SCAR-GS: Spatial Context Attention for Residuals in Progressive Gaussian Splatting

TL;DR

This paper introduces a novel progressive compression method for 3D Gaussian Splatting models, using a Residual Vector Quantization approach guided by a multi-resolution hash grid to improve rate-distortion performance.

Contribution

It proposes a new auto-regressive entropy model with multi-resolution guidance for more efficient compression of Gaussian Splatting features.

Findings

Enhanced compression efficiency over scalar quantization methods

Improved rate-distortion performance demonstrated

Effective prediction of feature indices using the proposed model

Abstract

Recent advances in 3D Gaussian Splatting have allowed for real-time, high-fidelity novel view synthesis. Nonetheless, these models have significant storage requirements for large and medium-sized scenes, hindering their deployment over cloud and streaming services. Some of the most recent progressive compression techniques for these models rely on progressive masking and scalar quantization techniques to reduce the bitrate of Gaussian attributes using spatial context models. While effective, scalar quantization may not optimally capture the correlations of high-dimensional feature vectors, which can potentially limit the rate-distortion performance. In this work, we introduce a novel progressive codec for 3D Gaussian Splatting that replaces traditional methods with a more powerful Residual Vector Quantization approach to compress the primitive features. Our key contribution is an auto-regressive entropy model, guided by a multi-resolution hash grid, that accurately predicts the conditional probability of each successive transmitted index, allowing for coarse and refinement layers to be compressed with high efficiency.