Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings

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 limit...

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Bibliographic Details
Published in:Computer graphics forum 2016-07, Vol.35 (4), p.107-117
Main Authors: Bitterli, Benedikt, Rousselle, Fabrice, Moon, Bochang, Iglesias-Guitián, José A., Adler, David, Mitchell, Kenny, Jarosz, Wojciech, Novák, Jan
Format: Article
Language:English
Subjects:
Algorithms
Buffers
Categories and Subject Descriptors (according to ACM CCS)
Collaboration
Computer graphics
Computer simulation
I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-Raytracing
I.4.3 [Computer Graphics]: Enhancement-Filtering
Monte Carlo methods
Monte Carlo simulation
Noise reduction
Regression
Regression analysis
Rendering
Studies
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Summary: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.
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12954