DELUGE：用于实时粒子流传输的分解熵编码实时非结构化几何交换
DELUGE: Decomposed Entropy-coded Live Unstructured Geometry Exchange for Real-time Particle Streaming
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中文总结 AI 辅助
针对动态粒子流实时传输中现有压缩方法延迟过高的问题，提出DELUGE架构，利用时间相干性和速度可预测性实现亚帧延迟编解码，解码比G-PCC快20倍，编码比Draco快6倍，并验证了实时协作体验。
中文摘要 AI 辅助
基于粒子的物理模拟，包括流体、烟雾和颗粒介质，是沉浸式VR和AR中视觉真实感的基础。随着社交VR和数字孪生的日益普及，对多用户实时交互同一模拟的共享体验的需求正在增加。实现此类体验需要从服务器向每个客户端低延迟流式传输大规模粒子数据，然而现有的点云压缩方法如G-PCC（TMC13）和Draco假设静态几何结构；当应用于动态粒子流传输时，其编码延迟超过帧周期，无法满足实时传输要求。我们提出DELUGE，一种流式压缩架构，利用物理模拟粒子固有的时间相干性和速度可预测性，通过三种互补技术实现亚帧延迟编码和解码。在动态点云数据集上的评估表明，DELUGE的解码速度比G-PCC（TMC13）快约20倍，编码速度比Draco快6倍。我们进一步为Web浏览器和Apple Vision Pro构建了端到端客户端实现，并通过受试者内感知质量评估和Vision Pro上的双人协作任务研究确认，所提方法支持具有手部追踪流体交互的实时协作体验。
英文摘要
Particle-based physics simulations, including fluids, smoke, and granular media, are fundamental to visual realism in immersive VR and AR. With the growing adoption of social VR and digital twins, demand is increasing for shared experiences in which multiple users interact with the same simulation in real time. Realizing such experiences requires low-latency streaming of large-scale particle data from a server to each client, yet existing point cloud compression methods such as G-PCC (TMC13) and Draco assume static geometric structures; when applied to dynamic particle streaming, their encoding latency exceeds the frame period, failing to meet real-time delivery requirements. We propose DELUGE, a streaming compression architecture that exploits the temporal coherence and velocity predictability inherent in physics simulation particles, achieving sub-frame-latency encoding and decoding through three complementary techniques. Evaluation on dynamic point cloud datasets demonstrates that DELUGE achieves approximately $20\times$ faster decoding than G-PCC (TMC13) and $6\times$ faster encoding than Draco. We further build end-to-end client implementations for both web browsers and Apple Vision Pro, and confirm through a within-participants perceptual quality evaluation and a two-person collaborative task study on Vision Pro that the proposed method supports real-time collaborative experiences with hand-tracked fluid interaction.
发表机构
- Cluster Metaverse Lab（集群元宇宙实验室）
- University of Tsukuba（筑波大学）
机构由 AI 辅助整理，请以论文原文为准。
