Skip to main content

A personal annotations of Veach's thesis (11) P.67

2.7.2 Russian Roulette

Russian roulette methods is one of sampling techniques. It selects sample path stochastically. This includes stochastic ray termination. The light transport equation is an integral and the equation has several terms. The magnitude of the term depends on the sample. If the term is quite small or the material's reflectance property said one direction is not so probable, sampling such term or direction has less effect, yet still costs the same. But, if we don't sample such term at all, the solution will be biased. Because it is always possible that the light comes from that direction.


Figure 1. Russian Roulette: (a) non-Russian roulette (splitting) ray tracing, sample many directions when sampling a glossy surface, (b1)  Russian roulette sample, sampling only one direction depends on the probability, (b2) Russian roulette stochastic termination (c) a trace path is also chosen and terminated by specific probabilities.


Figure1 (a) is an example of non Russian roulette sampling method (splitting). A glossy surface is affected from incoming light from many directions, therefore we need sample many directions. The method (b1) is a Russian roulette method and only samples one direction per one incoming ray. The direction of sampling decided stochastically. Or like (b2) case, a ray is terminated stochastically. You might say, if only one direction is sampled, how the glossy surface can be described. To see around, we need to cast many rays at each pixel. In the case of (b1,b2), one ray continues to sample only one direction, therefore there is no exponential growth of computational cost. (c) shows a stochastically created path and also it is stochastically terminated.  This is the Russian roulette method. In this method, even when the ray hit a transparent/reflective surface, we sample only one transparent or one reflective direction (or terminated). This method needs a lot of samples for each pixel, but, each path calculation time is not so drastically changed by path.  This is an important technique for an unbiased algorithm.

Acknowledgements:
Thanks to Leo for comments and discussions.

Comments

Popular posts from this blog

Gauss's quote for positive, negative, and imaginary number

Recently I watched the following great videos about imaginary numbers by Welch Labs. https://youtu.be/T647CGsuOVU?list=PLiaHhY2iBX9g6KIvZ_703G3KJXapKkNaF I like this article about naming of math by Kalid Azad. https://betterexplained.com/articles/learning-tip-idea-name/ Both articles mentioned about Gauss, who suggested to use other names of positive, negative, and imaginary numbers. Gauss wrote these names are wrong and that is one of the reason people didn't get why negative times negative is positive, or, pure positive imaginary times pure positive imaginary is negative real number. I made a few videos about explaining why -1 * -1 = +1, too. Explanation: why -1 * -1 = +1 by pattern https://youtu.be/uD7JRdAzKP8 Explanation: why -1 * -1 = +1 by climbing a mountain https://youtu.be/uD7JRdAzKP8 But actually Gauss's insight is much powerful. The original is in the GauĂź, Werke, Bd. 2, S. 178 . Hätte man +1, -1, √-1) nicht positiv, negative, imaginäre (oder gar um...

Why A^{T}A is invertible? (2) Linear Algebra

Why A^{T}A has the inverse Let me explain why A^{T}A has the inverse, if the columns of A are independent. First, if a matrix is n by n, and all the columns are independent, then this is a square full rank matrix. Therefore, there is the inverse. So, the problem is when A is a m by n, rectangle matrix.  Strang's explanation is based on null space. Null space and column space are the fundamental of the linear algebra. This explanation is simple and clear. However, when I was a University student, I did not recall the explanation of the null space in my linear algebra class. Maybe I was careless. I regret that... Explanation based on null space This explanation is based on Strang's book. Column space and null space are the main characters. Let's start with this explanation. Assume  x  where x is in the null space of A .  The matrices ( A^{T} A ) and A share the null space as the following: This means, if x is in the null space of A , x is also in the n...

My solution of Google drive hang up at "One moment please"

Today I installed Google drive to my Windows 7 environment to share files with my Linux machines. After sign in, the application window said "processing," then it hanged up. There was a button "you must enable javascript". I pushed it, then "One moment please..." after 5 minutes, I exited the program tried it again. It seems some security setting causes this problem. My solution: set  https://accounts.google.com  as a trusted site. Procedure: Open the control panel Go to network and control Go to Internet Options Open Security Tab Click Trusted sites Click the "site" button copy & paste  https://accounts.google.com  to "Add this website to the zone" and click Add button Now it worked for me. But if I removed this site, it still works. That puzzled me a bit...