Practical Error Estimation for Denoised Monte Carlo Image Synthesis

We present a practical global error estimation technique for Monte Carlo ray tracing combined with deep learning based denoising. Our method uses aggregated estimates of bias and variance to determine the squared error distribution of the pixels. Unlike unbiased estimates for classical Monte Carlo ray tracing, this distribution follows a noncentral chi-squared distribution, under reasonable assumptions. Based on this, we develop a stopping criterion for denoised Monte Carlo image synthesis that terminates rendering once a user specified error threshold has been achieved. Our results demonstrate that our error estimate and stopping criterion work well on a variety of scenes, and that we are able to achieve a given error threshold without the user specifying the number of samples needed.