In view synthesis, a neural radiance field approximates underlying density and radiance fields based on a sparse set of scene pictures. To generate a pixel of a novel view, it marches a ray through the pixel and computes a weighted sum of radiance emitted from a dense set of ray points. This rendering algorithm is fully differentiable and facilitates gradient-based optimization of the fields. However, in practice, only a tiny opaque portion of the ray contributes most of the radiance to the sum. We propose an end-to-end differentiable sampling algorithm based on inverse transform sampling. It generates samples according to the probability distribution induced by the density field and picks non-transparent points on the ray. We utilize the algorithm in two ways. First, we propose a novel rendering approach based on Monte Carlo estimates. Such a rendering algorithm allows for optimizing a neural radiance field with just a few radiance field evaluations per ray. Second, we use the sampling algorithm to modify the hierarchical scheme used in the original work on neural radiance fields. In this setup, we were able to train the proposal network end-to-end without any auxiliary losses and improved the baseline performance.

翻译：在视图合成中，神经辐射场基于稀疏的场景图像集近似底层密度和辐射场。为生成新视图的一个像素，该算法沿光线方向步进，并计算密集光线点集上辐射的加权和。此渲染算法完全可微，便于基于梯度的场优化。然而，实际上光线中仅微小不透明部分贡献了大部分辐射和。我们提出一种基于逆变换采样的端到端可微采样算法，该算法根据密度场诱导的概率分布生成样本，并选取光线上的非透明点。我们以两种方式利用该算法：首先，提出基于蒙特卡洛估计的新型渲染方法，该渲染算法允许每根光线仅需少量辐射场评估即可优化神经辐射场；其次，利用该采样算法改进原始神经辐射场工作中使用的分层方案。在此设置下，我们无需任何辅助损失即可端到端训练提议网络，并提升了基线性能。