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arXiv · 2607.25971

SplatStream: Fine Granular Scalable Gaussian Splatting for Adaptive 3D Scene Streaming

Muhammad Talha·William Gordon·Sajid Umair·Zhu Li·Anique Akhtar·Joel Jung
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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.

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Muhammad Talha, William Gordon, Sajid Umair, Zhu Li, Anique Akhtar, Joel Jung. 2026-07-28. SplatStream: Fine Granular Scalable Gaussian Splatting for Adaptive 3D Scene Streaming. https://arxiv.org/abs/2607.25971

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