With the ongoing shift in the computer graphics industry toward Monte Carlo rendering, there is a need for effective, practical noise-reduction techniques that are applicable to a wide range of rendering effects and easily integrated into existing production pipelines. This course surveys recent advances in image-space adaptive sampling and reconstruction algorithms for noise reduction, which have proven very effective at reducing the computational cost of Monte Carlo techniques in practice. These approaches leverage advanced image-filtering techniques with statistical methods for error estimation. They are attractive because they can be integrated easily into conventional Monte Carlo rendering frameworks, they are applicable to most rendering effects, and their computational overhead is modest.

Course slides:

• Introduction (Sen)
  
• Leveraging General Image-Denoising Algorithms For MC Denoising (Kalantari)
  
• Filtering The Noise From The Random Parameters In Monte Carlo (Sen)
  
• Cross-Bilateral and NL-Means Filtering With SURE-Based Error Estimation and Feature Pre-Filtering (Rousselle)
  
• Local Weighted Regression With Explicit Bias and Variance Estimation (Yoon)
• Using Machine Learning to Learn Optimal Filter Parameters (Kalantari)
• Conclusions (Zwicker)