SplatStream: Fine Granular Scalable Gaussian Splatting for Adaptive 3D Scene Streaming
arXiv:2607.25971
The paper introduces SplatStream, a framework that breaks down dynamic 3D Gaussian splatting scenes into quality and resolution layers and uses predictive coding and transformer-based prediction to enable adaptive, low-latency streaming of 3D content.
Abstract
Dynamic 3D Gaussian Splatting (GS) enables high quality real-time rendering for immersive media, but its large representation size and frame-wise redundancy create significant challenges for adaptive streaming. This paper presents SplatStream, a fine granular scalable Gaussian splatting framework for dynamic 3D scene delivery. The proposed method decompose the GS scenes into quality and resolution layers, and introduces inter-layer predictive coding to achieve scalability. For temporal direction, B-frames are introduced to have temporal quality scalability. A lightweight cross-layer transformer based predictor is utilized for both cross layer and temporal predictions. In addition, a volume-opacity based importance measure is used for fine-grained Gaussian packetization, allowing visually important primitives to be transmitted earlier for progressive refinement. Finally, the scalable GS bitstream is mapped to an MPEG-DASH compatible sub-representation structure, enabling fine granular adaptive, low-latency delivery of dynamic Gaussian splatting content under bandwidth-varying conditions.
Accepted in Asilomar Conference on Signals, Systems, and Computers 2026