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.

本文提出 EfficientNeRF，一种高效的基于 NeRF 的方法，用于表示 3D 场景和合成新视图图像。通过在粗略和精细阶段分别提出有效的和关键的采样方法，以显著提高采样效率。同时，设计一种新的数据结构来加速整个场景的缓存以加速渲染速度。结果表明，本方法可以缩短 88% 以上的训练时间，实现超过 200 FPS 的渲染速度，而仍保持竞争力的准确性，从而促进 NeRF 在现实世界中的实际应用和推广。