SRBF-Gaussian: Streaming-Optimized 3D Gaussian Splatting

Dayou Zhang, Zhicheng Liang, Zijian Cao, Dan Wang, Fangxin Wang

Abstract:

3D Gaussian Splatting (3DGS) has emerged as a groundbreaking 3D scene representation technique, offering unprecedented visual quality and rendering efficiency. However, the substantial data volume of 3DGS scenes poses significant challenges for streaming applications. Existing research on 3DGS has primarily focused on compression and rendering efficiency, neglecting the specific requirements of streaming transmission. Moreover, the Spherical Harmonics color representation in 3DGS complicates viewport-based transmission partitioning. Achieving hierarchical Gaussian streaming without noticeable quality degradation also remains a significant challenge.

To address these challenges, we propose SRBF-Gaussian, a new paradigm that revolutionizes the traditional 3DGS format. Our approach introduces viewport-dependent color encoding based on Spherical Radial Basis Functions (SRBFs) and HSL color space, enabling selective transmission of viewport-relevant color data. This reduces data transmission while maintaining visual quality. We implement adaptive Gaussian pruning and transmission, optimized for current viewports and network conditions. Additionally, we develop coherent multi-level Gaussian representations for smooth transitions between quality levels. Our system incorporates user-behavior-aware streaming strategies to anticipate and pre-fetch relevant data. In cloud VR scenarios, our approach demonstrates substantial improvements, achieving a 5.63\% - 14.17\% increase in PSNR, a 7.61\% - 59.16\% reduction in latency, and a 10.45\% - 30.12\% improvement in overall Quality of Experience (QoE).