A Novel Benchmark and Dataset for Efficient 3D Gaussian Splatting with Gaussian Point Cloud Compression

TL;DR

This paper introduces a new benchmark and dataset for efficient 3D Gaussian Splatting, leveraging AI-based point cloud compression techniques to improve storage and performance in immersive media and autonomous driving applications.

Contribution

It pioneers the integration of AI-based point cloud compression into Gaussian Splatting, proposing GausPcgc and a new dataset for enhanced Gaussian point cloud compression.

Findings

AI-based compression outperforms MPEG G-PCC in speed and efficiency

GausPcgc achieves higher compression ratios for Gaussian point clouds

The framework improves storage without sacrificing rendering quality

Abstract

Recently, immersive media and autonomous driving applications have significantly advanced through 3D Gaussian Splatting (3DGS), which offers high-fidelity rendering and computational efficiency. Despite these advantages, 3DGS as a display-oriented representation requires substantial storage due to its numerous Gaussian attributes. Current compression methods have shown promising results but typically neglect the compression of Gaussian spatial positions, creating unnecessary bitstream overhead. We conceptualize Gaussian primitives as point clouds and propose leveraging point cloud compression techniques for more effective storage. AI-based point cloud compression demonstrates superior performance and faster inference compared to MPEG Geometry-based Point Cloud Compression (G-PCC). However, direct application of existing models to Gaussian compression may yield suboptimal results, as Gaussian point clouds tend to exhibit globally sparse yet locally dense geometric distributions that differ from conventional point cloud characteristics. To address these challenges, we introduce GausPcgc for Gaussian point cloud geometry compression along with a specialized training dataset GausPcc-1K. Our work pioneers the integration of AI-based point cloud compression into Gaussian compression pipelines, achieving superior compression ratios. The framework complements existing Gaussian compression methods while delivering significant performance improvements. All code, data, and pre-trained models will be publicly released to facilitate further research advances in this field.