  Neural Radiance Fields (NeRF) have demonstrated superior novel view synthesis
performance but are slow at rendering. To speed up the volume rendering
process, many acceleration methods have been proposed at the cost of large
memory consumption. To push the frontier of the efficiency-memory trade-off, we
explore a new perspective to accelerate NeRF rendering, leveraging a key fact
that the viewpoint change is usually smooth and continuous in interactive
viewpoint control. This allows us to leverage the information of preceding
viewpoints to reduce the number of rendered pixels as well as the number of
sampled points along the ray of the remaining pixels. In our pipeline, a
low-resolution feature map is rendered first by volume rendering, then a
lightweight 2D neural renderer is applied to generate the output image at
target resolution leveraging the features of preceding and current frames. We
show that the proposed method can achieve competitive rendering quality while
reducing the rendering time with little memory overhead, enabling 30FPS at
1080P image resolution with a low memory footprint.
