This week at work, there was a discussion about games that had great vertigo inducing moments. The conversation was dominated by AAA titles from the last couple of years, such as Assassin’s Creed, Fallout 3, and especially Uncharted 2. For me though, none of them match some of the levels from Tomb Raider. Not the any of the recent games (although I do love Crystal Dynamics’ take on the series), but the original, from the now sadly defunct Core Design. You know, back when Lara steered like a tank and advertised Lucozade.

My favourite moment from the game occurs around a third of the way into St Francis’ Folly, where you run up some stairs, turn a corner and are confronted by this:

Holy shit! You can’t see the floor!

I very nearly soiled myself as a young lad when I saw that; it made such an impression on me that I still to this day dig out my copy of Tomb Raider every couple of years and play it through.

Thoughts of St. Francis’ Folly prompted me to try knocking together a simple Tomb Raider level viewer of my own. After all, the assets couldn’t be that complicated, so how hard could it be? Not hard at all as it turns out, thanks to some excellent documentation of the file format put together years ago by dedicated fans.

Parsing the first part of the level pack, extracting a mesh and getting it up on screen took surprisingly little time. Here it is. It certainly looks like something. Perhaps it’s a rock?

Skipping through a few more meshes that didn’t really look like anything much, I finally found something recognisable, an upside-down pistol.

It was at this point I realised that Tomb Raider uses a slightly unusual coordinate system; X and Z form the horizontal plane, but positive Y points down. After a bit of Y flipping and winding order reversal, things looked a little better. Here’s some shotgun ammo.

It’s interesting to see how differently meshes were put together 15 years ago; they’re made up of a mixture of textured and untextured quads and triangles, with flat shaded quads being used wherever possible. I did similar things back when I used to play with my Net Yaroze in my spare time (the embarrassing fruits of my labours have been thoughtfully uploaded by someone for posterity).

In order to make more sense out the meshes, I moved onto texture extraction. In the first Tomb Raider, one 256-entry colour palette is used for all textures in a particular level. Each level uses around ten 256 by 256 texture atlases. Here’s texture atlas #7 from level one. I’d like to draw your attention to the bottom right corner, where if you look closely, you’ll find a couple of pixelated nipples.

Anyway, after hooking up the textures and taking another look at the meshes, it turned out that the first mesh was in fact Lara’s bum.

The next few meshes in the level pack are all the bits and pieces that make up Lara’s body. Skinning still wasn’t commonplace in those days, so each body part is a separate mesh. Incidentally, Lara’s forehead is much bigger than I remember.


Once textured meshes were rendering correctly, it was time to move onto the world geometry. Each level in Tomb Raider is made up of a number of rooms, connected via portals. Rooms are made up of square sectors, 1024 world units in size (since everything was fixed point back then). Each sector stores only one floor height and one ceiling height, which means that if a level designer wanted to put overhangs into a level, they had to stack multiple rooms on top of each other. Given this limitation, the complexity of some of the levels in the game they managed to put together is amazing.

My first attempts at rendering a whole world were somewhat less that successful.

World geometry vertex positions are stored as 16bit XYZ triples, which are defined relative to a per-room origin. According to the documentation, the room positions are defined in world space, but even taking that into account I couldn’t get the rooms to fit together properly. Instead, since rooms are all connected via portals, it was easier to traverse the portal graph and stitch rooms together based on the vertex positions of the connecting portals. For the most part, this worked very well.

Update: I was two bytes off when reading the room position, the rooms line up just fine now.

After fixing up the baked vertex lighting and adding a little depth-based fog, I had something resembling a Tomb Raider level.

Here’s The Lost Valley. Any minute now, a T-Rex is going to come stomping round the corner.

And here’s the first stage of Natla’s Mines. It’s huge!

There’s still a whole bunch of features that I could add in: sprites, static meshes, dynamic meshes, animated textures to name just a few, but I’m pretty pleased with how far it’s come with just a couple of evening’s work. So huge thanks go to those folks who figured all this out over a decade ago.

Spherical harmonics, WTF?

April 14, 2011

I never really “got” spherical harmonics, there was something about them that just didn’t click for me. A little late to the party, I spent a few evenings recently reading over all the introductory papers I could find. Several times. This post is mostly just a brain dump, made in an effort to get everything straighted out in my head. I did stumble across a few curiosities though…

Real valued spherical harmonics can be defined as:
Y^m_l(\theta, \phi) = \Phi^m(\phi) \, N^{|m|}_l \, P^{|m|}_l(cos\,\theta)

P^m_l are the associated Legendre polynomials:
P^0_0(x) = 1
P^0_1(x) = x
P^1_1(x) = -\sqrt{1-x^2}
P^0_2(x) = \frac{1}{2}(3x^2-1)
P^1_2(x) = -3x\sqrt{1-x^2}
P^2_2(x) = 3(1-x^2)

\Phi^m(x) = \begin{cases}  \sqrt{2} cos(mx), & m > 0 \\  1, & m = 0 \\  \sqrt{2} sin(|m|x), & m < 0  \end{cases}

N^m_l = \sqrt{ \frac{2l+1}{4\pi} \, \frac{(l-m)!}{(l+m)!}}

Assuming points on a unit sphere are defined in Cartesian coordinates as:
x = sin\theta \, cos\phi
y = sin\theta \, sin\phi
z = cos\theta

Then the first three bands of the SH basis are simply:
Y^0_0(\theta, \phi) = \sqrt{\frac{1}{4\pi}}
Y^{-1}_1(\theta, \phi) = -\sqrt{\frac{3}{4\pi}} y
Y^0_1(\theta, \phi) = \sqrt{\frac{3}{4\pi}} z
Y^1_1(\theta, \phi) = -\sqrt{\frac{3}{4\pi}} x
Y^{-2}_2(\theta, \phi) = \sqrt{\frac{15}{4\pi}} xy
Y^{-1}_2(\theta, \phi) = -\sqrt{\frac{15}{4\pi}} yz
Y^0_2(\theta, \phi) = \sqrt{\frac{5}{16\pi}} (3z^2-1)
Y^1_2(\theta, \phi) = -\sqrt{\frac{15}{4\pi}} zx
Y^2_2(\theta, \phi) = \sqrt{\frac{15}{16\pi}} (x^2-y^2)

Note the change in sign of odd m harmonics, which is consistent with the above definitions of x, y, z and P. In many sources the basis function constants are all positive, which can be explained by assuming that they’re defined using the Condon-Shortley phase. That took me a while to figure out.

Projecting incident radiance L into the SH basis is done using the following integral:
L^m_l = \int^{2\pi}_0 \int^{\pi}_0 \, L(\theta, \phi) \, Y^m_l(\theta, \phi) \, sin(\theta) d\theta d\phi

This is actually a spectacularly bad approximation for low numbers of SH bands. For example, here’s Paul Debevec’s light probe of Grace cathedral:

And here’s that same probe’s projection into three SH bands (negative values have been clamped to zero):


Fortunately, while spherical harmonics aren’t generally good at representing incident radiance, they totally kick arse at representing irradiance. (Very roughly speaking, incident radiance is the the amount of light falling on a surface from a particular direction, while irradiance is the total sum of light falling on a surface from all directions.)

In SH form the conversion from radiance L to irradiance E is marvelously simple:
E^m_l = \hat{A}_l \, L^m_l

The definition of A isn’t exactly straight forward, but luckily smart people have already done the hard work for us:
\hat{A}_0 = 3.141593
\hat{A}_1 = 2.094395
\hat{A}_2 = 0.785398
\hat{A}_3 = 0
\hat{A}_4 = -0.130900
\hat{A}_5 = 0
\hat{A}_6 = 0.049087

The fact that terms after 2 fall off very quickly is what makes it possible to approximate irradiance fairly accurately with only three bands.

Given a set of spherical harmonic irradiance coefficients, the diffuse illumination for a particular direction is calculated by:
E(\theta, \phi) = \sum_{l, m} \hat{A}_l \, L^m_l Y^m_l(\theta, \phi)

Condon-Shortley phase aside, something else that confused me were the results from An Efficient Representation for Irradiance Environment Maps. As far as I can tell, the gamma is not correct for some (possibly all) of the images in that paper, which made comparing results from my own code frustrating. The problems are compounded by the fact that the authors chose to apply some undefined tone mapping operator to some images, but not others.

Here’s the Grace cathedral probe again, this time with an exposure of -2.5 stops:

Below is the result I got from performing a diffuse convolution of the probe using Monte Carlo integration and 1024 samples per pixel (I was too impatient to wait for a brute force convolution to finish). The exposure is set to -2.5 stops. It’s very close to the result of applying a diffuse convolution in HDR Shop:

And here’s the result of projecting the light probe into three SH bands and converting from the coefficients from radiance to irradiance. Again, the exposure is -2.5 stops. It’s pretty close to the Monte Carlo result, which is reassuring:

Now, let’s compare these results to the those from the irradiance environment maps paper. First, their brute force diffuse convolution:

And now their SH approximation:

My guess is that they either applied a gamma curve of 2.2 for some reason, or didn’t correctly account for sRGB colour space when performing the HDR to LDR conversion. Or am I doing it wrong?

Here are the papers that I cribbed from:

Ramamoorthi & Hanrahan’s paper that introduced SH to the rendering community: An Efficient Representation for Irradiance Environment Maps

Their earlier paper actually contains a more rigorous treatment of spherical harmonics: On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object

Robin Green’s great introduction to the topic: Spherical Harmonic Lighting: The Gritty Details

And Volker Schönefeld wrote my favourite introduction, the way he describes SH in terms of separate functions of theta and phi made everything fall into place: Spherical Harmonics

I’ve had an idea floating around in my head for several months now, but evening classes, a hard drive failure, then a GPU failure prevented me from doing much about it until this weekend. First, a couple of screenshots of what I’ll be talking about.

Cornell box, I choose you!

The above screenshots are of real-time single-bounce GI in a static scene with fully dynamic lighting. There are 7182 patches, and the lighting calculations take 36ms per frame on one thread of an Intel Core 2 @2.4 GHz. The code is not optimized.

The basic idea is simple and is split into two phases.

A one-time scene compilation phase:

  • Split all surfaces in the scene into roughly equal sized patches.
  • For each patch i, build a list of all other patches visible from i along with the form factor between patches i and j:
    F_{ij} = \frac{cos \phi_i \, cos \phi_j}{\pi \, |r|^2} \, A_j
    Fij is the form factor between patches i and j
    Aj is the area of patch j
    r is the vector from i to j
    Φi is the angle between r and the normal of patch i
    Φj is the angle between r and the normal of patch j

And a per-frame lighting phase:

  • For each patch i, calculate the direct illumination.
  • For each patch i, calculate single-bounce indirect illumination from all visible patches:
  • I_i = \sum_j D_j \, F_{ij}
    Ij is the single-bounce indirect illumination for patch i
    Dj is the direct illumination for patch j

So far, so radiosity. If I understand Michal Iwanicki’s GDC presentation correctly, this is similar to the lighting tech on Milo and Kate, only they additionally project the bounce lighting into SH.

The problem with this approach is that the running time is O(N2) with the number of patches. We could work around this by making the patches quite large, running on the GPU, or both. Alternatively, we can bring the running time down to O(N.log(N)) by borrowing from Michael Bunnell’s work on dynamic AO and cluster patches into a hierarchy. I chose to perform bottom-up patch clustering similarly to the method that Miles Macklin describes in his (Almost) realtime GI blog post.

Scene compilation is now:

  • Split all surfaces in the scene into roughly equal sized patches.
  • Build a hierarchy of patches using k-means clustering.
  • For each patch i, build a list of all other patches visible from i along with the form factor between patches i and j. If a visible patch j is too far from patch i look further up the hierarchy.

And the lighting phase:

  • For each leaf patch i in the hierarchy, calculate the direct illumination.
  • Propagate the direct lighting up the hierarchy.
  • For each patch i, calculate single-bounce indirect illumination from all visible patches clusters.

Although this technique is really simple, it supports a feature set similar to that of Enlighten:

  • Global illumination reacts to changes in surface albedo.
  • Static area light sources that cast soft shadows.

That’s basically about it. There are a few of other areas I’m tempted to look into once I’ve cleaned the code up a bit:

  • Calculate directly and indirect illumination at different frequencies. This would allow scaling to much larger scenes.
  • Perform the last two lighting steps multiple times to approximate more light bounces.
  • Project the indirect illumination into SH, HL2 or the Half-Life basis.
  • Light probes for dynamic objects.

You can grab the source code from here. Expect a mess, since it’s a C++ port of a C# proof of concept with liberal use of vector and hash_map. Scene construction is particularly bad and may take a minute to run. You can fly around using WASD and left-click dragging with the mouse.

Improved light attenuation

February 10, 2011

In my previous post, I talked about the attenuation curve for a spherical light source:
f_{att} = \frac{1}{(\frac{d}{r} + 1)^2}

I had suggested applying a scale and bias to the result in order to limit a light’s influence, which is a serviceable solution, but far from ideal. Unfortunately, applying such a bias causes the gradient of the curve to become non-zero at limit of the light’s influence.

Here’s the attenuation curve for a light of radius 1.0:

And after applying a scale and bias (shown in red):

You can see that the gradient at the zero-crossing is close to, but not quite zero. This is problematic because the human eye is irritatingly sensitive to discontinuities in illumination gradients and we might easily end up with Mach bands.

I was discussing this problem with a colleague of mine, Jerome Scholler, and he came up with an excellent suggestion – to transform d in the attenuation equation by some function whose value tends to infinity as its input reaches our desired maximum distance of influence. My first thought was of using tan:
d' = 2.1tan(\frac{\pi d}{2d_{max}})
f_{att} = \frac{1}{(\frac{d'}{r} + 1)^2}

That worked well, the resulting curve has roughly the same shape as the original, while also having both a gradient and value of zero at the desired maximum distance. It does have the disadvantage of using a trig function, which isn’t so hot, so we went looking for something else. After a few minutes playing around we came up with the following rational function:
d' = \frac{d}{1-(\frac{d}{d_{max}})^2}
f_{att} = \frac{1}{(\frac{d'}{r} + 1)^2}

It’s very similar to the tan version, but may run faster, depending on your hardware.

Below are some examples of the different methods, using a light with high intensity and small influence. On the left of each is the original image, on the right is the result of a levels adjustment, which emphasizes the tail of the attenuation curve.

Disclaimer: The parameters for the analytic functions were chosen to highlight their different characteristics, not to look good.

Original ray traced reference:

Analytic scale and bias:

Analytic tan:

Analytic rational:

The graphs today were brought to you courtesy of the awesome

Light attenuation

January 31, 2011

The canonical equation for point light attenuation goes something like this:
f_{att} = \frac{1}{k_c + k_ld + k_qd^2}
d = distance between the light and the surface being shaded
kc = constant attenuation factor
kl = linear attenuation factor
kq = quadratic attenuation factor

Since I first read about light attenuation in the Red Book I’ve often wondered where this equation came from and what values should actually be used for the attenuation factors, but I could never find a satisfactory explanation. Pretty much every reference to light attenuation in both books and online simply presents some variant of this equation, along with screenshots of objects being lit by lights with different attenuation factors. If you’re lucky, there’s sometimes an accompanying bit of handwaving.

Today, I did some experimentation with my path tracer and was pleasantly surprised to find a correlation between the direct illumination from a physically based spherical area light source and the point light attenuation equation.

I set up a simple scene in which to conduct the tests: a spherical area light above a diffuse plane. By setting the light’s radius and distance above the plane to different values and then sampling the direct illumination at a point on the plane directly below the light, I built up a table of attenuation values. Here’s a plot of a some of the results; the distance on the horizontal axis is that between the plane and the light’s surface, not its centre.

After looking at the results from a series of tests, it became apparent that the attenuation of a spherical light can be modeled as:
f_{att} = \frac{1}{(\frac{d}{r} + 1)^2}
d = distance between the light’s surface and the point being shaded
r = the light’s radius

Expanding this out, we get:
f_{att} = \frac{1}{1 + \frac{2}{r}d + \frac{1}{r^2}d^2}
which is the original point light attenuation equation with the following attenuation factors:
k_c = 1
k_l = \frac{2}{r}
k_q = \frac{1}{r^2}

Below are a couple of renders of four lights above a plane. The first is a ground-truth render of direct illumination calculated using Monte Carlo integration:

In this second render, direct illumination is calculated analytically using the attenuation factors derived from the light radius:

The only noticeable difference between the two is that in the second image, an area of the plane to the far left is slightly too bright due to a lack of a shadowing term.

Maybe this is old news to many people, but I was pretty happy to find out that an equation that had seemed fairly arbitrary to me for so many years actually had some physical motivation behind it. I don’t really understand why this relationship is never pointed out, not even in Foley and van Dam’s venerable tome*.

Unfortunately this attenuation model is still problematic for real-time rendering, since a light’s influence is essentially unbounded. We can, however, artificially enforce a finite influence by clipping all contributions that fall below a certain threshold. Given a spherical light of radius r and intensity Li, the illumination I at distance d is:
I = \frac{L_i}{(\frac{d}{r} + 1)^2}

Assuming we want to ignore all illumination that falls below some cutoff threshold Ic, we can solve for d to find the maximum distance of the light’s influence:
d_{max} = r(\sqrt{\frac{L_i}{I_c}}-1)

Biasing the calculated illumination by -Ic and then scaling by 1/(1-Ic) ensures that illumination drops to zero at the furthest extent, and the maximum illumination is unchanged.

Here’s the result of applying these changes with a cutoff threshold of 0.001; in the second image, areas which receive no illumination are highlighted in red:

And here’s a cutoff threshold of 0.005; if you compare to the version with no cutoff, you’ll see that the illumination is now noticeably darker:

Just to round things off, here’s a GLSL snippet for calculating the approximate direct illumination from a spherical light source. Soft shadows are left as an exercise for the reader.

vec3 DirectIllumination(vec3 P, vec3 N, vec3 lightCentre, float lightRadius, vec3 lightColour, float cutoff)
    // calculate normalized light vector and distance to sphere light surface
    float r = lightRadius;
    vec3 L = lightCentre - P;
    float distance = length(L);
    float d = max(distance - r, 0);
    L /= distance;
    // calculate basic attenuation
    float denom = d/r + 1;
    float attenuation = 1 / (denom*denom);
    // scale and bias attenuation such that:
    //   attenuation == 0 at extent of max influence
    //   attenuation == 1 when d == 0
    attenuation = (attenuation - cutoff) / (1 - cutoff);
    attenuation = max(attenuation, 0);
    float dot = max(dot(L, N), 0);
    return lightColour * dot * attenuation;

* I always felt a little sorry for Feiner and Hughes.

Fighting fireflies

December 23, 2010

I’ve been playing around with path tracing on and off for longer than I care to admit. Although my dalliances never produced anything earth-shattering (and certainly nothing I’d be willing to post about on ompf), I’ve found it to be an endlessly fascinating subject. No matter how much I read about it, I only ever seem to be scratching the surface.

One of the biggest headaches I encountered were caused by “fireflies”: those bright pixels that can occur when a sampling a strong response combined with a small PDF somewhere along the path. For a long time, I was “fixing” these by hand painting over the offending pixels and pretending like nothing ever happened. Eventually though, the guilt of this gnawed away at me long enough to motivate finding some kind of better solution.

My first thought was to write a filter that estimated variance in an image and replace any “bad” pixels it found with a weighted average of their neighbours. Luckily, my second thought was of shadow maps, the only other context in which I’d read about variance before. Based on the ideas in that paper, I accumulate two separate per-pixel buffers: one storing the running sum of the samples and the other storing the sum of their squares. Having these two buffers then makes it trivial to compute the sample variance of each pixel in the image.

My path tracer already had support for progressive refinement, so it was straightforward to add a separate “variance reduction” pass that would run at the touch of a button. During this pass, the N pixels with the highest variance are identified and oversampled a few hundred times, which hopefully reduces their variance sufficiently. If not, I just run the pass again.

As an example, here’s a render of the Manifold mesh from Torolf Sauermann’s awesome model repository, stopped after only a few paths have been traced per pixel:

I’ve highlighted a few areas that contain fireflies and below is a comparison of those areas before and after running the variance reduction pass:

And here’s the complete result of running the pass; it’s still noisy, but the pixels with particularly high variance have been cleaned up reasonably well:

I’m not too hot at statistics, but I would guess that this adds bias to the final result, which is frowned upon in some circles (but not others). Admittedly, a better solution would be to simply not generate so much variance in the first place, but this will do as a kludge until then. At least it’s better than painting pixels by hand!

Ok, since this post was mostly just an excuse to dump some results from my path tracer, here they are.

The XYZ RGB Dragon from Stanford’s 3D Scanning Repository:

A heat map of the BIH built for the same model.

The other Dragon from Stanford:

Manifold again, from

Some Stanford Bunnies:

Crytek’s updated version of Marko Dabrovic’s Sponza model:

And Stanford’s Lucy, just to prove that I can:
I’ve not been able to find any close up renders of Lucy to compare with, but I believe that the “pimples” on the model are noise in the original dataset.

Why my fluids don’t flow

December 14, 2010

I have an unopened copy of Digital Color Management sitting on my desk. It’s staring at me accusingly.

In order to keep myself distracted from its dirty looks, I’ve been tinkering around with fluid simulation. Miles Macklin has done some great work with Eulerian (grid based) solvers, so in an effort to distance myself from the competition, I’m sticking to 2D Lagrangian (particle based) simulation.

Until recently, I’d always thought that particle based fluid simulation was complicated and involved heavy maths. This wasn’t helped by the fact that most of the papers on the subject have serious sounding names like Particle-based Viscoelastic Fluid Simulation, Weakly compressible SPH for free surface flows, or even Smoothed Particle Hydrodynamics and Magnetohydrodynamics.

It wasn’t until I finally took the plunge and tried writing my own Smoothed Particle Hydrodynamics simulation that I found that it can be quite easy, provided you work from the right papers. SPH has a couple of advantages over grid based methods: it is trivial to ensure that mass is exactly conserved, and free-surfaces (the boundary between fluid and non-fluid) come naturally. Unfortunately, SPH simulations have a tendency to explode if the time step is too large and getting satisfactory results is heavily dependent on finding “good” functions with which to model the inter-particle forces.

I had originally intended to write an introduction to SPH, but soon realised that it would make this post intolerably long, so instead I’ll refer to the papers that I used when writing my own sim. Pretty much every SPH paper comes with an introduction to the subject, invariably in section 2. Related Work.

The first paper I tried implementing was Particle-Based Fluid Simulation for Interactive Applications by Müller et. al. It serves as a great introduction to SPH with a very good discussion of kernel weighting functions, but I had real difficulty getting decent results. In the paper pressure, viscosity and surface tension forces are modeled using following equations:

\textbf{f}_i^{pressure} = -\sum_{j}m_j\frac{p_j}{\rho_j}\nabla{W(\textbf{r}_i-\textbf{r}_j, h)}

\textbf{f}_i^{viscosity} = \mu\sum_{j}m_j\frac{\textbf{v}_j-\textbf{v}_i}{\rho_j}\nabla^2W(\textbf{r}_i-\textbf{r}_j, h)

c_S(\textbf{r}) = \sum_jm_j\frac{1}{\rho_j}W(\textbf{r}-\textbf{r}_j, h)

\textbf{f}_i^{surface} = -\sigma\nabla^2c_S\frac{\textbf{n}}{|\textbf{n}|}

The pressure for each particle is calculated from its density using:

P_i = k(\rho_i - \rho_0) where \rho_0 is the some non-zero rest density.

The first problem I encountered was with the pressure model; it only acts as a repulsive force if the particle density is greater than the rest density. If a particle has only a small number of neighbours, the pressure force will attract them to form a cluster of particles all sharing the same space. In my experiments, I often found large numbers of clusters of three or four particles all in the same position. It took me a while to figure out what was going on because Müller states that the value of the rest density “mathematically has no effect on pressure forces”, which is only true given a fairly uniform density of particles far from the boundary.

The second problem I found was with the surface tension force. It was originally developed for multiphase fluid situations with no free surfaces and doesn’t behave well near the surface boundary; in fact it can actually pull the fluid into concave shapes. Additionally, because it’s based on a Laplacian, it’s very sensitive to fluctuations in the particle density, which are the norm at the surface boundary.

After a week or so of trying, this was my best result:

From the outset, you can see the surface tension force is doing weird things. Even worse, once the fluid starts to settle the particles tended to stack on top of each and form a very un-fluid blob.

On the up side, I did create possibly my best ever bug when implementing the surface tension model; I ended up with something resembling microscopic life floating around under the microscope:

The next paper I tried was Particle-based Viscoelastic Fluid Simulation by Clavet et al. I actually had a lot of success with their paper and had a working implementation of their basic model up and running in less than two hours. Albeit minus the viscoelasticity. In addition to the pressure force described in Müller’s paper, they model “near” density and pressure, which are similar to their regular counterparts but with a zero rest density and different kernel functions:

P_i = k(\rho_i - \rho_0)

P_i^{near} = k^{near} \rho_i^{near}

\rho_i = \sum_j (1 - \frac{|\textbf{r}_i - \textbf{r}_j|}{h}) ^ 2

\rho_i^{near} = \sum_j (1 - \frac{|\textbf{r}_i - \textbf{r}_j|}{h}) ^ 3

This near pressure ensures a minimum spacing and as an added bonus performs a decent job of modelling surface tension too. This is the first simulation I ran using their pressure and viscosity forces:

Although initial results were promising, I struggled when tweaking the parameters to find a good balance between a fluid that was too compressible and one that was too viscous. Also, what I really wanted was to do multiphase fluid simulation. This wasn’t covered in the viscoelastic paper, so my next port of call was Weakly compressible SPH for free surface flows by Becker et al. In this paper, surface tension is modeled as:

\textbf{f}_i^{surface} = -\frac{\kappa}{m_i} \sum_j m_j W(\textbf{r}_i-\textbf{r}_j) (\textbf{r}_i - \textbf{r}_j)

They also discuss using Tait’s equation for the pressure force, rather than one based on the ideal gas law:

P_i = B((\frac{\rho_i}{\rho_0})^\gamma - 1) with \gamma = 7

I gave that a shot, but the large exponent caused the simulation to explode unless I used a really small time step. Instead, I found that modifying the pressure forces from the viscoelastic paper slightly gave a much less compressible fluid without the requirement for a tiny time step:

\rho_i = \sum_j (1 - \frac{|\textbf{r}_i - \textbf{r}_j|}{h}) ^ 3

\rho_i^{near} = \sum_j (1 - \frac{|\textbf{r}_i - \textbf{r}_j|}{h}) ^ 4

Here’s one of my more successful runs:

And here is a slightly simplified version of the code behind it. Be warned, it’s quite messy; I’m rather enjoying hacking code together these days:

#include <float.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#include <memory.h>
#include <glut.h>

#define kScreenWidth 640
#define kScreenHeight 480
#define kViewWidth 10.0f
#define kViewHeight (kScreenHeight*kViewWidth/kScreenWidth)
#define kPi 3.1415926535f
#define kParticleCount 3000

#define kRestDensity 82.0f
#define kStiffness 0.08f
#define kNearStiffness 0.1f
#define kSurfaceTension 0.0004f
#define kLinearViscocity 0.5f
#define kQuadraticViscocity 1.0f

#define kParticleRadius 0.05f
#define kH (6*kParticleRadius)
#define kFrameRate 20
#define kSubSteps 7

#define kDt ((1.0f/kFrameRate) / kSubSteps)
#define kDt2 (kDt*kDt)
#define kNorm (20/(2*kPi*kH*kH))
#define kNearNorm (30/(2*kPi*kH*kH))

#define kEpsilon 0.0000001f
#define kEpsilon2 (kEpsilon*kEpsilon)

struct Particle
    float x;
    float y;

    float u;
    float v;

    float P;
    float nearP;

    float m;

    float density;
    float nearDensity;
    Particle* next;

struct Vector2
    Vector2() { }
    Vector2(float x, float y) : x(x) , y(y) { }
    float x;
    float y;

struct Wall
    Wall() { }
    Wall(float _nx, float _ny, float _c) : nx(_nx), ny(_ny), c(_c) { }
    float nx;
    float ny;
    float c;

struct Rgba
    Rgba() { }
    Rgba(float r, float g, float b, float a) : r(r), g(g), b(b), a(a) { }
    float r, g, b, a;

struct Material
    Material() { }
    Material(const Rgba& colour, float mass, float scale, float bias) : colour(colour) , mass(mass) , scale(scale) , bias(bias) { }
    Rgba colour;
    float mass;
    float scale;
    float bias;

#define kMaxNeighbourCount 64
struct Neighbours
    const Particle* particles[kMaxNeighbourCount];
    float r[kMaxNeighbourCount];
    size_t count;

size_t particleCount = 0;
Particle particles[kParticleCount];
Neighbours neighbours[kParticleCount];
Vector2 prevPos[kParticleCount];
Vector2 relaxedPos[kParticleCount];
Material particleMaterials[kParticleCount];
Rgba shadedParticleColours[kParticleCount];

#define kWallCount 4
Wall walls[kWallCount] =
    Wall( 1,  0, 0),
    Wall( 0,  1, 0),
    Wall(-1,  0, -kViewWidth),
    Wall( 0, -1, -kViewHeight)

#define kCellSize kH
const size_t kGridWidth = (size_t)(kViewWidth / kCellSize);
const size_t kGridHeight = (size_t)(kViewHeight / kCellSize);
const size_t kGridCellCount = kGridWidth * kGridHeight;
Particle* grid[kGridCellCount];
size_t gridCoords[kParticleCount*2];

struct Emitter
    Emitter(const Material& material, const Vector2& position, const Vector2& direction, float size, float speed, float delay)
        : material(material), position(position), direction(direction), size(size), speed(speed), delay(delay), count(0)
        float len = sqrt(direction.x*direction.x + direction.y*direction.y);
        this->direction.x /= len;
        this->direction.y /= len;
    Material material;
    Vector2 position;
    Vector2 direction;
    float size;
    float speed;
    float delay;
    size_t count;

#define kEmitterCount 2
Emitter emitters[kEmitterCount] =
        Material(Rgba(0.6f, 0.7f, 0.9f, 1), 1.0f, 0.08f, 0.9f),
        Vector2(0.05f*kViewWidth, 0.8f*kViewHeight), Vector2(4, 1), 0.2f, 5, 0),
        Material(Rgba(0.1f, 0.05f, 0.3f, 1), 1.4f, 0.075f, 1.5f),
        Vector2(0.05f*kViewWidth, 0.9f*kViewHeight), Vector2(4, 1), 0.2f, 5, 6),

float Random01() { return (float)rand() / (float)(RAND_MAX-1); }
float Random(float a, float b) { return a + (b-a)*Random01(); }

void UpdateGrid()
    // Clear grid
    memset(grid, 0, kGridCellCount*sizeof(Particle*));

    // Add particles to grid
    for (size_t i=0; i<particleCount; ++i)
        Particle& pi = particles[i];
        int x = pi.x / kCellSize;
        int y = pi.y / kCellSize;

        if (x < 1)
            x = 1;
        else if (x > kGridWidth-2)
            x = kGridWidth-2;

        if (y < 1)
            y = 1;
        else if (y > kGridHeight-2)
            y = kGridHeight-2; = grid[x+y*kGridWidth];
        grid[x+y*kGridWidth] = &pi;

        gridCoords[i*2] = x;
        gridCoords[i*2+1] = y;

void ApplyBodyForces()
    for (size_t i=0; i<particleCount; ++i)
        Particle& pi = particles[i];
        pi.v -= 9.8f*kDt;

void Advance()
    for (size_t i=0; i<particleCount; ++i)
        Particle& pi = particles[i];

        // preserve current position
        prevPos[i].x = pi.x;
        prevPos[i].y = pi.y;

        pi.x += kDt * pi.u;
        pi.y += kDt * pi.v;

void CalculatePressure()
    for (size_t i=0; i<particleCount; ++i)
        Particle& pi = particles[i];
        size_t gi = gridCoords[i*2];
        size_t gj = gridCoords[i*2+1]*kGridWidth;

        neighbours[i].count = 0;

        float density = 0;
        float nearDensity = 0;
        for (size_t ni=gi-1; ni<=gi+1; ++ni)
            for (size_t nj=gj-kGridWidth; nj<=gj+kGridWidth; nj+=kGridWidth)
                for (Particle* ppj=grid[ni+nj]; NULL!=ppj; ppj=ppj->next)
                    const Particle& pj = *ppj;

                    float dx = pj.x - pi.x;
                    float dy = pj.y - pi.y;
                    float r2 = dx*dx + dy*dy;
                    if (r2 < kEpsilon2 || r2 > kH*kH)

                    float r = sqrt(r2);
                    float a = 1 - r/kH;
                    density += pj.m * a*a*a * kNorm;
                    nearDensity += pj.m * a*a*a*a * kNearNorm;

                    if (neighbours[i].count < kMaxNeighbourCount)
                        neighbours[i].particles[neighbours[i].count] = &pj;
                        neighbours[i].r[neighbours[i].count] = r;

        pi.density = density;
        pi.nearDensity = nearDensity;
        pi.P = kStiffness * (density - pi.m*kRestDensity);
        pi.nearP = kNearStiffness * nearDensity;

void CalculateRelaxedPositions()
    for (size_t i=0; i<particleCount; ++i)
        const Particle& pi = particles[i];

        float x = pi.x;
        float y = pi.y;

        for (size_t j=0; j<neighbours[i].count; ++j)
            const Particle& pj = *neighbours[i].particles[j];
            float r = neighbours[i].r[j];
            float dx = pj.x - pi.x;
            float dy = pj.y - pi.y;

            float a = 1 - r/kH;

            float d = kDt2 * ((pi.nearP+pj.nearP)*a*a*a*kNearNorm + (pi.P+pj.P)*a*a*kNorm) / 2;

            // relax
            x -= d * dx / (r*pi.m);
            y -= d * dy / (r*pi.m);

            // surface tension
            if (pi.m == pj.m)
                x += (kSurfaceTension/pi.m) * pj.m*a*a*kNorm * dx;
                y += (kSurfaceTension/pi.m) * pj.m*a*a*kNorm * dy;

            // viscocity
            float du = pi.u - pj.u;
            float dv = pi.v - pj.v;
            float u = du*dx + dv*dy;
            if (u > 0)
                u /= r;

                float a = 1 - r/kH;
                float I = 0.5f * kDt * a * (kLinearViscocity*u + kQuadraticViscocity*u*u);

                x -= I * dx * kDt;
                y -= I * dy * kDt;


        relaxedPos[i].x = x;
        relaxedPos[i].y = y;

void MoveToRelaxedPositions()
    for (size_t i=0; i<particleCount; ++i)
        Particle& pi = particles[i];
        pi.x = relaxedPos[i].x;
        pi.y = relaxedPos[i].y;
        pi.u = (pi.x - prevPos[i].x) / kDt;
        pi.v = (pi.y - prevPos[i].y) / kDt;

void ResolveCollisions()
    for (size_t i=0; i<particleCount; ++i)
        Particle& pi = particles[i];

        for (size_t j=0; j<kWallCount; ++j)
            const Wall& wall = walls[j];
            float dis = wall.nx*pi.x + wall.ny*pi.y - wall.c;
            if (dis < kParticleRadius)
                float d = pi.u*wall.nx + pi.v*wall.ny;
                if (dis < 0)
                    dis = 0;
                pi.u += (kParticleRadius - dis) * wall.nx / kDt;
                pi.v += (kParticleRadius - dis) * wall.ny / kDt;

void Render()
    glClearColor(0.02f, 0.01f, 0.01f, 1);

    glOrtho(0, kViewWidth, 0, kViewHeight, 0, 1);


    for (size_t i=0; i<particleCount; ++i)
        const Particle& pi = particles[i];
        const Material& material = particleMaterials[i];

        Rgba& rgba = shadedParticleColours[i];
        rgba = material.colour;
        rgba.r *= material.bias + material.scale*pi.P;
        rgba.g *= material.bias + material.scale*pi.P;
        rgba.b *= material.bias + material.scale*pi.P;



    glColorPointer(4, GL_FLOAT, sizeof(Rgba), shadedParticleColours);
    glVertexPointer(2, GL_FLOAT, sizeof(Particle), particles);
    glDrawArrays(GL_POINTS, 0, particleCount);



void EmitParticles()
    if (particleCount == kParticleCount)

    static int emitDelay = 0;
    if (++emitDelay < 3)

    for (size_t emitterIdx=0; emitterIdx<kEmitterCount; ++emitterIdx)
        Emitter& emitter = emitters[emitterIdx];
        if (emitter.count >= kParticleCount/kEmitterCount)

        emitter.delay -= kDt*emitDelay;
        if (emitter.delay > 0)

        size_t steps = emitter.size / (2*kParticleRadius);

        for (size_t i=0; i<=steps && particleCount<kParticleCount; ++i)
            Particle& pi = particles[particleCount];
            Material& material = particleMaterials[particleCount];


            float ofs = (float)i / (float)steps - 0.5f;

            ofs *= emitter.size;
            pi.x = emitter.position.x - ofs*emitter.direction.y;
            pi.y = emitter.position.y + ofs*emitter.direction.x;
            pi.u = emitter.speed * emitter.direction.x*Random(0.9f, 1.1f);
            pi.v = emitter.speed * emitter.direction.y*Random(0.9f, 1.1f);
            pi.m = emitter.material.mass;

            material = emitter.material;

    emitDelay = 0;

void Update()
    for (size_t step=0; step<kSubSteps; ++step)



int main (int argc, char** argv)
    glutInitWindowSize(kScreenWidth, kScreenHeight);
    glutInit(&argc, argv);
    glutInitDisplayString("samples stencil>=3 rgb double depth");
    memset(particles, 0, kParticleCount*sizeof(Particle));

    return 0;

I’m pretty happy with the results, even if at three seconds per frame for the video above, my implementation isn’t exactly fast. Here are a few other videos from various stages of development: