Abstract Photon density estimation is a robust solution for estimating complex light transport, such as those involving caustics and pure specular interactions. The shape and bandwidth of the density kernel are both crucial in achieving optimal performance. Recently, density kernels directly predicted by neural networks from local photon statistics have shown improved reconstruction results for small numbers of photons. The direct weight prediction approach of these methods, however, is fundamentally incompatible with consistent estimators as it does not allow for direct control over bias and variance. We address this problem by relying on a simpler yet effective analytical kernel, also inferred by a neural network. Unlike prior work, our technique supports progressive schemes by design, hence unlocking a large variety of applications such as stochastic photon mapping. Our method is fast, trivial to train and demonstrates state‐of‐the‐art caustics reconstruction at equal‐time over other photon mapping techniques.
Benoist et al. (Mon,) studied this question.