Locality-aware Gaussian Compression for Fast and High-quality Rendering

TL;DR

LocoGS introduces a locality-aware Gaussian compression framework that significantly reduces storage and accelerates rendering of 3D scenes while maintaining high quality, outperforming existing methods.

Contribution

The paper proposes a novel locality-aware 3D Gaussian representation and a comprehensive compression framework that enhances efficiency and quality in 3D scene rendering.

Findings

Achieves 54.6× to 96.6× compression compared to existing methods.

Provides 2.1× to 2.4× faster rendering speeds.

Outperforms state-of-the-art compression methods in quality and speed.

Abstract

We present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework that exploits the spatial coherence of 3D Gaussians for compact modeling of volumetric scenes. To this end, we first analyze the local coherence of 3D Gaussian attributes, and propose a novel locality-aware 3D Gaussian representation that effectively encodes locally-coherent Gaussian attributes using a neural field representation with a minimal storage requirement. On top of the novel representation, LocoGS is carefully designed with additional components such as dense initialization, an adaptive spherical harmonics bandwidth scheme and different encoding schemes for different Gaussian attributes to maximize compression performance. Experimental results demonstrate that our approach outperforms the rendering quality of existing compact Gaussian representations for representative real-world 3D datasets while achieving from 54.6$\times$ to 96.6$\times$ compressed storage size and from 2.1$\times$ to 2.4$\times$ rendering speed than 3DGS. Even our approach also demonstrates an averaged 2.4$\times$ higher rendering speed than the state-of-the-art compression method with comparable compression performance.