Noise Reduction on G-Buffers for Monte Carlo Filtering

Abstract

We propose a novel pre-filtering method that reduces the noise introduced by depth-of-field and motion blur effects in geometric buffers (G-buffers) such as texture, normal and depth images. Our pre-filtering uses world positions and their variances to effectively remove high-frequency noise while carefully preserving high-frequency edges in the G-buffers. We design a new anisotropic filter based on a per-pixel covariance matrix of world position samples. A general error estimator, Stein's unbiased risk estimator, is then applied to estimate the optimal trade-off between the bias and variance of pre-filtered results. We have demonstrated that our pre-filtering improves the results of existing filtering methods numerically and visually for challenging scenes where depth-of-field and motion blurring introduce a significant amount of noise in the G-buffers.