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 a simple 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. This approach allows for evaluating and optimizing a neural radiance field with just a few radiance field calls per ray. Second, we use the sampling algorithm to modify the hierarchical scheme proposed in the original NeRF work. We show that our modification improves reconstruction quality of hierarchical models, at the same time simplifying the training procedure by removing the need for auxiliary proposal network losses.

翻译：在视图合成中，神经辐射场根据稀疏的场景图片集近似底层密度场和辐射场。为生成新视角的像素，它沿光线行进并计算密集光线点集所发出辐射的加权和。该渲染算法完全可微，便于基于梯度的场优化。然而，实际中仅光线上一小段不透明部分贡献了和中的绝大部分辐射。我们提出一种基于逆变换采样的简单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。我们从两方面运用该算法：首先，提出一种基于蒙特卡洛估计的新型渲染方法，该方法允许每条光线仅需少量辐射场调用即可评估和优化神经辐射场；其次，利用该采样算法改进原始NeRF工作中提出的分层方案。我们证明，该改进在提升分层模型重建质量的同时，通过消除辅助提议网络损失简化了训练流程。