuse to support the unified processing of both static and dynamic scenes. This framework substantially reduces storage and computational costs while preserving high-fidelity visual quality, thereby enabling real-time rendering on mobile platforms.
📝 Abstract
Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in novel view synthesis, yet deploying both static and dynamic Gaussian representations on resource-constrained mobile devices remains challenging due to heavy storage, redundant primitives, and costly per-frame computation. We present Mobile-4DGS, a unified lightweight framework for high-fidelity real-time static and dynamic Gaussian rendering on mobile platforms. For compact appearance modeling, we introduce a Monte Carlo Specular Energy Aggregator that compresses high-order radiance residuals into the first-order Spherical Harmonics (SH), together with an Attribute-Conditioned SH Enhancement module whose predicted offsets are pre-baked before inference. We further propose a Multi-View Alpha-Based Densification and Pruning strategy to suppress redundant primitives while maintaining multi-view consistency. For dynamic scenes, we develop a compact explicit 4D representation by constructing second-order Gaussian motion, learnable temporal support, and a binary static-dynamic partition, enabling continuous-time modeling without runtime deformation networks. Based on this partition, a Depth-Order Certificate selectively reuses previously committed depth orders to reduce re-projection, sorting, merging, and index-buffer updates during playback. Extensive experiments on static and dynamic scenes demonstrate that Mobile-4DGS substantially reduces storage and rendering overhead while maintaining competitive visual quality, enabling real-time 3D and 4D Gaussian Splatting on mobile devices. \textcolor{magenta}{\href{https://xiaobiaodu.github.io/mobile-4dgs-project/}{Code has been released: https://xiaobiaodu.github.io/mobile-4dgs-project/}}.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Mobile 4D Gaussian Splatting
Spherical Harmonics Compression
Multi-View Pruning
Explicit 4D Representation
Depth-Order Certificate
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