Cornellbox: Markov Chain Random Walking, Part 1


CS 6650: Image Synthesis

Instructor Peter Shirley







XianMing Chen, xchen AT cs DOT utah DOT edu




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Again, physically correct result is my primary concern. And I also have a strong belief that for the simple scene such as cornell box, perhaps just the simplest strategy will be enough. To this end, I tried MLT algorithms to solve the light transport integral constrained to subdomain of path length 2, by re-initialize muation, so that T(y|x) is simply T(y). The luminance is taken as the sum of rgb component. The sample number listed below is the average sample per pixel.

Comparison of central pixel value of scene . For MLT algorithms, seed pool size is 100,000.

Analytic Value path tracing(s1t2) 9 samples MLT 100 samples MLT 400 samples MLT 900 samples
~~~0.01 0.0125904 0.0110081 0.0119063



Comparison of partial solution (constrained to lenght 2 path domain) between MLT and path-tracing.

s1t2, 100 samples MLT, 100 samples MLT, 400 samples