Optimizing Control Variate Estimators for Rendering

We present the Optimizing Control Variate (OCV) estimator, a new estimator for Monte Carlo rendering. Based upon a deterministic sampling framework, OCV allows multiple importance sampling functions to be combined in one algorithm. Its optimizing nature addresses a major problem with control variate estimators for rendering: users supply a generic correlated function which is optimized for each estimate, rather than a single highly tuned one that must work well everywhere. We demonstrate OCV with both direct lighting and irradiance-caching examples, showing improvements in image error of over 35% in some cases, for little extra computation time. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism Color, shading, shadowing, and texture G.3 [Probability and Statistics]: Probabilistic Algorithms

• Type: Journal
• Year: 2006
• Where: CGF
• Authors: Shaohua Fan, Stephen Chenney, Bo Hu, Kam-Wah Tsui, Yu-Chi Lai
• Keywords: CGF 2006, Deterministic Sampling Framework, Estimators, Generic Correlated Function