NEPHELE: A Neural Platform for Highly Realistic Cloud Radiance Rendering


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. Such a combination of NeRF and cloud rendering naturally requires a lightweight, real-time neural renderer with flexible scalability. To this end, analogous to i-NGP, 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 replace the brute-force multi-hashing in the original i-NGP, so as 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, with a ray-level scheduling design to maintain the resiliency of rendering jobs. We also 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, to sharing NeRF assets between multiple users and allowing NeRF assets to freely assemble into a new scene.

arXiv 2023
Haimin Luo
Haimin Luo
PhD Candidate

My research interests include Neural Modeling and Rendering, 3D Reconstruction, Motion Capture and Animation.