# Cranberry Lair

## Walking the programming parameter space

### Intro

In our last post, we took a look at texture sampling and bump mapping. In this post, we’ll be talking about the last few additions that were made to cranberray before it was wrapped up. Converting from a recursive path tracer to an iterative path tracer and implementing tone mapping using ACES tone mapping curves.

### Iterative Path Tracing

Converting cranberray from a recursive path tracer to an iterative path tracer was surprisingly simple. Before this change, cranberray would shoot a ray from the camera and run the appropriate shader at the intersection point. This shader would in turn shoot a ray in some arbitrary direction to determine how much light should illuminate that location. This would continue recursively until the rays would either reach their maximum recursive depth or the ray would terminate by exiting the scene completely and sampling our skybox.

This approach makes sense if you think of every point as reflecting light that it has received from another location. As a result, you need to know how much light you’ve received before you can shade your point.

However, instead you can consider the opposite view. That every point is continuously absorbing light. We can see that the amount of light that the viewer receives is modeled by $light = lightSource * absorb_0 * absorb_1 * absorb_2 ... * absorb_n$

With that in mind, we can see that we can simply calculate how much light we absorb at each point without needing to know the quality or the quantity of the light that we are receiving.

Instead of thinking of it as the light source being recursively reflected to the viewer.

light recursive(i)
{
return emissionAtPoint(i) + absorptionAtPoint(i) * recursive(i + 1);
}


We instead think of it as the light being absorbed at every stage

light iterative()
{
for(i = 0; i < maxDepth; i++)
{
light += absorption * emissionAtPoint(i);
absorption *= absorptionAtPoint(i);
}
return light;
}


This has some interesting properties!

• If we notice that our absorption has reached 0, we can stop iterating.
• We can multi-thread this much more easily than the recursive approach.
• It has the desirable property of not easily introducing stack overflows.

I also find this version easier to think about which allows me more flexibility in the solutions I can imagine.

### Tone Mapping

I will only touch briefly on tone mapping, I find that ,  and  already provide excellent views on this topic.

Cranberray makes use of the histogram based average luminance calculations alongside the ACES tone mapping operator. You can find more information about these in the referenced articles.

However, one portion that I would like to talk about is why we are using the geometric mean to calculate the average luminance and hopefully provide some intuitions about the geometric mean.

As you can see in , the histogram luminance is stored as a histogram of the logarithms of our luminance instead of as their raw value. We sum up the values of the logarithms, divide it by the total number of values and revert it to a luminance.

This is the definition of the geometric mean. You can calculate the geometric mean either through repeated multiplication: $\sqrt[n]{i_0*i_1*...*i_n}$

Or by using the logarithmic identities: $e^{1/n*((ln(i_0)+ln(i_1)+...+ln(i_n)))}$

It took me quite some time to understand why the geometric mean would be a desirable value when doing tone mapping. Then, I realized that it is desirable when you are speaking in terms of multiplicative properties.

If I’m speaking of light, I’m thinking of the intensity either being twice as bright, or half as bright. In most scenarios, I don’t necessarily care if something is 2 units brighter versus 2 units dimmer. The center value of something that is twice as bright and half as bright is the geometric mean.

You can derive this by imagining that we have our center value $x$.

Imagine that we have a set of numbers, $0.5x, 1x, 2x$ and I would like to determine the value of $x$. If we repeatedly multiply our values, we get $0.5*1*2*x^3=x^3$ and we can then retrieve our value by taking the cube root.

Generalizing, given 3 variables $y,z,w$

where $y=ax, z=bx, w=cx$

and $a*b*c = 1$ (1)

Then we can find a value for x by $\sqrt{y*z*w}=m$

since $\sqrt{ax*bx*cx}=m$ $\sqrt{a*b*c*x^3}=m$

given (1), then $\sqrt{x^3}=m$ $x=m$

As an example, say we have the values 0.2, 0.7, 1.3. If we take the geometric mean we get $\sqrt{0.2*0.7*1.3}$ which is 0.56670511081. Now if we divide our original values by that value we get 0.35291723364, 1.23521031776 and 2.2939620187 which when multiplied together give 1!

In terms of desirable properties, let’s take a look at a more visual example.

These images show the difference between using the arithmetic mean to normalize our values and using the geometric mean to normalize our values. The top row is our arithmetic mean and the bottom row is our geometric mean.

Our colors are simply generated by starting with 2, 4, 8, 16, 32, 2^n and calculating their arithmetic and geometric means then dividing each value by the respective means.

In our first example we have 2, 4 and 8. We divide by 4.6666 with the arithmetic mean and divide by 4 with the geometric mean.

You’ll notice that as our numbers get larger and larger, the arithmetic mean slowly loses all detail in the low range of our values and only maintains the brightest values at the edges.

Notice also that the geometric mean remains centered despite the ever growing magnitude of our values. This is desirable since most of our image would become very dark, very quickly.

### Conclusion

That’s it for this series! Our next series will likely focus on a completely different topic, possibly in the world of real-time rendering. Cranberray was an excellent experience in various areas of path tracing. I would love to come back to it someday. Until next time, happy coding!