Learning Patterns in Sample Distributions for Monte Carlo Variance Reduction
This work addresses variance reduction for Monte Carlo rendering, offering an incremental improvement that is compatible with existing denoising methods.
The paper tackles the problem of Monte Carlo variance reduction in image synthesis by proposing a novel a-posteriori approach that analyzes per-pixel sample distributions to train a neural network model, resulting in numerically better estimates compared to simple averaging.
This paper investigates a novel a-posteriori variance reduction approach in Monte Carlo image synthesis. Unlike most established methods based on lateral filtering in the image space, our proposition is to produce the best possible estimate for each pixel separately, from all the samples drawn for it. To enable this, we systematically study the per-pixel sample distributions for diverse scene configurations. Noting that these are too complex to be characterized by standard statistical distributions (e.g. Gaussians), we identify patterns recurring in them and exploit those for training a variance-reduction model based on neural nets. In result, we obtain numerically better estimates compared to simple averaging of samples. This method is compatible with existing image-space denoising methods, as the improved estimates of our model can be used for further processing. We conclude by discussing how the proposed model could in future be extended for fully progressive rendering with constant memory footprint and scene-sensitive output.