We have recently seen tremendous progress in neural rendering (NR) advances, i.e., NeRF, for photo-real free-view synthesis. Yet, as a local technique based on a single computer/GPU, even the best-engineered Instant-NGP or i-NGP cannot reach real-time performance when rendering at a high resolution, and often requires huge local computing resources. In this paper, we resort to cloud rendering and present NEPHELE, a neural platform for highly realistic cloud radiance rendering. In stark contrast with existing NR approaches, our NEPHELE allows for more powerful rendering capabilities by combining multiple remote GPUs and facilitates collaboration by allowing multiple people to view the same NeRF scene simultaneously. We introduce i-NOLF to employ opacity light fields for ultra-fast neural radiance rendering in a one-query-per-ray manner. We further resemble the Lumigraph with geometry proxies for fast ray querying and subsequently employ a small MLP to model the local opacity lumishperes for high-quality rendering. We also adopt Perfect Spatial Hashing in i-NOLF to enhance cache coherence. As a result, our i-NOLF achieves an order of magnitude performance gain in terms of efficiency than i-NGP, especially for the multi-user multi-viewpoint setting under cloud rendering scenarios. We further tailor a task scheduler accompanied by our i-NOLF representation and demonstrate the advance of our methodological design through a comprehensive cloud platform, consisting of a series of cooperated modules, i.e., render farms, task assigner, frame composer, and detailed streaming strategies. Using such a cloud platform compatible with neural rendering, we further showcase the capabilities of our cloud radiance rendering through a series of applications, ranging from cloud VR/AR rendering.

翻译：近期，神经渲染（NR）技术取得显著进展，例如NeRF（神经辐射场）在照片级自由视点合成中的应用。然而，作为基于单台计算机/GPU的局部技术，即使经过最佳优化的Instant-NGP或i-NGP在高分辨率渲染时也无法达到实时性能，且通常需要庞大的本地计算资源。本文采用云渲染方案，提出NEPHELE——一种用于高真实感云辐射渲染的神经平台。与现有NR方法截然不同，我们的NEPHELE通过组合多个远程GPU实现更强的渲染能力，并允许多用户同时观看同一NeRF场景以促进协作。我们引入i-NOLF，采用不透明度光场以“每射线单次查询”模式实现超快速神经辐射渲染。进一步借鉴Lumigraph结合几何代理实现快速射线查询，并利用小型MLP（多层感知机）建模局部不透明度光球以实现高质量渲染。同时，我们在i-NOLF中采用完美空间哈希（Perfect Spatial Hashing）增强缓存一致性。实验表明，我们的i-NOLF在效率上较i-NGP提升一个数量级，尤其在云渲染场景下的多用户多视点设置中表现突出。针对i-NOLF表示，我们专门设计了任务调度器，并通过包含渲染农场、任务分配器、帧合成器及详细流传输策略等一系列协同模块的综合云平台，展示方法设计的先进性。借助这一兼容神经渲染的云平台，我们还通过从云VR/AR渲染的一系列应用，进一步展示云辐射渲染的能力。