Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting
cs.CV
Submitted: 2026-10-04
Updated: 2026-10-10
Comments: Code has been released: https://xiaobiaodu.github.io/mobile-4dgs-project/
Project page: https://xiaobiaodu.github.io/mobile-4dgs-project
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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
Terminology
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. Code has been released: https://xiaobiaodu.github.io/mobile-4dgs-project/.
Sources
- Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
- DreamCar: Leveraging Car-specific Prior for in-the-wild 3D Car Reconstruction
- 3DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree Views
- VTNet: Visual Transformer Network for Object Goal Navigation
- Wonder3D: Single Image to 3D using Cross-Domain Diffusion
- One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
- Anything-3D: Towards Single-view Anything Reconstruction in the Wild
- Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
- TensoRF: Tensorial Radiance Fields
- NeRF++: Analyzing and Improving Neural Radiance Fields
- Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians
- 3D Gaussian Splatting as Markov Chain Monte Carlo
- MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis
- 3D Gaussian Splatting with Deferred Reflection
- Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splatting
- PCGS: Progressive Compression of 3D Gaussian Splatting
- Optimized Minimal 3D Gaussian Splatting
- HAC++: Towards 100X Compression of 3D Gaussian Splatting
- Locality-aware Gaussian Compression for Fast and High-quality Rendering
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