My first Supersize-Image

[XMAMan]

Ok here is my first little step to subsurface scattering. I have extended my bidirectional pathtracing with Homogen-Media-Support. So I have now a unbiased estimator. The next steps will be Rayleigh,Mie-Scattering and inhomogeneous media.

[XMAMan]

Step 2: Rayleigh- and Mie-Scattering.

For Rayleigh-Phase-Function-Direction-Sampling I used 'Importance sampling the Rayleigh phase function from jeppe' (Inverse CDF)
For the Mie I use tabulation and the formula from 'ScratchAPixel Simulating the Colors of the Sky'

For distancesamling, I use Raab-Woodcock 'Unbiased Global Illumination with Participating Media - Raab et al (2008)'.

For the attenuation-term I use a pre-computed table. A little Idea how to implement I used the paper from Nishita 'Display Method of the Sky Color Taking into Account Multiple Scattering 1996'.

The next step is adding clouds.

[XMAMan]

In the next Image you can see the difference between single-scattering and multiple-scattering
Image 1: Bidirectional Pathtracing (MultipeScattering)
Image 2: Sampling a Particle-Point on the Edge from a Eye-Subpaht (Dont know the offizial name from this path-creation-sampling-routine) (MultipeScattering)
Image 3: Sampline a Particle-Point only on the edge from the Primary-Ray (Single Scattering)
Image 4: Create 20 Segments on the Primary-Ray-Edge and connect it to the sun (Single Scattering)

[XMAMan]

This is the well known water-cornellbox

[XMAMan]

And this the Mirror-Box. Got the idea from dawelter^^

[dawelter]

Nice progress!

[XMAMan]

Ok. Here is my next Participating-Media-Image: Clouds . For the sky I use the Model from Nishita and for the clouds the cumulus-cloud-modul from David Ebert.

The images a rendered with 200 Samples per Pixel. I use lightsourcesampling on random points on the eye-path-segments and lightsourcesampling also for the eye-path-points to create a path from the camera to the light.

[XMAMan]

The next little step: Unifying Points, Beams, and Paths in Volumetric Light Transport Simulation (UPBP)

Finally after writing 435 UnitTests, to test every single Class/Sampler/SubPathSampler/FullPathSampler/PathcontributionCheck/Pdf match with Histogram-Test, I can now finally say, that I have mastered UPBP. I have written a importer for the scene-desciption-file, which are also is used in the stilllife-scene from SmallUPBP. To compare my result with a reference.

Can someone say me, what is the next step in the topic of Participating Media? Better Distancesampling? Better importancesampling durring segmentsampling?

Here is a image with 2150 samples.

[ultimatemau]

The next little step: Unifying Points, Beams, and Paths in Volumetric Light Transport Simulation (UPBP)

Finally after writing 435 UnitTests, to test every single Class/Sampler/SubPathSampler/FullPathSampler/PathcontributionCheck/Pdf match with Histogram-Test, I can now finally say, that I have mastered UPBP. I have written a importer for the scene-desciption-file, which are also is used in the stilllife-scene from SmallUPBP. To compare my result with a reference.

Can someone say me, what is the next step in the topic of Participating Media? Better Distancesampling? Better importancesampling durring segmentsampling?

Here is a image with 500 samples.

Hi,

Very nice progress, really impressed! The next big / most state-of-the-art step you can take is better: distance sampling + sampling scatter direction (phase function sampling) + better Russian roulette and possibly path-splitting! Sounds like a lot?

Check out this paper, as far as I'm concerned it's in the top 3 papers of the year:
https://cgg.mff.cuni.cz/~jaroslav/papers/2019-volume-path-guiding/index.html

For more related papers check out "Path Guiding in Production" from Siggraph this year, must read by the looks of your progress so far:
https://jo.dreggn.org/path-tracing-in-production/2019/guiding.pdf

If you're interested in "Surface Path Guiding" too after reading all that, I'd suggest checking out the links in chapter / section 10 in "Path Guiding in Production". Follow the links. "Practical Path Guiding" is a paper from 2017, but got some nice additions from a more recent paper "Neural Importance Sampling" to optimize the MIS weights during training. Don't shy away from this one, it has source code you can look at too.

Have fun!

Cheers

[XMAMan]

He ultimatemau, thank you for you answer. All the Papers sounds good. I will look on that.