# Scale-GS: Efficient Scalable Gaussian Splatting via Redundancy-filtering Training on Streaming Content

## TL;DR

This paper introduces Scale-GS, a scalable Gaussian Splatting framework that improves training efficiency and rendering quality for dynamic scenes by hierarchical organization, selective activation, motion modeling, and adaptive masking.

## Contribution

It proposes a hierarchical, anchor-based organization of Gaussians, a hybrid motion modeling strategy, and an adaptive masking mechanism to enhance efficiency in streaming 3D Gaussian Splatting.

## Key findings

- Achieves higher visual quality with less training time.
- Reduces computational overhead significantly.
- Effective in dynamic scene rendering.

## Abstract

3D Gaussian Splatting (3DGS) enables high-fidelity real-time rendering, a key requirement for immersive applications. However, the extension of 3DGS to dynamic scenes remains limitations on the substantial data volume of dense Gaussians and the prolonged training time required for each frame. This paper presents \M, a scalable Gaussian Splatting framework designed for efficient training in streaming tasks. Specifically, Gaussian spheres are hierarchically organized by scale within an anchor-based structure. Coarser-level Gaussians represent the low-resolution structure of the scene, while finer-level Gaussians, responsible for detailed high-fidelity rendering, are selectively activated by the coarser-level Gaussians. To further reduce computational overhead, we introduce a hybrid deformation and spawning strategy that models motion of inter-frame through Gaussian deformation and triggers Gaussian spawning to characterize wide-range motion. Additionally, a bidirectional adaptive masking mechanism enhances training efficiency by removing static regions and prioritizing informative viewpoints. Extensive experiments demonstrate that \M~ achieves superior visual quality while significantly reducing training time compared to state-of-the-art methods.