ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model

TL;DR

This paper introduces a novel anchor-level autoregressive context model for 3D Gaussian Splatting compression, significantly reducing data size while maintaining high rendering quality.

Contribution

It pioneers the use of an autoregressive context model at the anchor level for 3D Gaussian Splatting compression, improving efficiency and size reduction.

Findings

Over 100x size reduction compared to vanilla 3DGS

15x size reduction compared to Scaffold-GS

Maintains or improves rendering quality

Abstract

Recently, 3D Gaussian Splatting (3DGS) has become a promising framework for novel view synthesis, offering fast rendering speeds and high fidelity. However, the large number of Gaussians and their associated attributes require effective compression techniques. Existing methods primarily compress neural Gaussians individually and independently, i.e., coding all the neural Gaussians at the same time, with little design for their interactions and spatial dependence. Inspired by the effectiveness of the context model in image compression, we propose the first autoregressive model at the anchor level for 3DGS compression in this work. We divide anchors into different levels and the anchors that are not coded yet can be predicted based on the already coded ones in all the coarser levels, leading to more accurate modeling and higher coding efficiency. To further improve the efficiency of entropy coding, e.g., to code the coarsest level with no already coded anchors, we propose to introduce a low-dimensional quantized feature as the hyperprior for each anchor, which can be effectively compressed. Our work pioneers the context model in the anchor level for 3DGS representation, yielding an impressive size reduction of over 100 times compared to vanilla 3DGS and 15 times compared to the most recent state-of-the-art work Scaffold-GS, while achieving comparable or even higher rendering quality.