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.

翻译：神经辐射场( NERF) 被疯狂地应用于各种任务, 用于高质量地展示 3D 场景。 它需要长时间的每层训练时间和每个图像测试时间。 在本文中, 我们将高效的NERF 作为一种高效的 NERF 方法, 代表 3D 场景并合成新视觉图像。 虽然有几种方法可以加速培训或测试过程, 但同时同时大量缩短两个阶段的时间仍然困难。 我们分析取样点的密度和重量分布, 然后在粗糙和精细的阶段分别提出有效而关键的抽样, 以大幅提高取样效率。 此外, 我们设计了一个新的数据结构, 在测试期间将整个场景隐藏起来, 以加速转换速度 。 总体而言, 我们的方法可以减少超过 88 % 的培训时间, 达到 超过 200 FPS 的速度, 同时仍然达到竞争性的精确度 。 实验证明我们的方法可以促进 NRFFS 在现实世界中的实用性, 并且能够让许多应用 。