Saturday, May 12, 2018

Normal map filtering using vMF (part 3)

$$ \newcommand{\vv}{\mathbf{v}} \newcommand{\rv}{\mathbf{r}} \newcommand{\muv}{\boldsymbol\mu} \newcommand{\omegav}{\boldsymbol\omega} \newcommand{\mudotv}{\muv\cdot\vv} $$ What can we use this for? One example of a place where distributions are summed up is in normal map and roughness filtering. Normal and roughness maps are textures describing the distribution of microfacet normals. The normal of the normal map is the mean of the distribution and the roughness describes the width of the distribution. We can fit our chosen NDF with a vMF by finding a mapping from roughness to sharpness $\lambda$.

This mapping for Beckmann is given by [1] as: \begin{equation} \lambda \approx \frac{2}{\alpha^2} \label{eq:roughness_to_lambda} \end{equation} and following my previous post about specular models we can use the $\alpha$ from any of those distributions in this equation for a reasonable approximation.

Once you have vMFs we can sum or filter them in $\rv$ form. Then we can turn it back to normal and roughness by inverting the function: \begin{equation} \alpha \approx \sqrt{\frac{2}{\lambda}} \label{eq:lambda_to_roughness} \end{equation} We must be careful with floating point precision and divide by zero though. Instead of calculating $\lambda$ we can instead calculate its reciprocal which avoids multiple places where a divide by nearly zero can happen.

// Convert normal and roughness to r form
float InvLambda = 0.5 * Alpha*Alpha;
float exp2L = exp( -2.0 / InvLambda );
float CothLambda = InvLambda > 0.1 ? (1 + exp2L) / (1 - exp2L) : 1;
float3 r = ( CothLambda - InvLambda ) * Normal;

// Filter in r form

// Convert back to normal and roughness
float r2 = clamp( dot(r,r), 1e-8, 1 );
InvLambda = rsqrt( r2 ) * ( 1 - r2 ) / ( 3 - r2 );
Alpha = sqrt( 2 * InvLambda );
Normal = normalize(r);

How does this compare to the common approaches? The first to do something like this was Toksvig [2] which follows similar logic with vector length corresponding with gloss and uses properties of Gaussian distributions but not SGs exactly. LEAN mapping [3] is based on Gaussians as well but planar distributions, not spherical. The approach I just explained should in theory work just as well with object space normals.

Even though it was part of the original approach the common way to use "Toksvig" filtering (including UE4's implementation) is to find the normal variance and increase the roughness by it. There is no influence from the roughness on the normals when doing that and there should be. The correct way will affect how the normals are filtered. A smooth normal should have more weight in the filter than a rough normal.

vMF has been used for this task before in [5] and later [6]. There is a major difference from our approach in that Frequency domain normal map filtering relies on convolving instead of averaging. It finds the vMF for the distribution of normals over the filter region. It then convolves the normal and roughness by that vMF. But what is a convolution?


Graphics programmers know of convolutions like blurring. It sort of spreads data out right? What does it mean mathematically though? A convolution of one function by another creates a new function that is equivalent to the integral of the function being convolved multiplied by a the convolving function translated to that point.

Think of a blur kernel with weights per tap. That kernel center is translated to the pixel in the image that we write the blur result to. Each tap of the kernel is a value from the blur function. We multiply those kernel values by the image that is being convolved. All of those samples are then added together. Now usually a blur doesn't have infinite support or every pixel of the image would need to be sampled but the only reason that doesn't need to happen is because the convolving function, ie the blur kernel, is zero past a certain distance from the center of the function. Otherwise the integral needs to cover the entire domain. In the 1D case that means from negative to positive infinity. In the case of a sphere that means over the entire surface of the sphere.

This symbolically looks like this for 1D: \begin{equation} (f * g) (x) = \int_{-\infty}^\infty f(t) g(x-t)\,dt \end{equation} We now have the definition but why would we want to convolve a function besides image blurring? A convolution of one function by another creates a new function that when evaluated is equal to if the both functions were multiplied together and integrated at that translated point. Think of this like precalculating the multiplication and integration of those functions for any translated point. The integral of the product is done ahead of time and now we can evaluate it for any translation.

This is exactly the use case for preconvolving environment maps by the reflected GGX distribution. GGX is the convolving function, the environment map is the function being convolved, the reflection vector direction used to sample the preconvolved environment map is the "translation". SGs are very simple to multiply and integrate as we have already seen so precomputing it often doesn't save much. Convolving does have its uses though so let's see how to do it.

Convolving SGs

The convolution of two SGs is not closed in the SG basis, meaning it does not result in exactly a SG. Fortunately it can be well approximated by one. [7] gave a simple approximation that is fairly accurate so long as the lobe sharpnesses aren't very low: \begin{equation} \begin{aligned} \left(G_1 * G_2\right) \left( \vv \right) &= \int_{S^2} G_1(\omegav) G_2\left( \omegav; \vv, \lambda_2, a_2 \right) \,d\omegav \\ &\approx G \left( \vv; \muv_1, \frac{\lambda_1 \lambda_2}{\lambda_1 + \lambda_2}, 2\pi\frac{a_1 a_2}{\lambda_1 + \lambda_2} \right) \end{aligned} \label{eq:convolve_sg} \end{equation} The first line of the equation above may shed more light on how we can use this if it isn't clear already. This is identical to the inner product but with $\muv_2$ replaced with a free parameter.

For the case of normal map filtering we don't care about amplitude. We want a normalized SG. That means for this case the only part that matters is the convolved $\lambda'$: \begin{equation} \lambda' = \frac{\lambda_1 \lambda_2}{\lambda_1 + \lambda_2} \end{equation} We can ignore the rest of eq \eqref{eq:convolve_sg} for the moment. If we replace $\lambda$ everywhere with $\alpha$ using eq \eqref{eq:lambda_to_roughness} we get a nice simple equation: \begin{equation} \alpha' = \sqrt{ \alpha_1^2 + \alpha_2^2 } \end{equation} Leave $\lambda$ in for one of them and we get: \begin{equation} \alpha' = \sqrt{ \alpha^2 + \frac{2}{\lambda} } \label{eq:alpha_prime} \end{equation} which looks just like what [6] used except for the 2 factor. I believe this is a mistake in their course notes. In equation (37) of their notes they have it as 1/2 and in the code sample it is 1. I think the confusion comes from the Frequency Domain Normal Map Filtering paper working with Torrance Sparrow and not Cook Torrance, and $\sigma \neq \alpha$. Overall it means less roughness from normal variance. In my tests using eq \eqref{eq:alpha_prime} that we just derived looks closer to Toksvig results. Otherwise the range is off and less rough. MJP uses the same 2/a^2 for SG sharpness in his blog post so we don't disagree there.

As a gut check if $\alpha=0$ and all final roughness comes from normal variation then $\alpha'=\sqrt{\frac{2}{\lambda}}$ which is what we established in eq \eqref{eq:lambda_to_roughness}. If there is no normal variation then this equation explodes but if you calculate InvLambda like I did in the code snippet the second term becomes zero and $\alpha'=\alpha$ which is what we want.

Next up, converting from SH to SG (coming soon).


[1] Wang et al. 2007, "All-Frequency Rendering of Dynamic, Spatially-Varying Reflectance"
[2] Toksvig 2004, "Mipmapping Normal Maps"
[3] Olano et al. 2010, "LEAN Mapping"
[4] Hill 2011, "Specular Showdown in the Wild West"
[5] Han et al. 2007, "Frequency Domain Normal Map Filtering"
[6] Neubelt et al. 2013, "Crafting a Next-Gen Material Pipeline for The Order: 1886"
[7] Iwasaki 2012, "Interactive Bi-scale Editing of Highly Glossy Materials"

von Mises-Fisher (part 2)

$$ \newcommand{\vv}{\mathbf{v}} \newcommand{\rv}{\mathbf{r}} \newcommand{\muv}{\boldsymbol\mu} \newcommand{\mudotv}{\muv\cdot\vv} $$ A normalized SG has the same equation as the probability distribution function for a von Mises-Fisher (vMF) distribution on the 3 dimensional sphere. This affords us a few more tools and applications to work with. A vMF distribution can be defined for any dimension. I'll focus on 3D here because it is the most widely usable for computer graphics and simplifies discussion. Because a vMF does not have a free amplitude parameter it is written as: \begin{equation} \begin{aligned} V(\vv;\muv,\lambda) = \frac{\lambda}{ 2\pi \left( 1 - e^{-2 \lambda} \right) } e^{\lambda(\mudotv - 1)} \end{aligned} \label{eq:vmf} \end{equation} The more common form you will likely see in literature is this: \begin{equation} \begin{aligned} V(\vv;\muv,\lambda) = \frac{\lambda}{ 4\pi \sinh(\lambda) } e^{\lambda(\mudotv)} \end{aligned} \label{eq:vmf_sinh} \end{equation} which is equivalent due to the identity \begin{equation} \begin{aligned} \sinh(x) = \frac{ 1 - e^{-2x} }{ 2e^{-x} } \end{aligned} \label{eq:sinh_identity} \end{equation} The form in eq \eqref{eq:vmf} is more numerically stable so should be used in practice as explained by [2].

Compare the equation for a vMF to the equation for a SG and it is easy to see that: \begin{equation} \begin{aligned} V(\vv;\muv,\lambda) = G\left( \vv; \muv, \lambda, \frac{\lambda}{ 2\pi \left( 1 - e^{-2 \lambda} \right) } \right) \end{aligned} \label{eq:vmf_to_sg} \end{equation} That means a vMF is equivalent to a normalized SG and by moving terms from one side to the other we can show that a SG is equivalent to a scaled vMF. \begin{equation} \begin{aligned} G\left( \vv; \muv, \lambda, a \right) = \frac{2\pi a}{\lambda} \left( 1 - e^{-2 \lambda} \right) V(\vv;\muv,\lambda) \end{aligned} \label{eq:sg_to_vmf} \end{equation}

Fitting a vMF distribution to data

Fitting a vMF distribution to directions or points on a sphere is a very similar process as fitting a normal distribution to points on a line. In the case of a normal distribution, one calculates the mean and variance of the data set and then chooses a normal distribution with the same mean and variance as the best fit to the data.

For the vMF distribution the mean direction and spherical variance are used. Calculating these properties for a set of directions is simple. \begin{equation} \begin{aligned} \rv = \frac{1}{n}\sum_{i=1}^{n} \textbf{x}_i \end{aligned} \label{eq:r_average} \end{equation} where $\textbf{x}_1, \textbf{x}_2, ..., \textbf{x}_n$ are a set of unit vectors.

Often values are associated with these directions. So instead taking a simple average we can take a weighted average. \begin{equation} \begin{aligned} \rv = \frac{\sum_{i=1}^{n} \textbf{x}_i w_i}{\sum_{i=1}^{n} w_i} \end{aligned} \label{eq:r_weighted_average} \end{equation} We have the two properties, the mean direction $\muv = \frac{\rv}{\|\rv\|}$ and the spherical variance $\sigma^2 = 1 - \|\rv\|$. To fit a vMF distribution to the data we need to know what these properties are for the vMF distribution. Since the vMF distribution is convex, circularly symmetric about its axis, and is max in the direction of $\muv$, it is fairly obvious that the mean direction will be $\muv$ so I won't derive that here.

The spherical variance $\sigma^2$ on the other hand is a bit more involved. Because we already know the direction of $\rv$ is $\muv$ we can simplify this calculation to the integral of the projection of the function onto $\muv$. \begin{equation} \begin{split} \|\rv\| &= \int_{S^2} V(\vv;\muv,\lambda) (\mudotv) d\vv \\ &= \frac{\lambda}{ 4\pi \sinh(\lambda) } \int_{S^2} e^{\lambda(\mudotv)} (\mudotv) d\vv \\ \end{split} \end{equation} Because the integral over the sphere is rotation-invariant we will replace $\muv$ with the x-axis. \begin{equation} \begin{split} &= \frac{\lambda}{ 4\pi \sinh(\lambda) } \int_{0}^{2 \pi} \int_{0}^{\pi} e^{\lambda\cos\theta} \cos\theta\sin\theta d\theta d\phi \\ &= \frac{\lambda}{ 4\pi \sinh(\lambda) } 2 \pi \int_{0}^{\pi} e^{\lambda\cos\theta} \cos\theta\sin\theta d\theta \\ \end{split} \end{equation} Substituting $t=-\cos\theta$ and $dt=\sin\theta d\theta$ \begin{equation} \begin{split} &= \frac{\lambda}{ 2 \sinh(\lambda) } \int_{-1}^{1} -t e^{-\lambda t} dt \\ &= \frac{\lambda}{ 2 \sinh(\lambda) } \left( \frac{ 2 \lambda \cosh(\lambda) - 2 \sinh(\lambda) }{ \lambda^2 } \right) \\ &= \frac{\cosh(\lambda)}{ \sinh(\lambda) } - \frac{\sinh(\lambda)}{ \lambda \sinh(\lambda) } \\ \end{split} \end{equation} Arriving in its final form \begin{equation} \|\rv\| = \coth(\lambda)-\frac{1}{\lambda} \label{eq:r_length} \end{equation} Although simple in form, this function unfortunately isn't invertible. [1] provides an approximation which is close enough for our purposes. \begin{equation} \begin{aligned} \lambda &= \|\rv\| \frac{ 3 - \|\rv\|^2}{1 - \|\rv\|^2} \end{aligned} \end{equation} Now that we have a way to calculate the mean and spherical variance for a data set and we know the corresponding vMF mean and spherical variance, we can fit a vMF to the data set.

Using eq \eqref{eq:r_weighted_average} to calculate $\rv$, the vMF fit to that data is \begin{equation} V\left( \vv; \frac{\rv}{\|\rv\|},\|\rv\| \frac{ 3 - \|\rv\|^2}{1 - \|\rv\|^2} \right) \label{eq:r_to_vmf} \end{equation}
Going the other direction from $V(\vv;\muv,\lambda)$ form to $\rv$ form using eq \eqref{eq:r_length} is this: \begin{equation} \rv = \left( \coth(\lambda)-\frac{1}{\lambda} \right) \muv \label{eq:vmf_to_r} \end{equation}

Addition of SGs

We now have a way to convert to and from $\rv$ form. $\rv$ is linearly filterable as shown in how it was originally defined in eq \eqref{eq:r_weighted_average}. This means if our vMF functions are representing a spherical distribution of something then a weighted sum of those distributions can be approximately fit by another vMF. In other words we can approximate the resulting distribution by converting to $\rv$ form, filtering, and then converting back to traditional $V(\vv;\muv,\lambda)$ form.

By using the weighted average eq \eqref{eq:r_weighted_average} we can apply this concept to non normalized SGs too. This allows us to not just filter (ie sum with a total weight of 1) but add as well. A non-normalized SG as shown in eq \eqref{eq:sg_to_vmf} is a scaled vMF. We can use this scale as the weight when summing and use the total weight as the final scale for the summed SG.

This is the $\rv$ form for $G(\vv;\muv,\lambda, a)$. It includes an additional weight value you can think of like the energy this SG is adding to the sum: \begin{equation} \begin{aligned} \rv_i &= \left( \coth(\lambda_i)-\frac{1}{\lambda_i} \right) \muv_i \\ w_i &= \frac{2\pi a_i}{\lambda_i} \left( 1 - e^{-2 \lambda_i} \right) \\ \end{aligned} \end{equation} This weight is of course used in the weighted sum \begin{equation} \begin{aligned} \rv &= \frac{\sum_{i=1}^{n} \rv_i w_i}{\sum_{i=1}^{n} w_i} \\ w &= \sum_{i=1}^{n} w_i \\ \end{aligned} \end{equation} Using eq \eqref{eq:r_to_vmf} and eq \eqref{eq:vmf_to_sg} we can convert back to a scaled vMF and finally to a SG in $G(\vv;\muv,\lambda, a)$ form: \begin{equation} \begin{aligned} G\left( \vv; \muv, \lambda, a \right) &= w V(\vv;\muv,\lambda) \\ &= G\left( \vv; \muv, \lambda, w \frac{\lambda}{ 2\pi \left( 1 - e^{-2 \lambda} \right) } \right) \end{aligned} \end{equation} While addition and filtering are approximate they can be useful. The accuracy of the result is very dependent on the angle between the $\mu$ vectors or lobe axii. Adding sharp lobes pointed in different directions will result in a single wide lobe.

Next, what we can use this for:
Normal map filtering using vMF


[1] Banerjee et al. 2005, "Clustering on the Unit Hypersphere using von Mises-Fisher Distributions"
[2] Jakob 2012, Numerically stable sampling of the von Mises Fisher distribution on S2 (and other tricks)"

Spherical Gaussians (part 1)

$$ \newcommand{\vv}{\mathbf{v}} \newcommand{\rv}{\mathbf{r}} \newcommand{\muv}{\boldsymbol\mu} \newcommand{\mudotv}{\muv\cdot\vv} $$ A Spherical Gaussian (SG) is a function of unit vector $\vv$ and is defined as \begin{equation} G(\vv;\muv,\lambda, a) = a e^{\lambda(\mudotv - 1)} \end{equation} where unit vector $\muv$, scalar $\lambda$, and scalar $a$ represent the lobe axis, lobe sharpness, and lobe amplitude of the SG, respectively.

The formula can be read as evaluating a SG in the direction of $\vv$ where the SG has the parameters of $\muv,\lambda, a$. An abbreviated notation $G(\vv)$ can be used instead when the parameters can be assumed. Often the more verbose notation is used to assign values to the parameters.

SGs have a number of nice properties including simple equations for a number of common operations.

Product of Two SGs

The product of two SG's can be represented exactly as another SG. This product is sometimes referred to as the vector product. This formula was first properly given in [1] (it was shown earlier but in an non-normalized form).

Let $\lambda_m = \lambda_1 + \lambda_2$ and let $\muv_m = \frac{\lambda_1\muv_1 + \lambda_2\muv_2}{\lambda_1 + \lambda_2}$, then \begin{equation} \begin{split} G_1(\vv)G_2(\vv) = G\left(\vv; \frac{\muv_m}{\|\muv_m\|}, \lambda_m\|\muv_m\|, a_1 a_2 e^{\lambda_m\left(\|\muv_m\| - 1\right)}\right) \end{split} \label{eq:sg_product} \end{equation}

Raising to a power

Given that the product of two SGs is another SG it shouldn't be much of a surprise that a SG raised to a power can be expressed exactly as another SG: \begin{equation} \begin{aligned} G(\vv)^n &= G(\vv; \muv,n\lambda, a^n) \end{aligned} \label{eq:sg_power} \end{equation}

Integration Over The Sphere

The integral of a SG over the sphere has a closed form solution.

[2] showed that the integral was: \begin{equation} \int_{S^2}G(\vv) d\vv = 2 \pi \frac{a}{\lambda} \left( 1 - e^{-2\lambda} \right) \label{eq:sg_integral} \end{equation}

Inner product

The inner product is defined as the integral over the sphere of the product of two SGs. We can already find the product of two SGs and integrate over a sphere. Putting those together we have: \begin{equation} \int_{S^2}G_1(\vv) G_2(\vv) d\vv = \frac{4 \pi a_0 a_1}{e^{\lambda_m}} \frac{ \sinh\left(\|\muv_m\| \right) }{ \|\muv_m\| } \label{eq:sg_inner_product_sinh} \end{equation} This equation has numerical precision issues when evaluated with floating point arithmetic. An alternative form which is more stable is the following: \begin{equation} \int_{S^2}G_1(\textbf{v}) G_2(\textbf{v}) d\textbf{v} = 2 \pi a_0 a_1 \frac{ e^{ \|\boldsymbol\mu_m\| - \lambda_m } - e^{ -\|\boldsymbol\mu_m\| - \lambda_m } }{ \|\boldsymbol\mu_m\| } \label{eq:sg_inner_product_exp} \end{equation}


Although there are other definitions for normalization I use the term to mean having an integral over the sphere equal to 1. Normalizing a SG is a simple matter of dividing it by its integral over the sphere. \begin{equation} \begin{aligned} \frac{ G(\vv) }{ \int_{S^2}G(\vv) d\vv } = G\left( \vv; \muv, \lambda, \frac{\lambda}{ 2\pi \left( 1 - e^{-2 \lambda} \right) } \right) \end{aligned} \label{eq:sg_normalized} \end{equation} Notice that the original $a$ parameter canceled out. Instead lobe amplitude is derived purely from the lobe sharpness $\lambda$.

These are all the common operations that have closed form solutions. So far nothing new here but hopefully it is helpful to have all these equations in a centralized place for reference. I didn't include derivations for any of these formulas. If readers think that would be useful to see maybe those could be added at a later date.

Special thanks to David Neubelt. Although this has been heavily modified from what we previously had I'm sure his touch is still present.

Now on to some less well covered concepts.
von Mises-Fisher (part 2)


[1] Wang et al. 2007, "All-Frequency Rendering of Dynamic, Spatially-Varying Reflectance"
[2] Tsai et al. 2006, "All-Frequency Precomputed Radiance Transfer using Spherical Radial Basis Functions and Clustered Tensor Approximation"

Spherical Gaussian series

Intro and backstory

About 4 years ago now I ran into Spherical Gaussian (SG) math in a few different publications in a row, enough that it triggered the pattern detection in my brain. All were using SGs to approximate specular lobes. I remember feeling very similar 10 years prior when Spherical Harmonics were starting to become all the rage. Back with SH I noticed it fairly early, primarily from Tom Forsyth's slides on the topic and took the time to dig into the math and make sure I had this new useful thing in my toolbox. Doing so has proven to be well worth the time. I decided that I should do the same again and learn SGs and related math, in particular to build up a toolbox of operations I can do with them. Maybe it would prove as useful as SH has.

In the years since I'd say it has certainly been worth the effort. I don't think I can say it has proven as useful as SH has been to computer graphics but it was still worth learning. I intended to write up what I had found and share the toolbox of equations compiled in a centralized place at the very least. Unfortunately laziness and procrastination got in the way.

Also scope creep. About 3 years ago I mentioned to David Neubelt that I intended to write this up and he too had done a lot of work with SGs at Ready at Dawn so we decided we'd collaborate and write a joint paper and submit it to JCGT. The intention was to make something similar to Stupid Spherical Harmonics (SH) Tricks but for SG. That scope and seriousness is much larger than a simple single author blog post. We worked on derivations to all the formulas, wanted to have quality solutions to cube map fitting, multiple use cases proven in production, and a ton of other things to make it an exhaustive, professional, and ultimately great paper. This was much more than I ever intended as a simple blog post. I still think that paper we had in mind would be great to exist but the actual end result is it bloated the expectation of what either of us had the bandwidth or maybe the attention span to complete and after a couple of months of work the unfinished paper regretfully stagnated.

A year went by and eventually MJP of Ready at Dawn did in fact do a blog series write up on Spherical Gaussians. It is excellent and I suggest you read it before continuing if you haven't already. There are still some things I intended to cover that he did not as well as things he did that I never have done nor planned to cover so I think these should compliment each other well.

It is a royal shame I have not posted this in the 3+ years since I intended to. I had even promised folks publicly a write up was coming and then didn't deliver. I haven't posted anything on this blog since then in fact. I hope to do better and hopefully finally getting this out will unclog the pipes.

Without further ado,