Adaptive 3D Gaussian Splatting Video Streaming

TL;DR

This paper presents a novel framework for streaming 3D Gaussian splatting volumetric videos, addressing large data volume and complexity challenges through innovative construction, compression, and adaptive transmission techniques.

Contribution

It introduces a new 3DGS video construction method using Gaussian deformation fields and hybrid saliency tiling for efficient compression and adaptive streaming.

Findings

Superior video quality compared to existing methods

Enhanced compression effectiveness

Improved transmission rate and adaptability

Abstract

The advent of 3D Gaussian splatting (3DGS) has significantly enhanced the quality of volumetric video representation. Meanwhile, in contrast to conventional volumetric video, 3DGS video poses significant challenges for streaming due to its substantially larger data volume and the heightened complexity involved in compression and transmission. To address these issues, we introduce an innovative framework for 3DGS volumetric video streaming. Specifically, we design a 3DGS video construction method based on the Gaussian deformation field. By employing hybrid saliency tiling and differentiated quality modeling of 3DGS video, we achieve efficient data compression and adaptation to bandwidth fluctuations while ensuring high transmission quality. Then we build a complete 3DGS video streaming system and validate the transmission performance. Through experimental evaluation, our method demonstrated superiority over existing approaches in various aspects, including video quality, compression effectiveness, and transmission rate.