The dragon

FINALY AVAILABLE!

http://people.brandeis.edu/~mirman/

yay- 4000 spp

I fixed boolean opperations, and I have a webpage.

http://people.brandeis.edu/~mirman/

anybody who can, can yall try to open tlrcam's jar and see if you can read the source?

at 900 spp.

i ran some tests and found that path tracing runs at about 8000 mps on this image, and mlt/this runs at 7000 mps. not as big a difference over the long run as i though. it means i can stop trying to optimize it as much.

Problem: both mlt, this, and path tracing start out rendering at about 30000 mps, then go on to an average of 20000 mps for about 5 miniutes, (the warm up ends after about 1 miniute), and then finally jumps down to below 10000 mps. I have no idea why it is doing this.
ideas: Java's garbage collector not acting up...
mt random number generator something
memory leak.
no clue.

Any ideas?

More from the new MLT algorithm

I improved it a little, and it seems to work fairly well. Now all I need is an importer, to blender or some other program, and I will open source it- well, possibly with a copyright so I can prove that I wrote it, something like PBRT has.- all except for the new MLT code, which I'd prefer to keep a secret a bit longer.

Now tell me, is this an improvment worth pursuing? Or should I go ahead and implement the paper on adaptive multi-dimensional sampling that seems to show such great improvements (does anybody know of any renderers that use this?)

Regular path tracing- 300 spp- this was the first time i tried to benchmark it without eclipse being open and me doing stuff. on my 1.9ghz core 2 duo, with 2 threads, on vista 64, i was getting an average of 10000 mps

The image after 315 spp, I'm not going to say how long this took because it was only on one core and was rendered while I was using my computer intensively.

The new algorithm after 20spp, at about 10 minutes on a dual core. -Lprob .01-.99 -maxRej 10-1000

the regular MLT algorithm with 20spp at about 10 miniutes, with an -Lprob .4 and maxrej of 500

the regular image's lprob and maxrej was set to be what the new algorithm's lprob and maxrej would average out to given thoes paremeters and scene. All MLT is based off of Keleman et al.'s robust mutation strategy paper, and paper on hybrid MLT.

stratisfied sampling+MLT

I haven't found any papers on this subject exactly, but I had an idea (and hopefully if there is nothing out there, I can write my own paper). What I seem to have had the most trouble with in MLT was to sample uniformly(as uniformly as possible) every direction at a bounce, where applicable.  The trouble is, it is very hard to keep track of where MLT has sampled previously, and what it should do next/how it should do it next.  What I have done is essentially modified the balance heuristic in order to sample every pixel as evenly as possible

, and sample every direction as evenly as possible.  This only required slight modification to my path tracer, and does not introduce any bias.  There are numerous other subtleties, but I'd rather not go over all of them, because if It is a unique idea/implementation, I could write a paper on it, which would be nice.

With the modified balance heuristic at 51 spp at about 20 miniutes:

        

without the modification at 81 spp at about 30 miniutes:

High Dynamic Range lighting

I have the preliminary HDRI implemented, i just want to do it now with more than one sort of map (lat/long)

Now I have insane amounts of work to do, so I will not be able to post anything else for a while.