I am learning ray tracing and the mathematics behind it. I have a working monte-carlo raytracer I am experimenting on. I have gotten past pure diffuse BRDFS, area lights, acceleration structures, etc, and now I'm working on properly sampling the next ray direction from an anisotropic distribution. I believe I have interpreted the D,G,F factors correctly, and have working tests that verify those function's outputs.

I am having trouble with drawing a "wi" sample from the half vector distribution function. The purpose of the function is to give you a randomized half vector in shading space based on the "incoming" wo ray, which is the eye ray, and a roughness parameter. As I graph out the function outputs, it seems like the half-vector sampling function begins to rotate the spherical coordinates about the wrong axis (I know this makes no sense, but I'm just observing the graphed results).

Knowing that situation, I was hoping some people could post some pointers to good academic articles, open source code examples, or other educational material that could help me investigate and educate myself on the behavior of functions that generate half-vectors from distributions. I have found plenty of the original source papers from Cook-Torrence, etc that explain their algorithms, but I cannot find any that explain in plain language HOW to generate the half vector for the eye ray to reflect about.

Note: I have also extensively explored PBRT and its source code, all of which has been incredibly enlightening, and I have seen their sample_wi function, i even went so far as to directly copy and translate it to webgl for troubleshooting my functions. I know i must be doing or interpreting something wrong - I just can't find enough material out there to help me figure out what.

Here is a visualization of only the half vector normals. They have been put into a range of [0,1] for viewing in the RGB spectrum. I have also boosted the contrast of the image to make the error more easily visible. You can clearly see that instead of randomly orienting themselves about the surface normal, they are instead correlating themselves radially about the camera eye-vector. enter image description here

here is a webgl "proof" i put together to isolate and illustrate my problem. What should happen is the color should be fairly blended together, interpolated rather nicely. Instead I get this obvious hard shift in the distribution throughout the 4 quadrants: http://rdtests.ml3ds-test.com/microfacettesting.html

The functions referenced by main function here can all be viewed directly in the source code of the page I linked to:

        vec3 finalColor = vec3(0.);

        // create a changing "wo" vector across screen space (simulates perspective ray change)
        vec2 screenUv = gl_FragCoord.xy / u_resolution;
        // fire rays into positive z (forward) space from "camera"s
        float z = 1.;

        vec3 rd = normalize(vec3(screenUv, z));

        // remap 0,1 to -1,1
        vec3 wo = (rd * 2.) - 1.;
        // wo is outward facing
        wo *= -wo;

        // convert wo to spherical coordinates
        vec3 Nt, Nb;
        // simulate hitting an infinite plane, facing us like a wall
        vec3 normal = vec3(0., 0.,-1.);
        CoordinateSystem(normal, Nt, Nb);

        // put wo into the shading space coordinate system of the "wall" normal
        vec3 shadingWo = WorldToShading(wo, Nt, Nb, normal);

        // sample half vector distribution from trowbridge-reitz
        // make random input a constant to allow error to be more clearly seen
        vec3 wh = Dist_TR_Sample_wh(shadingWo, vec2(.2,.1), .5, .5);

        // Reflect wo about the half vector normal
        vec3 wi = Reflect(shadingWo, wh);

        // put wi back into cartesian coordinate system
        vec3 wiWorld = ShadingToWorld(wi, Nt, Nb, normal);

        // now remap wi to a viewable color: 0,1
        finalColor = normalize((wiWorld + 1.) / 2.);

        gl_FragColor = vec4(finalColor, 1.);
  • $\begingroup$ Have you tried stepping through in a debugger to see just where and how the values "go crazy"? If it happens only when cos > 0.9999, that suggests a possible numerical precision problem. (Also, it sounds like you have two questions here—is your main question "how to sample a half-vector distribution" or "why does this function go crazy"? We can answer the former, but I don't know if it will help debug the problem you're having; for the latter, the relevant source code, example results, etc would be useful.) $\endgroup$ – Nathan Reed May 1 '17 at 17:52
  • $\begingroup$ I worked on this over the last 2 days, I will update my question with the results. I have ruled out that the cosine angle is the issue. It more looks like an issue where the spherical coordinates are rotating about the wrong axis. I graphed the results of my half-vector samples and I will upload a link to the image viewing their distribution. $\endgroup$ – Steve May 1 '17 at 17:57
  • $\begingroup$ Also, thanks for the pointer - I updated the title accordingly. $\endgroup$ – Steve May 1 '17 at 18:19

The general idea for sampling half vector based distributions is that you generate $H$ and then compute $w_i$ by reflecting $w_o$ about $H$. This is so $H$ will be the half vector of your $w_i$ and $w_o$ pair. It is standard reflection: $$w_i = -w_o + 2(w_o\cdot H)H $$

How you generate $H$ depends on the specific distribution. Generally, it is done in polar coordinates, with the angle from your distribution's center being picked using some specific function. Then the azimuth will be a uniform distribution, unless your distribution is anisotropic, in which case it gets more complicated.

For example, with Cook-Torrance, it goes something like: $$\tan \theta = m \sqrt{ - \log \xi_1 }$$ $$\phi = \xi_2 2\pi$$

Then, given an orthonormal basis made of vectors $N$, $a$, $b$:

$$H = a\sin\theta\cos\phi + b\sin\theta\sin\phi + N\cos\theta$$

As for problems in your code, replacing this:

vec3 rd = normalize(vec3(screenUv, z));

// remap 0,1 to -1,1
vec3 wo = (rd * 2.) - 1.;
// wo is outward facing
wo *= -wo;


vec3 wo = normalize(vec3(screenUv*2.0-1.0, -z));

Gives me a much nicer plot. Your remapping was incorrect as it was done on a vector which includes z and is no longer in 0,1 for x and y because of the normalization. This explains why the center was not in the center of the frame and the whole thing was leaning. Removing wo *= -wo; (a transformation which makes no sense) and using -z instead fixes what I think you were calling the hard transition in the 4 quadrants. That was caused by squaring the components of the vector.

The remaining glitch in the center appears to be numerical issues. Fixing that will require reworking the code to better handle some cases (likely where you have the 0.0008 constant for a sin and the 0.9999 for a cos).

  • $\begingroup$ Thank you for that. I put together a live HTML/WebGL test that isolates only this problem. I am essentially using a variation of the equation you posted (but following the PBRT code to be anisotropic instead of iso). Something strange is happening as I move off of normal incidence to this function. My half-vectors seem to be "leaning". Not sure how else to describe it. I posted the HTML proof that has all the math/shader code inline and commented for readability. $\endgroup$ – Steve May 1 '17 at 20:27
  • $\begingroup$ Followed your advice and went back to the basics. Implemented the cook torrence equation, verified basic inputs look right, and determined that sampling the distribution works fine, the problem must be elsewhere, outside of these basics. Thanks for taking the time to read my question. $\endgroup$ – Steve May 1 '17 at 21:21
  • $\begingroup$ @Steve your WebGL example looks smooth here. Ugly yellow in the lower left corner, more grey in the upper right. Did you change it to the simpler distribution or is it still supposed to be the broken code? I'm using chromium on linux btw. Or is the yellow "blob" supposed to be centered? Either way,wo *= -wo; looks suspicious. $\endgroup$ – Olivier May 1 '17 at 22:48
  • $\begingroup$ I'm actually debugging that issue right now. I called it "solved" because i made an error. I just updated to "correct" code and my distribution problem is back again. If you refresh the webpage you'll see that my "crossed quadrants" are back $\endgroup$ – Steve May 1 '17 at 22:50
  • $\begingroup$ Currently, I am visualizing the half vectors as they come back raw from the distribution. $\endgroup$ – Steve May 1 '17 at 22:51

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