Denoising-Aware Adaptive Sampling for Monte Carlo Ray Tracing

Monte Carlo rendering is a computationally intensive task, but combined with recent deep-learning based advances in image denoising it is possible to achieve high quality images in a shorter amount of time. We present a novel adaptive sampling technique that further improves the efficiency of Monte Carlo rendering combined with deep-learning based denoising. Our proposed technique is general, can be combined with existing pre-trained denoisers, and, in contrast with previous techniques, does not itself require any additional neural networks or learning. A key contribution of our work is a general method for estimating the variance of the outputs of a neural network whose inputs are random variables. Our method iteratively renders additional samples and uses this novel variance estimate to compute the sample distribution for each subsequent iteration. Compared to uniform sampling and previous adaptive sampling techniques, our method achieves better equal-time error in all scenes tested, and when combined with a recent denoising post-correction technique, significantly faster error convergence is realized.