3DGStreaming: Spatial-Heterogeneity-Aware 3-D Gaussian Splatting Compression and Streaming

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

Abstract: 3-D Gaussian splatting (3DGS) has emerged as a promising technique for high-quality 3-D scene representation. However, streaming 3DGS scenes poses significant challenges due to large data volumes and complex spatial structures, resulting in nonsmooth scene loading, inferior visual quality, and ineffective streaming adaptation, ultimately impacting user experience adversely. To tackle these challenges and enhance user Quality of Experience (QoE), this article introduces a novel adaptive streaming framework called 3DGStreaming. Our framework comprises three key components: 1) smart Spatial Partitioning for efficient scene division, enabling selective streaming and seamless scene merging; 2) two-step Progressive Scene Generation, involving content-aware downsampling and attribute compression to create multibitrate 3DGS scenes; and 3) Field of View (FoV)-based Bitrate Adaptation using a decision transformer for viewport-based bitrate selection. Extensive experiments demonstrate the superiority of 3DGStreaming over existing state-of-the-art solutions. 3DGStreaming achieves a greater rendering quality with a 5.7%–25.5% increase in PSNR, a 27.8%–69.2% reduction in latency, a 54.7% reduction in training time, and a 17.2%–68.3% improvement in overall QoE.

External IDs:doi:10.1109/jiot.2025.3590142