  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.
