AI 中文总结
针对动态3D高斯点云绘制在自适应流方面的挑战，提出SplatStream框架，通过分解场景为质量和分辨率层、引入层间及时间预测编码、基于体不透明度度量分组等方法，实现细粒度自适应低延迟传输。
AI 中文摘要
动态3D高斯点云绘制（GS）为沉浸式媒体实现了高质量实时渲染，但其较大的表示尺寸和逐帧冗余给自适应流带来重大挑战。本文提出SplatStream，一种用于动态3D场景传输的细粒度可扩展高斯点云绘制框架。该方法将GS场景分解为质量和分辨率层，并引入层间预测编码实现可扩展性。在时间方向上，引入B帧实现时间质量可扩展性。利用基于轻量级跨层变换器的预测器进行跨层和时间预测。此外，基于体不透明度的重要性度量用于细粒度高斯分组，使视觉上重要的图元能更早传输以进行渐进细化。最后，将可扩展GS比特流映射到MPEG-DASH兼容的子表示结构，在带宽变化条件下实现动态高斯点云绘制内容的细粒度自适应、低延迟传输。
英文摘要
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.
CommentsAccepted in Asilomar Conference on Signals, Systems, and Computers 2026
