Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings
by
Benedikt Bitterli, Fabrice Rousselle, Bochang Moon, Jose A. Iglesias-Guitian,
David Adler, Kenny Mitchell, Wojciech Jarosz, Jan Novak
Computer Graphics Forum (Proc. of Eurographics Symposium on Rendering 2016)
Abstract
We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm
that yields state-of-the-art performance on a wide range of scenes. We analyze existing approaches from a theoretical and empirical
point of view, relating the strengths and limitations of their corresponding components with an emphasis on production
requirements. The observations of our analysis instruct the design of our new filter that offers high-quality results and stable
performance. A key observation of our analysis is that using auxiliary buffers (normal, albedo, etc.) to compute the regression
weights greatly improves the robustness of zero-order models, but can be detrimental to first-order models. Consequently, our
filter performs a first-order regression leveraging a rich set of auxiliary buffers only when fitting the data, and, unlike recent
works, considers the pixel color alone when computing the regression weights. We further improve the quality of our output by
using a collaborative denoising scheme. Lastly, we introduce a general mean squared error estimator, which can handle the
collaborative nature of our filter and its nonlinear weights, to automatically set the bandwidth of our regression kernel.
Contents
Main Report (pdf, 25.3 MB)
Video (mp4, 116 MB)
Supplementary Report