  Neural Radiance Fields (NeRF) has been wildly applied to various tasks for
its high-quality representation of 3D scenes. It takes long per-scene training
time and per-image testing time. In this paper, we present EfficientNeRF as an
efficient NeRF-based method to represent 3D scene and synthesize novel-view
images. Although several ways exist to accelerate the training or testing
process, it is still difficult to much reduce time for both phases
simultaneously. We analyze the density and weight distribution of the sampled
points then propose valid and pivotal sampling at the coarse and fine stage,
respectively, to significantly improve sampling efficiency. In addition, we
design a novel data structure to cache the whole scene during testing to
accelerate the rendering speed. Overall, our method can reduce over 88\% of
training time, reach rendering speed of over 200 FPS, while still achieving
competitive accuracy. Experiments prove that our method promotes the
practicality of NeRF in the real world and enables many applications.
