# Cranberry Lair

## Walking the programming parameter space

### Intro

In our last post, we looked at cranberray’s sampling strategy and it’s implementation of N-Rooks sampling. In this post, we’re going to look at cranberray’s BRDF implementations and multiple importance sampling.

Cranberray’s shading pipeling is very simple. When loading a mesh, ranges of triangles (or submesh) are tagged to use a specific material. When intersecting the geometry, cranberray will use the triangle’s index to lookup which material it should use for said triangle. Each material has an associated shader and appropriate data object. Once we know which material to use, we call the function associated to that shader and light our pixel. The heavy lifting happens inside the shader.

The primary lighting shader in cranberray is shader_microfacet. This function uses Lambert as it’s diffuse BRDF and GGX-Smith as it’s specular BRDF. It selects and blends both using multiple importance sampling.

In this post we’ll be taking a look at the magic behind the shader starting with our Lambert BRDF!

### Understanding The Lambertian BRDF

For this post, we’ll assume that there is some familiarity with the fact that the Lambert BRDF is simply the Albedo of the surface divided by $\pi$.

However, one bit I’d like to touch on is that a Lambertian reflector emits energy at a constant rate in every direction.

At least, that was how I understood a Lambertian reflector. This is incorrect. A Lambertian reflector emits energy at a constant rate in every direction per unit area.

The difference here is that the first definition describes radiant intensity and the second defines radiance. [3]

What that means is that the amount of energy emitted is proportional to the angle at which the surface is viewed. Wikipedia was quite insightful here [1].

If you imagine a rectangular Lambertian reflector of 1 meter on each side and imagine that it reflects 1 unit of radiant intensity.

Notice that in that case, our Lambertian reflector is reflecting $L = \frac{1}{1} = 1$ units of radiance.

Now, imagine that we’re looking at the reflector at an angle of 60 degrees

Notice that the perceived size of our reflector is now 0.5 meters squared. If our reflector were to emit 1 unit of radiant intensity when viewed at this angle, it would emit $L = \frac{1}{0.5} = 2$ units of radiance. Twice as much energy per unit area! Meaning that if we looked at it at an angle, it would emit the same amount of energy for less area in our field of view making it brighter.

As a result, you can imagine that when we’re looking to render a surface that looks uniform when viewed from all directions this is problematic.

A Lambertian reflector should reflect constant radiance. As a result, the energy reflected per direction by our material should be proportional to the change in size of our reflector when viewed at an angle.

I’ll save you the trigonometry and tell you that this factor is $cos(\theta)$ where $\theta$ is the zenith angle of our viewer.

As a result, you can see that to emit constant radiance, our reflector’s radiant intensity should be reduced by $cos(\theta)$ and if we do that, our reflector would emit 0.5 units of radiant intensity and as a result would emit $L = \frac{0.5}{0.5} = 1$ unit of radiance.

Now, like me, you might be thinking “If we need a cosine here to make our radiant intensity proportional to the foreshortening of our area, why don’t we multiply our emitted light by the cosine factor when rendering?”

The reason is that this cosine factor is relevant when we’re trying to calculate radiance from radiant intensity and the area being shaded. However, when we’re rendering, the result of our shading math is radiance not radiant intensity. If we wanted to instead calculate the radiant intensity, we would have to integrate across our surface.

$I = \int_A L dA$

Taking our previous surface as an example, when we’re shading it in our renderer, we’re asking “Given this irradiance, what is it’s radiance?”. In our previous example, our surface was being shaded with an irradiance of $\pi$ (conveniently skipping over why here) and as a result, our radiance was 1. If we were to ask what the radiant intensity was in any given direction, you would have to multiply the radiance by the projected area of our surface. At an angle of 60 degrees, this would be 0.5, making our radiant intensity $I = L * A = 1 * 0.5 = 0.5$.

This was a long winded way to attempt at explaining a neat and surprising (to me) property of Lambertian reflectors.

### Smith-GGX and microfacet BRFDs

Aside from our Lambert BRDF for diffuse, cranberray makes use of the Smith-GGX specular formulation. This formulation is very common and well documented. I refer the interested reader to [6] for an overview of the topic.

I won’t explain microfacet BRDFs here, but I highly recommend [6] and [9] for a derivation of the specular microfacet BRDF formula, and [8] for more info about the origins of the formula.

As presented in the heading, we’re making use of the Smith Uncorrelated function for our shadowing function and GGX for our NDF. [9]

There’s nothing particularly novel about this portion of the renderer. I recommend the interested reader see some of the linked references for information on implementing a similar shading model.

### Importance Sampling

At this point in our rendering, we have a specular BRDF and a diffuse BRDF however we now need to decide on where to sample our ray.

The simplest method for selecting a ray is to uniformly select a point on the hemisphere centered about the normal of our surface.

This works well to get started but can converge very slowly. Especially in scenarios with sharp highlights which often happens with smooth surfaces.

If we sample only a few times, we might miss all the important details.

As a result, we want to make use of something called importance sampling. Simply put, importance sampling is the idea of trying to sample the most “important” parts of a function.

I assume the reader has some familiarity with Monte Carlo estimation method. If not, I suggested going to [15]. Their treatment of importance sampling and Monte Carlo estimators is excellent.

To sample the most important parts of our scene, we want to take samples where we expect the most contributions to our image would live. We unfortunately don’t know exactly where that is, but we can guess. And to guess, we can use an approximate function to choose what direction we want to send our ray.

Let’s imagine our basic Lambert BRDF, we know that rays that are closest to our normal will contribute the most light to our illumination. As a result, we can try to send more rays towards our normal’s direction with some falloff. [16]

To select a ray, we want to convert our PDF to a CDF and randomly select a point on our CDF using a uniform random number. [13] This allows us to select a random number for the specified distribution using a random number with a uniform distribution.

Once we’ve selected our value, we can calculate the PDF and plug it into our Monte Carlo estimator.

A potential downside of selecting an approximate function is that it might not accurately reflect the illumination in the scene, causing us to increase our variance instead of reducing it. [15]

Cranberray uses the BRDF functions as the approximate representation of the illumination in the scene. This is easy to implement and also quite effective since there tends to be quite a few rays that would effectively contribute nothing at all to the image if we sampled uniformly.

For Lambert, I use the derivation from a previous post [16].

And for GGX I use the derivation found here [13].

static cv3 hemisphere_surface_random_lambert(float r1, float r2)
{
float theta = acosf(1.0f - 2.0f*r1) * 0.5f;
float cosTheta = cosf(theta);
float sinTheta = sinf(theta);
float phi = cran_tao * r2;

return (cv3) { sinTheta*cosf(phi), sinTheta*sinf(phi), cosTheta };
}

static float lambert_pdf(cv3 d, cv3 n)
{
return cv3_dot(d, n) * cran_rpi;
}

static cv3 hemisphere_surface_random_ggx_h(float r1, float r2, float a)
{
float cosTheta = sqrtf((1.0f-r1)*cf_rcp(r1*(a*a-1.0f)+1.0f));
float sinTheta = sqrtf(1.0f - cosTheta*cosTheta);
float phi = cran_tao * r2;
return (cv3) { sinTheta*cosf(phi), sinTheta*sinf(phi), cosTheta };
}

static float ggx_pdf(float roughness, float hdotn, float vdoth)
{
float t = hdotn*hdotn*roughness*roughness - (hdotn*hdotn - 1.0f);
float D = (roughness*roughness)*cf_rcp(t*t)*cran_rpi;
return D*hdotn*cf_rcp(4.0f*fabsf(vdoth));
}


### Multiple Importance Sampling and blending our BRDFs

With these importance functions, we need a way to select between either our diffuse or our specular BRDF and blending between them. This is where Multiple Importance Sampling comes in.

Multiple Importance Sampling allows us to blend our functions using weights assigned to each PDF. [14]

In cranberray, we use a simple strategy for determining if we select from either our specular BRDF or our diffuse BRDF. We use the Fresnel factor for our geometric normal and select our specular BRDF when our random number is less than the Fresnel factor and our diffuse BRDF in the other case.

float geometricFresnel = cmi_fresnel_schlick(1.0f, microfacetData.refractiveIndex, normal, viewDir);
geometricFresnel = fmaxf(geometricFresnel, microfacetData.specularTint.r * gloss); // Force how much we can reflect at a minimum
float weights[distribution_count] =
{
[distribution_lambert] = 1.0f - geometricFresnel,
[distribution_ggx] = geometricFresnel
};

bool reflected = random01f(&context->randomSeed) < weights[distribution_ggx];


Once we’ve selected our BRDF and sampled from it, we want to blend our results with our previous sampling results. This is where multiple importance sampling comes in. (See [17] for an excellent treatment on the topic)

Notice that if we imagine our scene as a simplified function.

Let’s assume this function has an area of 1.2 units.

Imagine that our function is a combination of 2 simpler functions.

$f(X) = g(X)h(X)$

Let’s imagine that we select only one of those functions as the importance function uniformly at random and we select $g(x)$.

To get our value using importance sampling, we need to divide by our PDF and the chance of selecting that PDF as our distribution (Which is 0.5 in this example).

If we were to use this single sample as our estimate using $p_g$ as our PDF and the values $g(x) = 0.1$, $h(x) = 2$ and $p_g(x) = 0.1$ we would get $y = \frac{g(x)h(x)}{0.5*p_g(x)} = \frac{0.1*2}{0.5*0.1} = \frac{0.2}{0.05} = 4$ which is a poor estimate of our area of 1.2.

If we were to use $p_h(x)$ and where $p_h(x) = 1$ as our importance function we would get $y = \frac{g(x)h(x)}{0.5*p_h(x)} = \frac{0.1*2}{0.5*1} = \frac{0.2}{0.5} = 0.4$ which is a closer estimate of our area of 1.2.(Admittedly, this example is a bit contrived)

The issue presented here (and in [17]) is that because our sample location had a low likelihood of being selected (0.1) but our second function $h(x)$ had a relatively high value, our value gets a very high value as well since $2/0.1$ is quite large. It’s important to note that if we were to continue taking samples, our estimator would still converge to 1.2.

Multiple Importance Sampling suggests a method to reduce the overall impact of these spikes to reduce the variance of our estimates by using a set of appropriate weighting functions instead of simply using our PDF directly as we just did.

The one sample model presented in [14] is what we’ll be using for this example, as it’s simpler to think about.

Instead of only dividing by our PDF, we want to apply a weighing function to our calculation.

$F = \frac{w_I(X)f(X)}{c_Ip_I(X)}$

Where $I$ represents the index of our selected PDF, $w_I$ represents the weight assigned to our PDF, $c_I$ represents the probability that we select that PDF and $p_I$ is our PDF function.

In our previous example, we did not have $w_I(X)$. This function does all the magic.

[14] presents the balance heuristic as an option for a weighting function which has the form

$w_i(x) = \frac{c_i p_i(x)}{\sum_k c_k p_k(x)}$

Notice that this is simply a weighted average of our probability density functions.

If we add this function to our single sample model we get

$F = \frac{c_i p_i(x)}{\sum_k c_k p_k(x)} \frac{f(x)}{c_ip_i(x)}$

$F = \frac{f(X)}{\sum_k c_k p_k(x)}$

If we look at our previous example using $p_g(x)$ as our PDF, we can make use of the same values here (notice that our final formulation does not divide by $p_g(x)$ individually anymore.

$c_g = 0.5$, $c_h = 0.5$, $p_g(x) = 0.1$, $g(x) = 0.1$, $p_h(x) = 1$ and $h(x) = 2$

$y = \frac{g(x)h(x)}{0.5*p_g(x)+0.5*p_h(x)}$

$y = \frac{g(x)h(x)}{0.5*p_g(x)+0.5*p_h(x)}$

$y = \frac{0.1*2}{0.5*0.1+0.5*1}$

$y = \frac{0.2}{0.55}$

$y = 0.36$

Which although is not as good as our estimate of 0.4 from earlier, it is much better than our estimate of 4 when using $p_g(x)$

We can see that this is unbiased by noticing that

$F(x) = \frac{g(x)h(x)}{c_g p_g(x) + c_h p_h(x)}$

$E[F(x)] = c_g p_g(x) \frac{g(x)h(x)}{c_g p_g(x) + c_h p_h(x)} + c_h p_h(x) \frac{g(x)h(x)}{c_g p_g(x) + c_h p_h(x)}$

$E[F(x)] = (c_g p_g(x) + c_h p_h(x)) \frac{g(x)h(x)}{c_g p_g(x) + c_h p_h(x)}$

$E[F(x)] = g(x)h(x)$

(I took some shortcuts here, I recommend reading [14] for proofs)

An interesting property of the balance heuristic is that our values are bounded.

Notice that since

$\frac{g(x)}{c_g p_g(x) + c_h p_h(x)} <= \frac{g(x)}{c_g p_g(x)}$

then

$\frac{g(x)h(x)}{c_g p_g(x) + c_h p_h(x)} <= \frac{g(x)h(x)}{c_g p_g(x)}$

as well as

$\frac{g(x)h(x)}{c_g p_g(x) + c_h p_h(x)} <= \frac{g(x)h(x)}{c_h p_h(x)}$

Where both equations on the right were the formulas that we used originally when sampling either with $p_g(x)$ and $p_h(x)$ (Where $c_g=0.5$ and $c_h = 0.5$).

Meaning that our balanced sampling is bounded by the smaller of the 2 values generated by both of our PDFs. Neat! (Note that with uniform sampling, our value would simply be 0.2)

All that to say that this is our weighting code

float sum = 0.00001f;
for (uint32_t i = 0; i < distribution_count; i++)
{
sum += weights[i] * PDFs[i];
}
weight = cf_rcp(sum);


And that’s it! That was a lot of words for a very little chunk of code.

### Conclusion

This post is definitely a large one. But it was a lot of fun to learn enough about multiple importance sampling and the Lambertian BRDF to at least convey some sense of it in this blog post. I hope you enjoyed it! Next time we’ll be taking a look at the much simpler Obj loading code in cranberray. Until next time, happy coding!

### Future Work

I’d like to make more use of a better importance sampling function for Smith-GGX as seen in [10]. I also wonder if it’s possible to only select from half vectors that reflect on the upper half of the hemisphere instead of requiring us to discard rays that would be generated in the lower half of our sphere.

I would also like to make more effective use of multiple importance sampling in the path tracer as a whole. At this moment, it is only used to combine our lambertian importance function and our GGX-Smith importance function.

## Intro

In our last post, we looked at building our BVH using SAH as our guiding heuristic. Now it’s time to look at how we intersect that BVH.

## High Level

Querying the BVH is actually quite simple. We test our ray against our parent node, if it intersects, we test the ray against both of it’s children. If the ray intersects either of it’s children, we test that children’s children. Once we reach a leaf node, we add it to our list of nodes that we’ve intersected.

This sounds perfect for recursion, but recursion never scales particularly well. Instead a queue or stack is a more effective method to represent the nodes to be tested.

There are three pieces that bring the BVH traversal together. The math library, the AABB intersection code and the (roughly) branchless intersection response code.

We’ve already seen the math library in this post, as a result, let’s take a dive into the intersection routine!

## Ray/AABB Intersection Routine

There are quite a few excellent sources on the ray/AABB intersection routine [1][2][3]. We’ll do a small dive into how we get to the final implementation.

The intersection routine is based on the slab intersection method presented in [3]. We want to define our AABB as a set of 3 slabs in each axis. A slab is simply represented by a pair of planes, the slab being the space in between them.

In order to determine if we’re intersecting the AABB, we want to determine if our ray passes through the shared intersection of each slab.

In order to determine that, we want to determine if our ray enters each slab before it exits any of the other slabs.

A simple approach to this could be:

slabEntryX = intersect(ray, startPlaneX)
slabExitX = intersect(ray, endPlaneX)
slabEntryY = intersect(ray, startPlaneY)
slabExitY = intersect(ray, endPlaneY)

if(slabEntryX is greater than slabExitY or slabEntryY is greater than slabExitX)
return missed collision

return hit collision


As a result, our core approach is to find if there is any entry value that is greater than our exit values. And if so, we’re not intersecting the slab.

To intersect a ray and a plane, we need to define our ray as:

$\vec{p} = \vec{a}*t+\vec{b}$

and our plane as

$0 = \vec{n}.\vec{p}-d$

We can then plug our ray function into our plane function to get:

$0 = \vec{n}.(\vec{a}*t+\vec{b})-d$

And solving for $t$

$d = \vec{n}.\vec{a}*t+vec{n}.\vec{b}$

$d-\vec{n}.\vec{b} = vec{n}.\vec{a}*t$

$\frac{d-\vec{n}.\vec{b}}{vec{n}.\vec{a}} = t$

Now that we have $t$ we can calculate it for each plane, since our planes are axis aligned, our intersection math gets quite simple. Here is an example for our x axis.

$\frac{d-\vec{n}.\vec{b}}{vec{n}.\vec{a}} = t$

where $n = (1, 0, 0)$

$\frac{d_x-b_x}{a_x} = t$

And repeat for each axis.

Now let’s look at our intersection code:

cv3l rayOLanes = cv3l_replicate(rayO);
cv3l invD = cv3l_replicate(cv3_rcp(rayD));
cv3l t0s = cv3l_mul(cv3l_sub(aabbMin, rayOLanes), invD);
cv3l t1s = cv3l_mul(cv3l_sub(aabbMax, rayOLanes), invD);

cv3l tsmaller = cv3l_min(t0s, t1s);
cv3l tbigger  = cv3l_max(t0s, t1s);

cfl rayMinLane = cfl_replicate(rayMin);
cfl rayMaxLane = cfl_replicate(rayMax);
cfl tmin = cfl_max(rayMinLane, cfl_max(tsmaller.x, cfl_max(tsmaller.y, tsmaller.z)));
cfl tmax = cfl_min(rayMaxLane, cfl_min(tbigger.x, cfl_min(tbigger.y, tbigger.z)));
cfl result = cfl_less(tmin, tmax);


This code looks different than our math, but it’s essence is the same.

In this code we’re testing 4 AABBs against a single ray instead of one ray against one AABB.

Notice that in this line:

cv3l t0s = cv3l_mul(cv3l_sub(aabbMin, rayOLanes), invD);


We’re implementing $\frac{d_x-b_x}{a_x} = t$ where $b_x, b_y, b_z$ is rayOLanes, invD is $\frac{1}{a_x}, \frac{1}{a_y}, \frac{1}{a_z}$ and aabbMin is $d_x, d_y, d_z$.

The next few lines:

cv3l tsmaller = cv3l_min(t0s, t1s);
cv3l tbigger = cv3l_max(t0s, t1s);


Are there to account for the situation where our ray direction is negative. If our ray direction is negative on one axis, t0 and t1 will actually be flipped. As a result, we want to simply make sure that t0 is the smaller one and t1 is the larger one. [1]

And finally,

cfl tmin = cfl_max(rayMinLane, cfl_max(tsmaller.x, cfl_max(tsmaller.y, tsmaller.z)));
cfl tmax = cfl_min(rayMaxLane, cfl_min(tbigger.x, cfl_min(tbigger.y, tbigger.z)));
cfl result = cfl_less(tmin, tmax);


These lines are calculating to see if any of our latest entrance is earlier than our earliest exit. If so, cfl_mask will set the appropriate bit for that AABB.

That’s it for our intersection routine! Now we can take a look at what we do with these results.

## Intersection Response

The previous intersection code is nicely packaged in the caabb_does_ray_intersect_lanes routine.

In our bvh traversal, the call to it looks like this:

uint32_t intersections = caabb_does_ray_intersect_lanes(rayO, rayD, rayMin, rayMax, boundMins, boundMaxs);


Where intersections hold the bits of the AABBs that intersect with our ray.

One approach to handling these bits is to simply loop through each AABB, determine if it’s bit is set and add it’s children volumes to the queue of being tested or to the final list of leaf nodes.

However, that’s no fun.

Instead it’s a lot more fun to make this code branchless using some SSE intrinsics!

Fair warning, this code can be complicated if you aren’t used to reading SSE intrinsics.

#define _MM_SHUFFLE_EPI8(i3,i2,i1,i0) _mm_set_epi8(i3*4+3,i3*4+2,i3*4+1,i3*4,i2*4+3,i2*4+2,i2*4+1,i2*4,i1*4+3,i1*4+2,i1*4+1,i1*4,i0*4+3,i0*4+2,i0*4+1,i0*4)
__m128i shuffles[16] =
{
_MM_SHUFFLE_EPI8(0,0,0,0), _MM_SHUFFLE_EPI8(0,0,0,0), _MM_SHUFFLE_EPI8(0,0,0,1), _MM_SHUFFLE_EPI8(0,0,1,0), // 0000, 0001, 0010, 0011
_MM_SHUFFLE_EPI8(0,0,0,2), _MM_SHUFFLE_EPI8(0,0,2,0), _MM_SHUFFLE_EPI8(0,0,2,1), _MM_SHUFFLE_EPI8(0,2,1,0), // 0100, 0101, 0110, 0111
_MM_SHUFFLE_EPI8(0,0,0,3), _MM_SHUFFLE_EPI8(0,0,3,0), _MM_SHUFFLE_EPI8(0,0,3,1), _MM_SHUFFLE_EPI8(0,3,1,0), // 1000, 1001, 1010, 1011
_MM_SHUFFLE_EPI8(0,0,3,2), _MM_SHUFFLE_EPI8(0,3,2,0), _MM_SHUFFLE_EPI8(0,3,2,1), _MM_SHUFFLE_EPI8(3,2,1,0)  // 1100, 1101, 1110, 1111
};

__m128i queueIndices = _mm_load_si128((__m128i*)testQueueIter);
uint32_t leafLine = bvh->count - bvh->leafCount;
{
__m128i isParent = _mm_cmplt_epi32(queueIndices, _mm_set_epi32(leafLine, leafLine, leafLine, leafLine));

}

uint32_t leafCount = __popcnt(childIndexMask);
uint32_t parentCount = __popcnt(parentIndexMask);
__m128i childIndices = _mm_shuffle_epi8(queueIndices, shuffles[childIndexMask]);
__m128i parentIndices = _mm_shuffle_epi8(queueIndices, shuffles[parentIndexMask]);

union
{
uint32_t i[4];
__m128i v;
} indexUnion;

indexUnion.v = childIndices;
for (uint32_t i = 0; i < leafCount; i++)
{
uint32_t nodeIndex = indexUnion.i[i];
candidateIter[i] = bvh->jumps[nodeIndex].leaf.index;
}
candidateIter+=leafCount;

indexUnion.v = parentIndices;
for (uint32_t i = 0; i < parentCount; i++)
{
uint32_t nodeIndex = indexUnion.i[i];
cran_assert(nodeIndex < bvh->count);
testQueueEnd[i*2] = bvh->jumps[nodeIndex].jumpIndices.left;
testQueueEnd[i*2 + 1] = bvh->jumps[nodeIndex].jumpIndices.right;
}
testQueueEnd += parentCount * 2;


Now this looks like a lot, so let’s take a look at it piece by piece. As we presented in the previous post, our leaf nodes are stored at the end of our array. This allows us to determine which nodes are leaf nodes simply by comparing their indices against that boundary. The following bit of code does exactly that:

__m128i isParent = _mm_cmplt_epi32(queueIndices, _mm_set_epi32(leafLine, leafLine, leafLine, leafLine));


The first line here simply compares our indices against our “leaf boundary”. The result of this tells us if our index is a parent or a leaf node.

Once we know which ones are our leaf nodes, we convert this to a bitmask representing the elements of the vector.

And finally, we mask out the indices that have no intersections associated with them.

The result of this operation is that we have two bitmasks that represents the parents that have intersected the ray and the leaves that have intersected the ray.

Next we want to turn this bit mask into a series of indices.

uint32_t leafCount = __popcnt(childIndexMask);
uint32_t parentCount = __popcnt(parentIndexMask);
__m128i childIndices = _mm_shuffle_epi8(queueIndices, shuffles[childIndexMask]);
__m128i parentIndices = _mm_shuffle_epi8(queueIndices, shuffles[parentIndexMask]);


The first two lines are very simple, it tells us how many leaf nodes have intersections and how many parent nodes. The next pair of lines require a little more explaining.

Right now, our queueIndices vector represents the indices to the AABBs that we’ve tested against our rays.

One approach we could implement is to simply loop through every element, test their bits and read the values if those bits are set.

However, what we can do instead is pack all of our active indices to the front of our vector allowing us to read only the ones that have reported an intersection. Reducing our branching at the cost of shuffling our data. That’s what _mm_shuffle_epi8 is doing. It’s taking a lookup table (shuffles[]) and determining where our values should be moved in the vector to pack them to the front of the vector.

Since our masks only contain 4 bits, they have a maximum of 15 values making for a very manageable look up table.

Once we have our indices packed, we simply have to read them.

union
{
uint32_t i[4];
__m128i v;
} indexUnion;

indexUnion.v = childIndices;
for (uint32_t i = 0; i < leafCount; i++)
{
uint32_t nodeIndex = indexUnion.i[i];
candidateIter[i] = bvh->jumps[nodeIndex].leaf.index;
}
candidateIter+=leafCount;

indexUnion.v = parentIndices;
for (uint32_t i = 0; i < parentCount; i++)
{
uint32_t nodeIndex = indexUnion.i[i];
cran_assert(nodeIndex < bvh->count);
testQueueEnd[i*2] = bvh->jumps[nodeIndex].jumpIndices.left;
testQueueEnd[i*2 + 1] = bvh->jumps[nodeIndex].jumpIndices.right;
}
testQueueEnd += parentCount * 2;


These parts are relatively self explanatory. We’re reading our indices and doing the appropriate work if our values are leaves or parent nodes. To understand why we’re using a union to convert our SSE vector, I recommend looking into type punning. [5]

## Conclusion

That’s it! I don’t really have any future work for this part of cranberray, I’m actually quite happy with the end result.

Next we’ll be looking at the ray tracer’s sampling strategy. Until then, happy coding!

## Intro

In our last post, we looked at our SIMD-friendly math library. In this post, we’ll be taking a look at how cranberray builds its BVH. For a primer to BVHs, I recommend going to PBRT’s treatment on the topic here.

Canberray’s BVH is built recursively as a binary tree. It starts with the top level node containing all nodes and recursively splits it into 2 children at each level. Originally, the BVH was split simply by selecting the left and right halves of the children nodes. The next approach was to split according to the median of a particular axis (typically the largest one), and now it makes use of the surface area heuristic.

## SAH

The Surface Area Heuristic or SAH is a well known strategy to build an efficient BVH that tries to minimize the number of intersection tests applied on a tree.

PBRT has an excellent treatment on the topic [2], and I’ve linked one of the original papers referencing the idea [1]. I will try to instead provide an intuitive picture of why and how SAH works.

The idea behind SAH is to discover an efficient partition of a collection that will try to minimize the cost of traversing the tree for any ray.

The SAH function is presented as:

$C_o = C_{trav} + P_A\sum C_{isect}(a_i) + P_B\sum C_{isect}(b_i)$

This cost model is based on probabilities. $C_{trav}$ is the cost of traversing the parent node to determine if any of the two children nodes intersect the ray. In cranberray, this would be the cost of testing the ray against both children AABBs and determining if it intersects either. $C_{isect}$ is the cost of testing the shape itself against the ray. In cranberray, this would be the cost of testing the triangles against the ray. Finally, $P_A$ and $P_B$ are the probabilities that the bounding volumes containing the children nodes intersect the ray.

Now, well make a slight modification and reformulate $P_A$ and $P_B$ as $P(A|C)$ and $P(B|C)$ where C is our parent node. This states that $P(A|C)$ represents the probability of the event A happening given the event C has already happened. In this context, this means that $P(A|C)$ represents the probability that we intersect our volume A given that we’ve already intersected our parent volume C.

With this change, our function is now:

$C_o = C_{trav} + P(A|C)\sum C_{isect}(a_i) + P(B|C)\sum C_{isect}(b_i)$

Now, let’s imagine that we’re a lonesome ray traveling through our BVH. We arrive at a node and we test both of it’s children to see if we intersect it. This takes us 10 units of time ($C_{trav}$). At this point we would intersect A with a probability of $P(A|C)$ and we would intersect B with a probability of $P(B|C)$. In this example, we only intersect with A, and so we test our ray against it’s contained shapes. Let’s say that A has 10 shapes, as a result, we have to pay the cost of testing the intersection with all 10 shapes. This is $\sum_{i=1}^{10} C_{isect}(a_i)$. Finally, let’s say that the cost of $C_{isect}$ is 15 for every child shape.

This gives us the final actual cost of:

$C_o = 10+sum_{i=1}^{10} 15 = 10+150 = 160$

What our original cost function is doing, is calculating the expected cost of doing this operation for any possible ray.

As a result, if we were to build with these values:

$C_{trav}=10$

$C_{isect}=15$

$P(A|C)=1$

$P(B|C)=0$

$A_n = 10$

$B_n = 0$

Then our expected cost for this node would be:

$C_o = 10 + 1*\sum_{i=1}^{10} 15 + 0*\sum_{i=1}^{0} 15 = 10+150 = 160$

Which is exactly the cost of our example above because if our A node has a probability of 1, we would always experience the scenario above.

Of course, if we were to take some more likely value such as:

$P(A|C)=0.5$

$P(B|C)=0.6$

$A_n = 10$

$B_n = 12$

$C_o = 10 + 0.5*\sum_{i=1}^{10} 15 + 0.6*\sum_{i=1}^{12} 15 = 10+0.5*150+0.6*180 = 193$

Which would be the expected value of our node. Notice that our probabilities can add up to more than or less than 1 because it is reasonable that we might be able to intersect with both volumes or none at all.

Now we need to determine how we want to define $P(A|C)$. This probability is the probability of a random ray intersecting with our volume given that we’ve intersected with our parent volume.

$P(A|C)$ is given as the surface area of our volume A divided by the surface area of our parent volume C. As a result, we can define $P(A|C) = \frac{S_A}{S_C}$ as the probability of intersecting A given that we’ve intersected C.

### Derivation In 2D

In 2 dimensions, this makes sense, as we can image a rectangle, embedded in another rectangle. The probability of a random point being placed in the child rectangle given that it is contained in the parent rectangle is equal to the area of the embedded rectangle divided by the area of the parent rectangle.

Now, instead we’ll imagine uniformally random ray instead of a random line. To select a random ray, we can select a random direction and then select a random ray along the plane defined by that direction.

With this in mind, we want to calculate the probability that our ray will intersect the children volume given that it has intersected the parent volume.

To do this, we want to calculate the average projected area of our shape.

Imagine that we’ve selected our random direction for our ray, we can see that our ray will have intersected our shape if it’s projection onto the plane intersects our random ray.

Now if we add a second volume embedded in the first volume, we can see that the probability of intersecting the first volume would be given by the projected area of the child volume divided by the projected area of the parent volume.

This example only works for a single direction. However, since we’re looking for a probability given any ray direction, we need to calculate the average of this projection across the sphere of all directions.

In 2 dimensions, the projected area of a circle is always it’s diameter. We will use this fact to test our work.

To calculate the average projected area of an arbitrary convex shape[4][5], we first want to split that shape into a series of small discrete patches.

This will be our integration domain.

Once we’ve split our shape into discrete patches, we want to calculate the average projected area of a single patch.

Now we’ll integrate across the circle of directions containing the patch and divide it by the area of our integration. This will effectively give us the average projected area for a single patch of our shape. This works because we will have calculated the projection for every single direction.

$dA_p = \frac{1}{2\pi}\int_{\Omega} |cos\theta| dA d\theta$

Integrating with an absolute value is tricky. Instead due to the symmetry of the problem, we can look at only a quarter of our circle and multiply by 4 and remove our absolute term.

$dA_p = \frac{2}{\pi} dA \int_{0}^{\frac{\pi}{2}} cos\theta d\theta$

Since $\int_{0}^{\frac{\pi}{2}} cos\theta d\theta = 1$ our expression reduces to

$dA_p = \frac{2}{\pi} dA$

Finally, we want to divide our value by 2, this is because we’ve now counted our projected area twice.

$dA_p = \frac{1}{\pi} dA$

Using this formulation, we can calculate the average projected area of our shape by adding all of our average projected areas together.

$A_p = \int_A dA_p$

$A_p = \int_A \frac{1}{\pi} dA$

$A_p = \frac{1}{\pi} A$

And there you have it, the average projected area of a 2 dimensional convex object is $\frac{1}{\pi}$ the area of said object.

Notice that if we apply this to the surface area of a circle with the formula $2\pi r$ we get $A_p = \frac{2\pi r}{\pi} = 2r$ which is our diameter which matches up with our expectation.

### Derivation In 3D

We can take the same steps in 3D.

Calculate the average projection over our sphere of directions:

$dA_p = \frac{1}{4\pi} \int_0^{2\pi} \int_0^{\pi} |cos\theta| sin\theta dA d\theta d\phi$

Integrating across the positive hemisphere and multiplying by 2

$dA_p = \frac{1}{2\pi} dA \int_0^{2\pi} \int_0^{\frac{\pi}{2}} cos\theta sin\theta d\theta d\phi$

Since $\int_0^{\frac{\pi}{2}} cos\theta sin\theta d\theta = \frac{1}{2}$

$dA_p = \frac{1}{2\pi} dA \int_0^{2\pi} \frac{1}{2} d\phi$

$dA_p = \frac{1}{4\pi} dA \int_0^{2\pi} d\phi$

$dA_p = \frac{1}{2} dA$

Finally, dividing by 2 for our double projection

$dA_p = \frac{1}{4} dA$

And plugging into our surface area calculation

$A_p = \int_A dA_p$

$A_p = \frac{1}{4} \int_A dA$

$A_p = \frac{1}{4} A$

### Putting It Together

Finally we can see that our average projected area in 3D is $\frac{1}{4}$ it’s surface area.

To calculate our probability, we simply want to divide our parent’s projected area (C) divided by our child’s projected area (A)

$P(A|C) = \frac{A_p}{C_p}$

$P(A|C) = \frac{0.25*A}{0.25*C}$

$P(A|C) = \frac{A}{C}$

Where A and C is the surface area of our volumes. And voila, that’s how we got that original formula.

## In Depth View

Now that we have all the pieces, we can take a look at the construction of our BVH.

Cranberray builds it’s BVH from the top down. Starting from a containing bounding volume and splitting it in 2 recursively.

Cranberray keeps a queue of bounding volumes to process as a ring buffer. This makes management of front popping and back pushing very simple but makes resizing the queue trickier. As a result, Cranberray simply allocates a large buffer. We could likely allocate the maximum possible number of elements in the ring buffer instead (something along the lines of $2n-1$ where n is the next largest power of 2)

Cranberray then selects the axis with the widest breadth of centroids. The code for this looks like so:

cv2 axisSpan[3];
for (uint32_t axis = 0; axis < 3; axis++)
{
for (uint32_t i = 0; i < count; i++)
{
axisSpan[axis].x = fminf(axisSpan[axis].x, caabb_centroid(start[i].bound, axis));
axisSpan[axis].y = fmaxf(axisSpan[axis].y, caabb_centroid(start[i].bound, axis));
}
}

uint32_t axis;
if (axisSpan[0].y - axisSpan[0].x > axisSpan[1].y - axisSpan[1].x && axisSpan[0].y - axisSpan[0].x > axisSpan[2].y - axisSpan[2].x)
{
axis = 0;
}
else if (axisSpan[1].y - axisSpan[1].x > axisSpan[2].y - axisSpan[2].x)
{
axis = 1;
}
else
{
axis = 2;
}


Once we’ve selected our axis, we split our axis in 12 distinct buckets as. (See PBRT [2] for more info on this approach)

We then calculate the cost of each seperation by adding up all the volumes on the left of the seperation and all the buckets on the right of the seperation.

We then store the cost of each seperation and select the seperation with the minimal cost as our split.

We then continue in this recursion until we’ve run out of items in our queue. (When we’ve partitioned all of our child volumes into leaf nodes).

Finally, our BVH is restructured somewhat for an improvement in memory usage.

Our BVH is stored in 2 arrays, a “jump index” array and a bounds array. This allows us to load the bounds array without having to load the jump indices into memory until we absolutely need them.

We read from our bounds memory much more frequently than our jump memory, as a result, splitting them allows us to make more effective use of our caches.

Our BVH structure looks like this:


typedef struct
{
union
{
struct
{
uint32_t left;
uint32_t right;
} jumpIndices;

struct
{
uint32_t index;
} leaf;
};
} bvh_jump_t;

typedef struct
{
caabb* bounds;
bvh_jump_t* jumps;
uint32_t count;
uint32_t leafCount;
} bvh_t;



The final special format of our BVH is that all the leaf nodes in our tree are stored at the end of our array. This allows us to test if a node is a leaf node by simply comparing the index in our array against the size of our array minus the number of leaves contained in the tree. This allows us to use data that we’ve already loaded into memory instead of requiring us to load extra data to use in our branch introducing a data dependency.

You can find the source for BVH construction here: https://github.com/AlexSabourinDev/cranberries/blob/5fe9c25e1df23d558b7ef8b5475717d9e67a19fc/cranberray.c#L963

We’ll be taking a look at the BVH traversal next. Until then, happy coding!

## Future Work

The primary addition to this BVH construction algorithm would likely be to look into parallelizing it’s construction. [6]

## Introduction

Recently, I’ve been wrestling with attempting to define the mathematics behind the illumination of a triangular area light. Although I have not succeeded yet, I figured I would chronicle my recent attempts. Here goes!

## Premise

I have recently been attempting to improve my understanding of perspective projection. This included a variety of topics such as deriving a perspective projection matrix and understanding interpolation of vertex shader outputs in perspective. However, one topic that evaded me, was the surprising result that depth is interpolated as 1/z instead of z.