Voxel-GS: Quantized Scaffold Gaussian Splatting Compression with Run-Length Coding

TL;DR

Voxel-GS introduces a lightweight, effective compression framework for Gaussian splatting point clouds using quantization, a Laplacian rate proxy, and run-length coding, achieving high compression ratios and faster speeds.

Contribution

It proposes a simple, neural entropy model-free compression method for Gaussian splatting point clouds using differentiable quantization and run-length coding.

Findings

Achieves high compression ratios with faster coding speeds.

Accurately estimates bitrate using a Laplacian-based rate proxy.

Outperforms prior methods in compression efficiency.

Abstract

Substantial Gaussian splatting format point clouds require effective compression. In this paper, we propose Voxel-GS, a simple yet highly effective framework that departs from the complex neural entropy models of prior work, instead achieving competitive performance using only a lightweight rate proxy and run-length coding. Specifically, we employ a differentiable quantization to discretize the Gaussian attributes of Scaffold-GS. Subsequently, a Laplacian-based rate proxy is devised to impose an entropy constraint, guiding the generation of high-fidelity and compact reconstructions. Finally, this integer-type Gaussian point cloud is compressed losslessly using Octree and run-length coding. Experiments validate that the proposed rate proxy accurately estimates the bitrate of run-length coding, enabling Voxel-GS to eliminate redundancy and optimize for a more compact representation. Consequently, our method achieves a remarkable compression ratio with significantly faster coding speeds than prior art. The code is available at https://github.com/zb12138/VoxelGS.