Real-time Subsurface Control Variates

Real-time adaptive sampling is a new technique recently proposed for efficient importance sampling in realtime Monte Carlo sampling in subsurface scattering. It adaptively places samples based on variance tracking to help escape the uncanny valley of subsurface rendering. However, the occasional performance drop due to temporal lighting dynamics (e.g., guns or lights turning on and off) could hinder adoption in games or other applications where smooth high frame rate is preferred. In this paper we propose a novel usage of Control Variates (CV) in the sample domain instead of shading domain to maintain a consistent low pass time. Our algorithm seamlessly reduces to diffuse with zero scattering samples for sub-pixel scattering. We propose a novel joint-optimization algorithm for sample count and CV coefficient estimation. The main enabler is our novel time-variant covariance updating method that helps remove the effect of recent temporal dynamics from variance tracking. Since bandwidth is critical in real-time rendering, a solution without adding any extra textures is also provided.

[1]  Jan Novák,et al.  Residual ratio tracking for estimating attenuation in participating media , 2014, ACM Trans. Graph..

[2]  B. Welford Note on a Method for Calculating Corrected Sums of Squares and Products , 1962 .

[3]  Stephen S. Lavenberg,et al.  Statistical Results on Control Variables with Application to Queueing Network Simulation , 1982, Oper. Res..

[4]  Bo Hu,et al.  Optimizing Control Variate Estimators for Rendering , 2006, Comput. Graph. Forum.

[5]  Bochang Moon,et al.  Adaptive Rendering Based on Weighted Local Regression , 2014, ACM Trans. Graph..

[6]  Mateu Sbert,et al.  Combined Correlated and Importance Sampling in Direct Light Source Computation and Environment Mapping , 2004, Comput. Graph. Forum.

[7]  U. Cherubini,et al.  RiskMetrics Technical Document , 2015 .

[8]  Diego Gutierrez,et al.  Screen-space perceptual rendering of human skin , 2009, TAP.

[9]  Steve Marschner,et al.  A practical model for subsurface light transport , 2001, SIGGRAPH.

[10]  Jorge Jimenez Separable subsurface scattering , 2012, SIGGRAPH '12.

[11]  Brian D. Ripley,et al.  Stochastic Simulation , 2005 .

[12]  Eric R. Ziegel,et al.  Analysis of Financial Time Series , 2002, Technometrics.

[13]  Philipp Slusallek,et al.  Optimal multiple importance sampling , 2019, ACM Trans. Graph..

[14]  Barry L. Nelson,et al.  Control Variate Remedies , 1990, Oper. Res..

[15]  James Arvo,et al.  Unbiased sampling techniques for image synthesis , 1991, SIGGRAPH.

[16]  George Borshukov,et al.  Pre-Integrated Skin Shading , 2011 .

[17]  Kevin Henry,et al.  Risk Management and Analysis , 2007, Information Security Management Handbook, 6th ed..

[18]  Jan Novák,et al.  Neural control variates , 2020, ACM Trans. Graph..

[19]  Alexander Keller,et al.  A Hierarchical Automatic Stopping Condition for Monte Carlo Global Illumination , 2010 .

[20]  J. L. Roux An Introduction to the Kalman Filter , 2003 .

[21]  Extending the Disney BRDF to a BSDF with Integrated Subsurface Scattering , 2015 .

[22]  Jan Novák,et al.  Image-space control variates for rendering , 2016, ACM Trans. Graph..

[23]  Tiantian Xie,et al.  Real-time subsurface scattering with single pass variance-guided adaptive importance sampling , 2020, Proc. ACM Comput. Graph. Interact. Tech..

[24]  Per H. Christensen,et al.  An approximate reflectance profile for efficient subsurface scattering , 2015, SIGGRAPH Talks.

[25]  B. Nelson,et al.  Control Variates for Probability and Quantile Estimation , 1998 .

[26]  Greg Humphreys,et al.  Physically Based Rendering: From Theory to Implementation , 2004 .