Neural Radiance Fields (NeRF) is an emerging technique to synthesize 3D
objects from 2D images with a wide range of potential applications. However,
rendering existing NeRF models is extremely computation intensive, making it
challenging to support real-time interaction on mobile devices. In this paper,
we take the first initiative to examine the state-of-the-art real-time NeRF
rendering technique from a system perspective. We first define the entire
working pipeline of the NeRF serving system. We then identify possible control
knobs that are critical to the system from the communication, computation, and
visual performance perspective. Furthermore, an extensive measurement study is
conducted to reveal the effects of these control knobs on system performance.
Our measurement results reveal that different control knobs contribute
differently towards improving the system performance, with the mesh granularity
being the most effective knob and the quantization being the least effective
knob. In addition, diverse hardware device settings and network conditions have
to be considered to fully unleash the benefit of operating under the
appropriate knobs

Neural Radiance Fields (NeRF) is a real-time rendering technique for synthesizing 3D objects from 2D images, with a focus on examining the system perspective and identifying critical control knobs for improving performance on mobile devices.