Efficient Cloud Pipelines for Neural Radiance Fields
Derek Jacoby Donglin Xu Weder Ribas Minyi Xu Ting Liu Vishwanath Jayaraman Mengdi Wei Emma De Blois Yvonne Coady
Since their introduction in 2020, Neural Radiance Fields (NeRFs) have taken
the computer vision community by storm. They provide a multi-view
representation of a scene or object that is ideal for eXtended Reality (XR)
applications and for creative endeavors such as virtual production, as well as
change detection operations in geospatial analytics. The computational cost of
these generative AI models is quite high, however, and the construction of
cloud pipelines to generate NeRFs is neccesary to realize their potential in
client applications. In this paper, we present pipelines on a high performance
academic computing cluster and compare it with a pipeline implemented on
Microsoft Azure. Along the way, we describe some uses of NeRFs in enabling
novel user interaction scenarios.