解耦边缘物理算法用于协作式扩展现实仿真
Decoupled Edge Physics algorithms for collaborative XR simulations
AI总结：
提出解耦物理引擎的算法，采用边缘云N-1架构，提升XR协作仿真的帧率与体验质量，支持超100并发用户。验证表明技术可行且性能无损。
摘要：
这项工作提出了一种新颖的方法，用于改造任何现代游戏引擎管线，以在扩展现实（XR）环境中实现优化性能和增强用户体验。将物理引擎与游戏引擎管线解耦，并采用客户端-服务器N-1架构，创建了一个可扩展的解决方案，通过边缘云基础设施上的单一物理引擎，高效地为头戴式显示器（HMD）上的多个图形客户端提供服务。这种方法确保了多人场景中更好的同步性，同时不会在单人体验中引入额外开销，并在用户参与变化时保持会话连续性。将物理引擎迁移到边缘或云节点减少了对本地硬件的压力，将更多资源用于高质量渲染，并释放了无绳HMD的全部潜力。我们提出了四种解耦物理引擎的算法，提高了VR仿真中的帧率和体验质量（QoE），支持高级交互、大量物理对象以及超过100个并发用户的多用户会话。结合几何代数插值器，减少了被解剖部分之间的内部调用，保持了QoE并减轻了网络压力。实验验证，在超过100个并发用户、10,000个物理对象和软体仿真的条件下，确认了所提出架构的技术可行性，展示了在不影响性能的情况下，为更具沉浸感和协作性的XR应用带来的变革能力。
英文摘要：
This work proposes a novel approach to transform any modern game engine pipeline, for optimized performance and enhanced user experiences in Extended Reality (XR) environments. Decoupling the physics engine from the game engine pipeline and using a client-server N-1 architecture creates a scalable solution, efficiently serving multiple graphics clients on Head-Mounted Displays (HMDs) with a single physics engine on edge-cloud infrastructure. This approach ensures better synchronization in multiplayer scenarios without introducing overhead in single-player experiences, maintaining session continuity despite changes in user participation. Relocating the Physics Engine to an edge or cloud node reduces strain on local hardware, dedicating more resources to high-quality rendering and unlocking the full potential of untethered HMDs. We present four algorithms that decouple the physics engine, increasing frame rates and Quality of Experience (QoE) in VR simulations, supporting advanced interactions, numerous physics objects, and multi-user sessions with over 100 concurrent users. Incorporating a Geometric Algebra interpolator reduces inter-calls between dissected parts, maintaining QoE and easing network stress. Experimental validation, with more than 100 concurrent users, 10,000 physics objects, and softbody simulations, confirms the technical viability of the proposed architecture, showcasing transformative capabilities for more immersive and collaborative XR applications without compromising performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
