Noise Reduction on G-Buffers for Monte Carlo Filtering

Bochang Moon 12 , Jose A. Iglesias-Guitian 1 , Steven McDonagh 13 , Kenny Mitchell 14

Disney Research 1 , Gwangju Institute of Science and Technology 2 , Imperial College London 3 , Edinburgh Napier University 4

Computer Graphics Forum (Presented at Eurographics Symposium on Rendering 2017)

Noise Reduction on G-Buffers for Monte Carlo Filtering
Our results for the Cars scene where each car has a different motion. The input image (a), and its close-up (b), are generated using 8 samples per pixel (spp) allocated by an adaptive sampler using weighted local regression (WLR) [MCY14]. Recent filtering methods utilize geometric buffers (G-buffers) such as texture (c), normal (e), and depth, which may contain severe noise in regions with strong motion blur effects. As a result, the state-of-the-art method (g) produces over- and underblurred results in those regions. Our method applies an anisotropic pre-filtering to the noisy feature buffers and generates the pre-filtered G-buffers (d) and (f). The recent filter (h) that utilizes our results instead of the noisy G-buffers, shows a reduced error, i.e., the relative mean squared error (rMSE) [RKZ11], and better preserved edges thanks to our high-quality pre-filtering.


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.