Noise Reduction on G‐Buffers for Monte Carlo Filtering. (23rd May 2017)

• Record Type: Journal Article
• Authors: Moon, Bochang; Iglesias‐Guitian, Jose A.; McDonagh, Steven; Mitchell, Kenny
• Is Part Of: Computer graphics forum. Volume 36:Number 8(2017)
• Page Start: 600
• Page End: 612
• Publication Date: 2017-05-23
• Subjects: image filtering -- denoising -- Monte Carlo ray tracing -- Three‐Dimensional Graphics and Realism [I.3.7]: Raytracing; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13155
• Languages: English
• ISSNs: 0167-7055

Abstract

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. 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.