Lecture 13: Global Illumination & Path Tracing (81)

ABSchloss

Any intuition into common choices for p_rr, or is it extremely variable by scene? If there is a common choice, does it seem to model the real world well? Or is real world modeling inefficient so, if we chose solely for efficiency, we end up with a noticeably different result?

bufudash

How does Russian Roulette, aka reweighing first and terminate if high expense unbiased? Isn't it still true in this case that we cannot account what happens if a light takes n+1 bounces?

visatish

@bufudash I believe it is unbiased by definition because it has the same expected value as the original estimator

visatish

@ABSchloss I looked at some example implementations online, and it seems that $p_{rr}$ is not really a constant value but dynamically computed at runtime and changes as the algorithm progresses

ChrisP19

Although the estimator is unbiased, I was curious about its effect on the variance: $V[X_{rr}] = E[X_{rr}^2] - E[X_{rr}]^2$

Let's say $p_{rr} = \frac{1}{2^n}$, then the increase in variance scales at a rate of $2^n$. We can also note if $p_{rr}=1$, then there is no increase in variance.

Any intuition into common choices for p_rr, or is it extremely variable by scene? If there is a common choice, does it seem to model the real world well? Or is real world modeling inefficient so, if we chose solely for efficiency, we end up with a noticeably different result?

How does Russian Roulette, aka reweighing first and terminate if high expense unbiased? Isn't it still true in this case that we cannot account what happens if a light takes n+1 bounces?

@bufudash I believe it is unbiased by definition because it has the same expected value as the original estimator

@ABSchloss I looked at some example implementations online, and it seems that $p_{rr}$ is not really a constant value but dynamically computed at runtime and changes as the algorithm progresses

Although the estimator is unbiased, I was curious about its effect on the variance:

$V[X_{rr}] = E[X_{rr}^2] - E[X_{rr}]^2$

$E[X_{rr}^2] = p_{rr} E[\frac{X^2}{p_{rr}^2}]$

$= \frac{1}{p_{rr}} E[X^2]$

$E[X_{rr}]^2 = E[X]^2$

$V[X_{rr}] = \frac{1}{p_{rr}} E[X^2] - E[X^2]$

$= V[X] + (\frac{1}{p_{rr}} - 1)E[X^2]$

Let's say $p_{rr} = \frac{1}{2^n}$, then the increase in variance scales at a rate of $2^n$. We can also note if $p_{rr}=1$, then there is no increase in variance.