Uploaded June 2017 | Updated September 2026, 3 days ago
This talk was given by undergraduate Raymond Kim during the 11th Annual Computer Science Undergraduate Research Symposium in 2016. Raymond‘s research was supervised by Dr. Sanjoy Baruah.
“Variance Reduction in Global Illumination with Monte Carlo Methods”
Monte Carlo methods are a family of numerical methods that solves problems by producing an approximation through a statistical approach using both deterministic and stochastic processes. The naïve way to reduce variance in our approximation is to increase the number of samples we take; however, as the complexity of the problem grows, this method becomes inefficient. We look into a few common variance reduction techniques and how we can apply them in the global illumination problem.
Raymond Kim is a senior majoring in computer science and mathematics who is interested in probability and geometry. He will be returning in the fall as part of the B.S./M.S. program.
cs.unc.edu/academics/undergraduate/symposium/symposium-2017/
This talk was given by undergraduate Raymond Kim during the 11th Annual Computer Science Undergraduate Research Symposium in 2016. Raymond‘s research was supervised by Dr. Sanjoy Baruah.
“Variance Reduction in Global Illumination with Monte Carlo Methods”
Monte Carlo methods are a family of numerical methods that solves problems by producing an approximation through a statistical approach using both deterministic and stochastic processes. The naïve way to reduce variance in our approximation is to increase the number of samples we take; however, as the complexity of the problem grows, this method becomes inefficient. We look into a few common variance reduction techniques and how we can apply them in the global illumination problem.
Raymond Kim is a senior majoring in computer science and mathematics who is interested in probability and geometry. He will be returning in the fall as part of the B.S./M.S. program.
cs.unc.edu/academics/undergraduate/symposium/symposium-2017/
