Category Archives: Distance Estimation

Rendering 3D fractals without a distance estimator

I have written a lot about distance estimated 3D fractals, and while Distance Estimation is a fast and elegant technique, it is not always possible to derive a distance estimate for a particular system.

So, how do you render a fractal, if the only knowledge you have is whether a given point belongs to the set or not? Or, in other words, how much information can you extract if the only information you have is a black-box function of the form:

I decided to try out some simple brute-force methods to see how they would compare to the DE methods. Contrary to my expectations, it turned out that you can actually get reasonable results without a DE.

First a couple of disclaimers: brute-force methods can not compete with distance estimators in terms of speed. They will typically be a magnitude slower. And if you do have more information available, you should always use it: for instance, even if you can’t find a distance estimator for a given escape time fractal, the escape length contains information that can be used to speed up the rendering or create a surface normal.

The method I used is not novel nor profound: I simply sample random points along the camera ray for each pixel. Whenever a hit is found on the camera ray, the sampling will proceed on only the interval between the camera and the hit point (since we are only interested in finding the closest pixels), e.g. something like this:

(The Near and Far distances are used to restrict the sample space, and speed up rendering)

There are different ways to choose the samples. The simplest is to just sample uniformly (as in the example above), but I found that a stratified approach, where the camera ray segment is divided into equal pieces and a sample is choosen from each part works better. I think the sampling scheme could be improved: in particular once you found a hit, you should probably bias the sampling towards the hit to make convergence faster. Since I use a progressive (double buffered) approach in Fragmentarium, it is also possible to read the pixel depths of adjacent pixels, which probably also could be used.

Now, after sampling the camera rays you end up with a depth map, like this:

(Be sure to render to a texture with 32-bit floats – a 8-bit buffer will cause quantization).

For distance estimated rendering, you can use the gradient of the distance estimator to obtain the surface normal. Unfurtunately this is not an option here. We can, however, calculate a screen space surface normal, based on the depths of adjacent pixels, and transform this normal back into world space:

(Update: I found out that GLSL supports finite difference derivatives through the dFdx statement, which made the code above much simpler).

Now we can use a standard lighting scheme, like Phong shading. This really brings a lot of detail to the image:

In order to improve the depth perception, it is possible to apply a screen space ambient occlusion scheme. Recently, there was a very nice tutorial on SSAO on devmaster, but I was to lazy to try it out. Instead I opted for the simplest method I could think of: simply sample some pixels in a neighborhood, and count how many of them that are closer to the camera than the center pixel.

This is how this naive ambient occlusion scheme works:

(Notice that for pixels with no hits, I’ve choosen to lighten, rather than darken them. This creates an outer glow effect.)

Now combined with the Phong shading we get:

I think it is quite striking how much detail you can infer simply from a depth map! In this case I didn’t color the fractal, but nothing prevents you from assigning a calculated color. The depth buffer information only uses the alpha channel.

Here is another example (Aexion’s MandelDodecahedron):

While brute-force rendering is much slower than distance estimation, it is possible to render these systems at interactive frame rates in Fragmentarium, especially since responsiveness can be improved by using progressive rendering: do a number of samples, then storing the best found solution (closest pixel) in a depth buffer (I use the alpha channel), render the frame and repeat.

There are a couple of downsides to brute force rendering:

  • It is slower than distance estimation
  • You have to rely on screen space methods for ambient occlusion, surface normals, and depth-of-field
  • Anti-aliasing is more tricky since you cannot accumulate and average. You may render at higher resolution and downsample, or use tiled rendering, but beware that screen space ambient occlusion introduce artifacts which may be visible on tile edges.

On the other hand, there are also advantages:

  • Much simpler to construct
  • Interior renderings are trivial – just reverse the ‘inside’ function
  • Progressive quality rendering: just keep adding samples, and the image will converge.

To use the Fragmentarium script, just implement an ‘inside’ function:

It is also possible to use the raytracer on existing DE’s – here a point is assumed to be inside a fractal if the DE returns a negative number, and outside if the DE returns a positive one.

The script can be downloaded as part of Fragmentarium source distribution (it is not yet in the binary distributions). The following files are needed:

Distance Estimated 3D Fractals (Part VIII): Epilogue

This is the last post in my introduction to distance estimated 3D fractals (see Part one for an overview). Originally, I intended this to be much shorter and more focused, but different topics kept sneaking up on me.

This final post discusses hybrid systems, and a few things that didn’t fit naturally in the previous posts. It also contains a small collection of links to relevant resources.

Hybrids

All the fractal systems mentioned in the previous parts apply the same transformation to each point for a number of iterations. But there is nothing that prevents applying different transformations at each iteration step. This has led to a number of hybrid systems, using building blocks from different fractals. They are very popular in Mandelbulb 3D, which comes with a huge library of transformations, which may be stringed together in a vast number of possible combinations.

Spudsville

It is difficult to trace the origin of many of these hybrids, since they are often cloned and modified. One of the more interesting base forms is the Spudsville system by Lenord (see also Hal Tenny’s tutorial on this system).

It is based on the following recipe:

Pseudo Kleinian

This is another popular base form, based on parameters from Theli-at’s Kleinian Drops. It is based on this formula:

A version of a similar system is available in Fragmentarium as “Knighty Collection/PseudoKleinian.frag”:

It is also possible to throw some Menger structure into the mix (see “Knighty Collection/PseudoKleinianMenger.frag”):

It is a very diverse system: this is the same formula, that I used as a base form for both Time Pieces:

and Pseudo-Kleinian Blue:

There really is no end to the possibilities. Here is another example:

where an octahedral symmetry transformation has been substituted in a Spudsville-like system:

The question is how to construct a suitable distance estimator for these hybrids systems. There is no easy answer to this. Mandelbulb3D and Mandelbulber both use the numerical gradient approximation discussed in part V of this series.

If the system is composed only of conformal transformations, the scalar approach discussed in part VI will be sufficient.

But for general combinations there is no easy way: it is often possible to guess a decent distance estimator, but more often than not, the analytic distance estimator overshoots and needs to be compensated by a fudge factor.

Interior renderings

The Mandelbrot distance estimation formula discussed in part V is only valid for exterior distances. There also exists a formula for the interior distance (for the 2D case), but it is much more complex than the exterior one, since it requires detecting cycles in the orbit.

However, in some cases the exterior distance estimate (or the absolute value of it), also works as an interior estimate (thanks to Visual for pointing this out). Here is an example of the interior of a Mandelbulb:

Geometric Orbit Trapping

Orbit trapping is often used to color fractals. During the orbit calculation the minimum distance to various geometric objects is stored (often the center, a sphere shell, or the x,y, and z-planes).

But it is also possible to use orbit traps to define the geometry of the fractals. Here is a standard Kaleidoscopic IFS like system, defined by DE such as:

resulting in an image like this:

but by inserting a trap-function and keeping the minimum value, we can create some interesting geometric variations:

for instance, using a cylinder-function for trap(z) results in an image like this:

Heightmap renderings

It is also possible to use distance estimated methods to draw heightmaps of fractals, e.g.:


Included in Fragmentarium as ‘Knighty Collection/MandelbrotHeightField.frag’

Or use heightmaps to visualize the algebraic structure of poles and zeroes in the complex plane:


Included in Fragmentarium as ‘Experimental/LiftedDomainColoring3D.frag’

Heightmaps can also be generated from Perlin Noise, to create more realistic terrains:


Included in Fragmentarium as ‘Experimental/Terrain.frag’

Knots, Polytopes, and Honeycombs

It is also possible to use distance estimation techniques to depict other mathematical structures than fractals. I’ve written about them before, but Knighty has explored DE’s for knots and polyhedra:

for four-dimensional polychora:

and even for hyperbolic honeycombs:

(There are several examples included with Fragmentarium)

Resources

Software

The easiest way to start exploring 3D fractals is probably by trying Mandelbulb 3D or Mandelbulber. Both are very powerful and feature-rich applications.

Mandelbulb 3D (by Jesse) is probably the most used 3D fractal creation tool (judging by pictures posted at Fractal Forums). It contains many different formulas and fragments, which can be combined as hybrids. It is free, closed-source, CPU-based, and Windows only.

Mandelbulber (by Buddhi) is open source, and available for Windows, Linux, and Mac. CPU-based, but with OpenCL preview!

GPU Based renderers

Fragmentarium is my own playground for working with GPU (GLSL) based pixel graphics. It is meant to create a modular and interactive environment for working with 2D and 3D graphics. All the images in this series of blog post were made with Fragmentarium, and many of the systems are included as examples.

Rrrola’s Boxplorer is a fast interactive Mandelbox explorer. It has been extended by Marius Schilder in Boxplorer2 to include spline animations, stereo view, and many examples of fractal systems.

Subblue’s Pixel Bender Mandelbulb script was one of the first GPU implementations. He has made many great fractal animations and images, so be sure to visit his web site. He also created the impressive Fractal Lab WebGL site, which made it possible to explore fractals directly in a browser (the site is currently under reconstruction)

Eiffie’s Animandel Pro is a tool for creating fractal animations. It features a GLSL editor and even an integrated C-compiler for dynamically compiled CPU code. It is certainly not the easiest way to get started, but as can be seen from Eiffie’s videos it is a powerful tool.

Web sites and papers

Fractal Forums is the place, where all the new development and discoveries can be followed. It’s a treasure chest filled with information, but it can be difficult to find it in the archives. A good place to start is the original Mandelbox thread and the thread about DE’s for the Mandelbox.

Daniel White’s Mandelbulb site is probably the best account of the history of this fractal. Also see Paul Nylander’s Hypercomplex systems.

Tom Lowe’s Mandelbox site has a lot of information on the Mandelbox, collected by the person who discovered it himself.

Hypercomplex Iterations: Distance Estimation and Higher Dimensional Fractals (2002). by Dang, Kaufmann, and Sandin is a rare mathematical treatment of higher-dimensional fractals and their distance estimates. It is free (but tough!).

J. C. Hart’s original paper Ray tracing deterministic 3-D fractals and his sphere tracing papers are must-reads. He has also written many other great papers.

Pouet.net is a web site for demo scene coders. There is a strong emphasis on heavily optimized and efficient code. Several demos features distance estimation and fractals.

In particular Iñigo Quílez has explored fractals and distance fields in a demo scene context. His Rendering Worlds With Two Triangles is a good introduction to distance field rendering. But be sure to check out Quilez’s website – there is an abundance of good stuff, including lots of tutorials.

Distance Estimated Polychora

My last post mentioned some scripts that Knighty (a Fractal Forums member) made for distance estimated rendering of many types of polyhedra, including the Platonic solids. Shortly after, Knighty really raised the bar by finding a distance estimator for four dimensional polytopes. In this post, I’ll show some images of a subset of these, the convex regular polychora.

There are several ways to depict four dimensional structures. The 4D Quaternion Julia, one of the first distance estimated fractals, simply showed 3D slices of 4D space. Another way would be to project the shadow of a 4D object onto a 3D space. Ideally, a proper perspective projective would be preferable, but this seems to be complicatated with distance estimation techniques.

The technique Knighty used to create a 3D projection, was to place the polychoron boundary on a 3-sphere, and then stereographically project the 3-sphere surface onto a 3-dimensional space. For a very thorough and graphical introduction to stereographic projection of higher-dimensional polytopes, see this great movie: Dimensions. It should be noted that the Jenn3D program also uses this projection to depict a variety of polytopes (using polygonal rendering, not distance estimated ray marching).

Back to the structures:

The convex regular 4-polytopes are the four-dimensional analogs of the Platonic solids. They are bounded by three-dimensional cells, which are all Platonic solids of the same kind (similar to the way the Platonic solids are bounded by identical regular 2D polygons). The convex regular polytopes are consistently named by the number of identical cells (Platonic solids) that bounds them. In four dimensions, there are six of these, one more than the number of Platonic solids.

5-cell

The 5-cell (or 4-simplex, or hypertetrahedron) is the simplest of the convex regular polytopes. It is composed of 5 three-dimensional tetrahedrons, resulting in a total of 5 vertices and 10 edges. It is the 4D generalisation of a tetrahedron.

The curved lines are a consequence of the stereographic projection. In 4D the lines would be straight.

8-cell

The 8-cell (or hypercube, or Tesseract) is also simple. Composed of 8 3D-cubes, it has 16 vertices, and 32 edges. It is the 4D generalisation of a cube.

16-cell

Things start to get more complicated with the 16-cell (or hyperoctahedron). It is composed of 16 tetrahedrons, and has 8 vertices and 24 edges. It is the 4D generalisation of an octahedron.

Notice, that if we rotate the 3-sphere, we can get interesting depictions, with edges getting infinitely long in 3-space:

24-cell

The 24-cell is exceptional, since it has no 3D analogue. It is built from 24 octahedrons, has 24 vertices and 96 edges.

120-cell

The 120-cell is a beast, built from 120 three-dimensional dodecahedra. With 600 vertices and 1200 edges, it is the most complex of the convex, regular polychora. It is the 4D generalisation of a dodecahedron.

Zooming in, it is easier to see the pentagons making up the dodecahedra.

600-cell

Finaly, the 600-cell is built from an even larger number of polyhedra: 600 three-dimensional tetrahedrons. However, the simpler polyhedra means there is only a total of 120 vertices and 720 edges. It is the 4D generalisation of an icosahedron.

Knighty’s great Fragmentarium scripts can be found in this thread at Fractal Forums.

Knighty’s script is not limited to regular convex polychora. Many types of polytopes can be made. Here are the parameters used for the images in this post:

polychora06.frag parameters:

Type,U,V,W,T
3,0,1,0,0 - 5-cell
4,0,1,0,0 - 8-cell
4,0,0,1,0 - 24-cell
4,0,0,0,1 - 16-cell
5,0,1,0,0 - 120-cell
5,0,0,0,1 - 600-cell

And for completeness, here are the parameters for the 3D polyhedron script:

Type,U,V,W
3,0,1,0 Tetrahedron
4,0,0,1 Cube
3,1,0,0 Octahedron
5,0,0,1 Dodecahedron
5,0,1,0 Icosahedron

Distance Estimated 3D Fractals (VII): Dual Numbers

Previous parts: part I, part II, part III, part IV, part V, and part VI.

This was supposed to be the last blog post on distance estimated 3D fractals, but then I stumbled upon the dual number formulation, and decided it would blend in nicely with the previous post. So this blog post will be about dual numbers, and the next (and probably final) post will be about hybrid systems, heightmap rendering, interior rendering, and links to other resources.

Dual Numbers

Many of the distance estimators covered in the previous posts used a running derivative. This concept can be traced back to the original formula for the distance estimator for the Mandelbrot set, where the derivative is described iteratively in terms of the previous values:

\(f’_n(c) = 2f_{n-1}(c)f’_{n-1}(c)+1\)
 
In the previous post, we saw how the Mandelbox could be described a running Jacobian matrix, and how this matrix could be replaced by a single running scalar derivative, since the Jacobians for the conformal transformations all have a particular simple form (and thanks to Knighty the argument was extended to non-Julia Mandelboxes).

Now, some month ago I stumbled upon automatic differentation and dual numbers, and after having done some tests, I think this a very nice framework to complete the discussion of distance estimators.

So what are these dual numbers? The name might sound intimidating, but the concept is very simple: we extend the real numbers with another component – much like the complex numbers:

\(x = (x_r, x_d) = x_r + x_d \epsilon\)

where \(\epsilon\) is the dual unit, similar to the imaginary unit i for the complex numbers. The square of a dual unit is defined as: \(\epsilon * \epsilon = 0\).

Now for any function which has a Taylor series, we have:

\(f(x+dx) = f(x) + f'(x)dx + (f”(x)/2)dx^2 + …\)
 
If we let \(dx = \epsilon\), it follows:

\(f(x+\epsilon) = f(x) + f'(x)\epsilon \)
 
because the higher order terms vanish. This means, that if we evaluate our function with a dual number \(d = x + \epsilon = (x,1)\), we get a dual number back, (f(x), f'(x)), where the dual component contains the derivative of the function.

Compare this with the finite difference scheme for obtaining a derivative. Take a quadratic function as an example and evaluate its derivative, using a step size ‘h':

\(f(x) = x*x\)

This gives us the approximate derivative:

\(f'(x) \approx \frac {f(x+h)-f(x)}{h} = \frac { x^2 + 2*x*h + h^2 – x^2 } {h} = 2*x+h\)
 
The finite difference scheme introduces an error, here equal to h. The error always gets smaller as h gets smaller (as it converges towards to the true derivative), but numerical differentiation introduces inaccuracies.

Compare this with the dual number approach. For dual numbers, we have:

\(x*x = (x_r+x_d\epsilon)*(x_r+x_d\epsilon) = x_r^2 + (2 * x_r * x_d )\epsilon\).
 
Thus,

\(f(x_r + \epsilon) = x_r^2 + (2 * x_r)*\epsilon\)
 
Since the dual component is the derivative, we have f'(x) = 2*x, which is the exact answer.

But the real beauty of dual numbers is, that they make it possible to keep track of the derivative during the actual calculation, using forward accumulation. Simply by replacing all numbers in our calculations with dual numbers, we will end up with the answer together with the derivative. Wikipedia has a very nice article, that explains this in more details: Automatic Differentation. The article also list several arithmetric rules for dual numbers.

For the Mandelbox, we have a defining function R(p), which returns the length of p, after having been through a fixed number of iterations of the Mandelbox formula: scale*spherefold(boxfold(z))+p. The DE is then DE = R/DR, where DR is the length of the gradient of R.

R is a scalar-valued vector function. To find the gradient we need to find the derivative along the x,y, and z direction. We can do this using dual vectors and evaluate the three directions, e.g. for the x-direction, evaluate \(R(p_r + \epsilon (1,0,0))\). In practice, it is more convenient to keep track of all three dual vectors during the calculation, since we can reuse part of the calculations. So we have to use a 3×3 matrix to track our derivatives during the calculation.

Here is some example code for the Mandelbox:

The 3×3 matrix dz contains our three dual vectors (they are stored as columns in the matrix, dz[0], dz[1], dz[2]).

In order to calculate the dual numbers, we need to know how to calculate the length of z, and how to divide by the length squared (for sphere folds).

Using the definition of the product for dual numbers, we have:

\(|z|^2 = z \cdot z = z_r^2 + (2 z_r \cdot z_d)*\epsilon\)

For the length, we can use the power rule, as defined on Wikipedia:

\(|z_r + z_d \epsilon| = \sqrt{z_r^2 + (2 z_r \cdot z_d)*\epsilon}
= |z_r| + \frac{(z_r \cdot z_d)}{|z_r|}*\epsilon\)
 
Using the rule for division, we can derive:

\(z/|z|^2=(z_r+z_d \epsilon)/( z_r^2 + 2 z_r \cdot z_d \epsilon)\)
\( = z_r/z_r^2 + \epsilon (z_d*z_r^2-2z_r*z_r \cdot z_d)/z_r^4\)

Given these rules, it is relatively simple to update the dual vectors: For the sphereFold, we either multiply by a real number or use the division rule above. For the boxFold, there is both multiplication (sign change), and a translation by a real number, which is ignored for the dual numbers. The (real) scaling factor is also trivially applied to both real and dual vectors. Then there is the addition of the original vector, where we must remember to also add the original dual vector.

Finally, using the length as derived above, we find the length of the full gradient as:

\(DR = \sqrt{ (z_r \cdot z_x)^2 + (z_r \cdot z_y)^2 + (z_r \cdot z_z)^2 } / |z_r|\)
 
In the code example, the vectors are stored in a matrix, which makes a more compact notation possible: DR = length(z*dz)/length(z), leading to the final DE = R/DR = dot(z,z)/length(z*dz)

There are some advantages to using the dual numbers approach:

  • Compared to the four-point Makin/Buddhi finite difference approach the arbitrary epsilon (step distance) is avoided – which should give better numerical accuracy. It is also somewhat slightly faster computationally.
  • Very general – e.g. works for non-conformal cases, where running scalar derivatives fail. The images here are from a Mandelbox where a different scaling factor was applied to each direction (making them non-conformal). This is not possible to capture in a running scalar derivative.

On the other hand, the method is slower than using running scalar estimators. And it does require code changes. It should be mentioned that libraries exists for languages supporting operator overloading, such as C++.

Since we find the gradient directly in this method, we can also use it as a surface normal – this is also an advantage compared to the scalar derivates, which normally use a finite difference scheme for the normals. Using the code example the normal is:

It should be noted that in my experiments, I found the finite difference method produced better normals than the above definition. Perhaps because it smothens them? The problem was somehow solved by backstepping a little before calculating the normal, but this again introduces an arbitrary distance step.

Now, I said the scalar method was faster – and for a fixed number of ray steps it is – but let us take a closer look at the distance estimator function:

The above image shows a sliced Mandelbox.

The graph in the lower right conter shows a plot of the DE function along a line (two dimensions held fixed): The blue curve is the DE function, and the red line shows the derivative of the DE function. The function is plotted for the dual number derived DE function. We can see that our DE is well-behaved here: for a consistent DE the slope can never be higher than 1, and when we move away from the side of the Mandelbox in a perpendicular direction the derivative of the DE should be plus or minus one.

Now compare this to the scalar estimated DE:

Here we see that the DE is less optimal – the slope is ~0.5 for this particular line graph. Actually, the slope would be close to one if we omitted the ‘+1′ term for the scalar estimator, but then it overshoots slightly some places inside the Mandelbox.

We can also see that there are holes in our Mandelbox – this is because for this fixed number of ray steps, we do not get close enough to the fractal surface to hit it. So even though the scalar estimator is faster, we need to crank up the number of ray steps to achieve the same quality.

Final Remarks

The whole idea of introducing dual derivatives of the three unit vectors seems to be very similar to having a running Jacobian matrix estimator – and I believe the methods are essentially idential. After all we try to achieve the same: keeping a running record of how the R(p) function changes, when we vary the input along the axis.

But I think the dual numbers offer a nice theoretical framework for calculating the DE, and I believe they could be more accurate and faster then finite difference four point gradient methods. However, more experiments are needed before this can be asserted.

Scalar estimators will always be the fastest, but they are probably only optimal for conformal systems – for non-conformal system, it seems necessary to introduce terms that make them too conservative, as demonstrated by the Mandelbox example.

The final part contains all the stuff that didn’t fit in the previous posts, including references and links.

Distance Estimated 3D Fractals (VI): The Mandelbox

Previous parts: part I, part II, part III, part IV and part V.

After the Mandelbulb, several new types of 3D fractals appeared at Fractal Forums. Perhaps one of the most impressive and unique is the Mandelbox. It was first described in this thread, where it was introduced by Tom Lowe (Tglad). Similar to the original Mandelbrot set, an iterative function is applied to points in 3D space, and points which do not diverge are considered part of the set.

Tom Lowe has a great site, where he discusses the history of the Mandelbox, and highlights several of its properties, so in this post I’ll focus on the distance estimator, and try to make some more or less convincing arguments about why a scalar derivative works in this case.

The Mandelbulb and Mandelbrot systems use a simple polynomial formula to generate the escape-time sequence:

\(z_{n+1} = z_n^\alpha + c\)
 
The Mandelbox uses a slightly more complex transformation:

\(z_{n+1} = scale*spherefold(boxfold(z_n)) + c\)
 
I have mentioned folds before. These are simply conditional reflections.
A box fold is a similar construction: if the point, p, is outside a box with a given side length, reflect the point in the box side. Or as code:

(this must be done for each dimension. Notice, that in GLSL this can be expressed elegantly in one single operation for all dimensions: p = clamp(p,-L,L)*2.0-p)

The sphere fold is a conditional sphere inversion. If a point, p, is inside a sphere with a fixed radius, R, we will reflect the point in the sphere, e.g:

(Actually, the sphere fold used in most Mandelbox implementations is slightly more complex and adds an inner radius, where the length of the point is scaled linearly).

Now, how can we create a DE for the Mandelbox?

Again, it turns out that it is possible to create a scalar running derivative based distance estimator. I think the first scalar formula was suggested by Buddhi in this thread at fractal forums. Here is the code:

where the sphereFold and boxFold may be defined as:

It is possible to simplify this even further by storing the scalar derivative as the fourth component of a 4-vector. See Rrrola’s post for an example.

However, one thing that is missing, is an explanation of why this distance estimator works. And even though I do not completely understand the mechanism, I’ll try to justify this formula. It is not a strict derivation, but I think it offers some understanding of why the scalar distance estimator works.

A Running Scalar Derivative

Let us say, that for a given starting point, p, we obtain an length, R, after having applied a fixed number of iterations. If the length is less then \(R_{min}\), we consider the orbit to be bounded and is thus part of the fractal, otherwise it is outside the fractal set. We want to obtain a distance estimate for this point p. Now, the distance estimate must tell us how far we can go in any direction, before the final radius falls below the minimum radius, \(R_{min}\), and we hit the fractal surface. One distance estimate approximation would be to find the direction, where R decreases fastest, and do a linear extrapolation to estimate when R becomes less than \(R_{min}\):

\(DE=(R-R_{min})/DR\)
 
where DR is the magnitude of the derivative along this steepest descent (this is essentially Newton root finding).

In the previous post, we argued that the linear approximation to a vector-function is best described using the Jacobian matrix:

\(
F(p+dp) \approx F(p) + J(p)dp
\)
 
The fastest decrease is thus given by the induced matrix norm of J, since the matrix norm is the maximum of \(|J v|\) for all unit vectors v.

So, if we could calculate the (induced) matrix norm of the Jacobian, we would arrive at a linear distance estimate:

\(
DE=(R-R_{min})/\|J\|
\)

Calculating the Jacobian matrix norm sounds tricky, but let us take a look at the different transformations involved in the iteration loop: Reflections (R), Sphere Inversions (SI), Scalings (S), and Translations (T). It is also common to add a rotation (ROT) inside the iteration loop.

Now, for a given point, we will end applying an iterated sequence of operations to see if the point escapes:

\(
Mandelbox(p) = (T\circ S\circ SI\circ R\circ \ldots\circ T\circ S\circ SI\circ R)(p)
\)
 
In the previous part, we argued that the most obvious derivative for a R^3 to R^3 function is a Jacobian. According to the chain rule for Jacobians, the Jacobian for a function such as this Mandelbox(z) will be of the form:

\(
J_{Mandelbox} = J_T * J_S * J_{SI} * J_R …. J_T * J_S * J_{SI} * J_R
\)
 
In general, all of these matrices will be functions of R^3, which should be evaluated at different positions. Now, let us take a look of the individual Jacobian matrices for the Mandelbox transformations.

Translations

A translation by a constant will simply have an identity matrix as Jacobian matrix, as can be seen from the definitions.

Reflections

Consider a simple reflection in one of the coordinate system planes. The transformation matrix for this is:

\(
T_{R} = \begin{bmatrix}
1 & & \\
& 1 & \\
& & -1
\end{bmatrix}
\)
 
Now, the Jacobian of a transformation defined by multiplying with a constant matrix is simply the constant matrix itself. So the Jacobian is also simply a reflection matrix.

Rotations

A rotation (for a fixed angle and rotation vector) is also constant matrix, so the Jacobian is also simply a rotation matrix.

Scalings

The Jacobian for a uniform scaling operation is:

\(
J_S = scale*\begin{bmatrix}
1 & & \\
& 1 & \\
& & 1
\end{bmatrix}
\)
 
Sphere Inversions

Below can be seen how the sphere fold (the conditional sphere inversion) transforms a uniform 2D grid. As can be seen, the sphere inversion is an anti-conformal transformation – the angles are still 90 degrees at the intersections, except for the boundary where the sphere inversion stops.

The Jacobian for sphere inversions is the most tricky. But a derivation leads to:

\(
J_{R} = (r^2/R^2) \begin{bmatrix}
1-2x^2/R^2 & -2xy/R^2 & -2xz/R^2 \\
-2yx/R^2 & 1-2y^2/R^2 & -2yz/R^2 \\
-2zx/R^2 & -zy/R^2 & 1-2z^2/R^2
\end{bmatrix}
\)
 
Here R is the length of p, and r is radius of the inversion sphere. I have extracted the scalar front factor, so that the remaining part is an orthogonal matrix (as is also demonstrated in the derivation link).

Notice that all reflection, translation, and rotation Jacobian-matrices will not change the length of a vector when multiplied with it. The Jacobian for the Scaling matrix, will multiply the length with the scale factor, and the Jacobian for the Sphere Inversion will multiply the length by a factor of (r^2/R^2) (notice that the length of the point must evaluated at the correct point in the sequence).

Now, if we calculated the matrix norm of the Jacobian:

\(
|| J_{Mandelbox} || = || J_T * J_S * J_{SI} * J_R …. J_T * J_S * J_{SI} * J_R ||
\)
 
we can easily do it, since we do only need to keep track of the scalar factor, whenever we encounter a Scaling Jacobian or a Sphere Inversion Jacobian. All the other matrix stuff will simply not change the length of a given vector and may be ignored. Also notice, that only the sphere inversion depends on the point where the Jacobian is evaluated – if this operation was not present, we could simply count the number of scalings performed and multiply the escape length with \(2^{-scale}\).

This means that the matrix norm of the Jacobian can be calculated using only a simply scalar variable, which is scaled, whenever we apply the scaling or sphere inversion operation.

This seems to hold for all conformal transformations (strictly speaking sphere inversions and reflections are not conformal, but anti-conformal, since orientations are reversed). Wikipedia also mentions, that any function, with a Jacobian equal to a scalar times a rotational matrix, must be conformal, and it seems the converse is also true: any conformal or anti-conformal transformation in 3D has a Jacobian equal to a scalar times a orthogonal matrix.

Final Remarks

There are some reasons, why I’m not completely satisfied why the above derivation: first, the translational part of the Mandelbox transformation is not really a constant. It would be, if we were considering a Julia-type Mandelbox, where you add a fixed vector at each iteration, but here we add the starting point, and I’m not sure how to express the Jacobian of this transformation. Still, it is possible to do Julia-type Mandelbox fractals (they are quite similar), and here the derivation should be more sound. The transformations used in the Mandelbox are also conditional, and not simple reflection and sphere inversions, but I don’t think that matters with regard to the Jacobian, as long as the same conditions are used when calculating it.

Update: As Knighty pointed out in the comments below, it is possible to see why the scalar approximation works in the Mandelbrot case too:

Let us go back to the original formula:
\(f(z) = scale*spherefold(boxfold(z)) + c\)

and take a look at its Jacobian:
\(J_f = J_{scale}*J_{spherefold}*J_{boxfold} + I\)

Now by using the triangle inequality for matrix norms, we get:
\(||J_f|| = ||J_{scale}*J_{spherefold}*J_{boxfold} + I|| \)
\(\leq ||J_{scale}*J_{spherefold}*J_{boxfold}|| + ||I|| \)
\(= S_{scale}*S_{spherefold}*S_{boxfold} + 1 \)

where the S’s are the scalars for the given transformation. This argument can also be applied to repeated applications of the Mandelbox transformation. This means, that if we add one to the running derivative at each iteration (like in the Mandelbulb case), we get an upper bound of the true derivative. And since our distance estimate is calculated by dividing with the running derivate, this approximation yields a smaller distance estimate than the true one (which is good).

Another point is, that it is striking that we end up with the same scalar estimator as for the tetrahedron in part 3 (except that is has no sphere inversion). But for the tetrahedron, the scalar estimator was based on straight-forward arguments, so perhaps it is possible to come up with a much simpler argument for the running scalar derivative for the Mandelbox as well.

There must also be some kind of link between the gradient and the Jacobian norm. It seems, that the norm of the Jacobian should be equal to the absolute value of the length of the Mandelbox(p) function: ||J|| = |grad|MB(p)||, since they both describe how fast the length varies along the steepest descent path. This would also make the link to the gradient based numerical methods (discussed in part 5) more clear.

And finally, if we reused our argumentation for using a linear zero-point approximation of the escape length to the Mandelbulb, it just doesn’t work. Here it is necessary to introduce a log-term (\(DE= 0.5*r*log(r)/dr\)). Of course, the Mandelbulb is not composed of conformal transformations, so the “Jacobian to Scalar running derivative” argument is not valid anymore, but we already have an expression for the scalar running derivative for the Mandelbulb, and this expression does not seem to work well with the \(DE=(r-r_{min})/dr\) approximation. So it is not clear under what conditions this approximation is valid. Update: Again, Knighty makes some good arguments below in the comments for why the linear approximations holds here.

The next part is about dual numbers and distance estimation.

Distance Estimated 3D Fractals (V): The Mandelbulb & Different DE Approximations

Previous posts: part I, part II, part III and part IV.

The last post discussed the distance estimator for the complex 2D Mandelbrot:

(1)  \(DE=0.5*ln(r)*r/dr\),

with ‘dr’ being the length of the running (complex) derivative:

(2)  \(f’_n(c) = 2f_{n-1}(c)f’_{n-1}(c)+1\)

In John Hart’s paper, he used the exact same form to render a Quaternion system (using four-component Quaternions to keep track of the running derivative). In the paper, Hart never justified why the complex Mandelbrot formula also should be valid for Quaternions. A proof of this was later given by Dang, Kaufmann, and Sandin in the book Hypercomplex Iterations: Distance Estimation and Higher Dimensional Fractals (2002).

I used the same distance estimator formula, when drawing the 3D hypercomplex images in the last post – it seems to be quite generic and applicable to most polynomial escape time fractal. In this post we will take a closer look at how this formula arise.

The Mandelbulb

But first, let us briefly return to the 2D Mandelbrot equation: \(z_{n+1} = z_{n}^2+c\). Now, squaring complex numbers has a simple geometric interpretation: if the complex number is represented in polar coordinates, squaring the number corresponds to squaring the length, and doubling the angle (to the real axis).

This is probably what motivated Daniel Nylander (Twinbee) to investigate what happens when turning to spherical 3D coordinates and squaring the length and doubling the two angles here. This makes it possible to get something like the following object:


On the image above, I made made some cuts to emphasize the embedded 2D Mandelbrot.

Now, this object is not much more interesting than the triplex and Quaternion Mandelbrot from the last post. But Paul Nylander suggested that the same approach should be used for a power-8 formula instead: \(z_{n+1} = z_{n}^8+c\), something which resulted in what is now known as the Mandelbulb fractal:

The power of eight is somewhat arbitrary here. A power seven or nine object does not look much different, but unexpectedly these higher power objects display a much more interesting structure than their power two counterpart.

Here is some example Mandelbulb code:

It should be noted, that several versions of the geometric formulas exists. The one above is based on doubling angles for spherical coordinates as they are defined on Wikipedia and is the same version as Quilez has on his site. However, several places this form appears:

which results in a Mandelbulb object where the poles are similar, and where the power-2 version has the nice 2D Mandelbrot look depicted above.

I’ll not say more about the Mandelbulb and its history, because all this is very well documented on Daniel White’s site, but instead continue to discuss various distance estimators for it.

So, how did we arrive at the distance estimator in the code example above?

Following the same approach as for the 4D Quaternion Julia set, we start with our iterative function:

\(f_n(c) = f_{n-1}^8(c) + c, f_0(c) = 0\)

 
Deriving this function (formally) with respect to c, gives

(3)   \(f’_n(c) = 8f_{n-1}^7(c)f’_{n-1}(c)+1\)

where the functions above are ‘triplex’ (3 component) valued. But we haven’t defined how to multiply two spherical triplex numbers. We only know how to square them! And how do we even derive a vector valued function with respect to a vector?

The Jacobian Distance Estimator

Since we have three different function components, which we can derive with three different number components, we end up with nine possible scalar derivatives. These may be arranged in a Jacobian matrix:
 

\(
J = \begin{bmatrix}
\frac {\partial f_x}{\partial x} & \frac {\partial f_x}{\partial y} & \frac {\partial f_x}{\partial z} \\
\frac {\partial f_y}{\partial x} & \frac {\partial f_y}{\partial y} & \frac {\partial f_y}{\partial z} \\
\frac {\partial f_z}{\partial x} & \frac {\partial f_z}{\partial y} & \frac {\partial f_z}{\partial z}
\end{bmatrix}
\)
 

The Jacobian behaves like similar to the lower-dimensional derivatives, in the sense that it provides the best linear approximation to F in a neighborhood of p:
 

(4)   \(
F(p+dp) \approx F(p) + J(p)dp
\)
 

In formula (3) above this means, we have to keep track of a running matrix derivative, and use some kind of norm for this matrix in the final distance estimate (formula 1).

But calculating the Jacobian matrix above analytically is tricky (read the comments below from Knighty and check out his running matrix derivative example in the Quadray thread). Luckily, other solutions exist.

Let us start by considering the complex case once again. Here we also have a two-component function derived by a two-component number. So why isn’t the derivative of a complex number a 2×2 Jacobian matrix?

It turns out that for a complex function to be complex differentiable in every point (holomorphic), it must satisfy the Cauchy Riemann equations. And these equations reduce the four quantities in the 2×2 Jacobian to just two numbers! Notice, that the Cauchy Riemann equations are a consequence of the definition of the complex derivative in a point p: we require that the derivative (the limit of the difference) is the same, no matter from which direction we approach p (see here). Very interestingly, the holomorphic functions are exactly the functions that are conformal (angle preserving) – something which I briefly mentioned (see last part of part III) is considered a key property of fractal transformations.

What if we only considered conformal 3D transformation? This would probably imply that the Jacobian matrix of the transformation would be a scalar times a rotation matrix (see here, but notice they only claim the reverse is true). But since the rotational part of the matrix will not influence the matrix norm, this means we would only need to keep track of the scalar part – a single component running derivative. Now, the Mandelbulb power operation is not a conformal transformation. But even though I cannot explain why, it is still possible to define a scalar derivative.

The Scalar Distance Estimator

It turns out the following running scalar derivative actually works:

(5)   \(dr_n = 8|f_{n-1}(c)|^7dr_{n-1}+1\)

where ‘dr’ is a scalar function. I’m not sure who first came up with the idea of using a scalar derivative (but it might be Enforcer, in this thread.) – but it is interesting, that it works so well (it also works in many other cases, including for Quaternion Julia system). Even though I don’t understand why the scalar approach work, there is something comforting about it: remember that the original Mandelbulb was completely defined in terms of the square and addition operators. But in order to use the 3-component running derivative, we need to able to multiply two arbitrary ‘triplex’ numbers! This bothered me, since it is possible to draw the Mandelbulb using e.g. a 3D voxel approach without knowing how to multiply arbitrary numbers, so I believe it should be possible to formulate a DE-approach, that doesn’t use this extra information. And the scalar approach does exactly this.

The escape length gradient approximation

Let us return to formula (1) above:

(1)  \(DE=0.5*ln(r)*r/dr\),

The most interesting part is the running derivative ‘dr’. For the fractals encountered so far, we have been able to find analytical running derivatives (both vector and scalar valued), but as we shall see (when we get to the more complex fractals, such as the hybrid systems) it is not always possible to find an analytical formula.

Remember that ‘dr’ is the length of the f'(z) (for complex and Quaternion numbers). In analogy with the complex and quaternion case, the function must be derived with regard to the 3-component number c. Deriving a vector-valued function with regard to a vector quantity suggests the use of a Jacobian matrix. Another approach is to take the gradient of the escape length: \(dr=|\nabla |z_{n}||\) – while it is not clear to me why this is valid, it work in many cases as we will see:

David Makin and Buddhi suggested (in this thread) that instead of trying to calculate a running, analytical derivative, we could use an numerical approximation, and calculate the above mentioned gradient using the finite forwarding method we also used when calculating a surface normal in post II.

The only slightly tricky point is, that the escape length must be evaluated for the same iteration count, otherwise you get artifacts. Here is some example code:

Notice the use of the ‘last’ variable to ensure that all escapeLength’s are evaluated at the same iteration count. Also notice that ‘gradient’ is a global varible – this is because we can reuse the normalized gradient as an approximation for our surface normal and save some calculations.

The approach above is used in both Mandelbulber and Mandelbulb 3D for the cases where no analytical solution is known. On Fractal Forums it is usually refered to as the Makin/Buddhi 4-point Delta-DE formula.

The potential gradient approximation

Now we need to step back and take a closer look at the origin of the Mandelbrot distance estimation formula. There is a lot of confusion about this formula, and unfortunately I cannot claim to completely understand all of this myself. But I’m slowly getting to understand bits of it, and want to share what I found out so far:

Let us start by the original Hart paper, which introduced the distance estimation technique for 3D fractals. Hart does not derive the distance estimation formula himself, but notes that:

Now, I haven’t talked about this potential function, G(z), that Hart mentions above, but it is possible to define a potential with the properties that G(Z)=0 inside the Mandelbrot set, and positive outside. This is the first thing that puzzled me: since G(Z) tends toward zero near the border, the “log G(Z)” term, and hence the entire term will become negative! As it turns out, the “log” term in the Hart paper is wrong. (And also notice that his formula (8) is wrong too – he must take the norm of complex function f(z) inside the log function – otherwise the distance will end up being complex too)

In The Science of Fractal Images (which Hart refers to above) the authors arrive at the following formula, which I believe is correct:

Similar, in Hypercomplex Iterations the authors arrive at the same formula:

But notice that formula (3.17) is wrong here! I strongly believe it misses a factor two (in their derivation they have \(sinh(z) \approx \frac{z}{2}\) for small z – but this is not correct: \(sinh(z) \approx z\) for small z).

The approximation going from (3.16) to (3.17) is only valid for points close to the boundary (where G(z) approaches zero). This is no big problem, since for points far away we can restrict the maximum DE step, or put the object inside a bounding box, which we intersect before ray marching.

It can be shown that \(|Z_n|^{1/2^n} \to 1\) for \(n \to \infty\). By using this we end up with our well-known formula for the lower bound from above (in a slightly different notation):

(1)  \(DE=0.5*ln(r)*r/dr\),

Instead of using the above formula, we can work directly with the potential G(z). For \(n \to \infty\), G(z) may be approximated as \(G(z)=log(|z_n|)/power^n\), where ‘power’ is the polynomial power (8 for Mandelbulb). (This result can be found in e.g. Hypercomplex Iterations p. 37 for quadratic functions)

We will approximate the length of G'(z) as a numerical gradient again. This can be done using the following code:

Notice, that this time we do not have to evaluate the potential for the same number of iterations. And again we can store the gradient and reuse it as a surface normal (when normalized).


A variant using Subblue’s radiolari tweak

Quilez’ Approximation

I arrived at the formula above after reading Iñigo Quilez’ post about the Mandelbulb. There are many good tips in this post, including a fast trigonometric version, but for me the most interesting part was his DE approach: Quilez used a potential based DE, defined as:

\(DE(z) = \frac{G(z)}{|G'(z)|} \)
 
This puzzled me, since I couldn’t understand its origin. Quilez offers an explanation in this blog post, where he arrives at the formula by using a linear approximation of G(z) to calculate the distance to its zero-region. I’m not quite sure, why this approximation should be justified, but it seems a bit like an example of Newtons method for root finding. Also, as Quilez himself notes, he is missing a factor 1/2.

But if we start out by formula (3.17) above, and notes that \(sinh(G(z)) \approx G(z)\) for small G(z) (near the fractal boundary) , and that \(|Z_n|^{1/2^n} \to 1\) for \(n \to \infty\) we arrive at:

\(DE(z) = 0.5*\frac{G(z)}{|G'(z)|} \)
 
(And notice that the same two approximations are used when arriving at our well-known formula (1) at the top of the page).

Quilez’ method can be implemented using the previous code example and replacing the DE return value simply by:

If you wonder how these different methods compare, here are some informal timings of the various approaches (parameters were adjusted to give roughly identical appearances):

Sinh Potential Gradient (my approach) 1.0x
Potential Gradient (Quilez) 1.1x
Escape Length Gradient (Makin/Buddhi) 1.1x
Analytical 4.1x

The three first methods all use a four-point numerical approximation of the gradient. Since this requires four calls to the iterative function (which is where most of the computational time is spent), they are around four times slower than the analytical solution, that only uses one evaluation.

My approach is slightly slower than the other numerical approaches, but is also less approximated than the others. The numerical approximations do not behave in the same way: the Makin/Buddhi approach seems more sensible to choosing the right EPS size in the numerical approximation of the gradient.

As to which function is best, this requires some more testing on various systems. My guess is, that they will provide somewhat similar results, but this must be investigated further.


The Mandelbulb can also be drawn as a Julia fractal.

Some final notes about Distance Estimators

Mathematical justification: first note, that the formulas above were derived for complex mathematics and quadratic systems (and extended to Quaternions and some higher-dimensional structures in Hypercomplex Iterations). These formulas were never proved for exotic stuff like the Mandelbulb triplex algebra or similar constructs. The derivations above were included to give a hint to the origin and construction of these DE approximations. To truly understand these formula, I think the original papers by Hubbard and Douady, and the works by John Willard Milnor should be consulted – unfortunately I couldn’t find these online. Anyway, I believe a rigorous approach would require the attention of someone with a mathematical background.

Using a lower bound as a distance estimator. The formula (3.17) above defines lower and upper bounds for a given point to the boundary of the Mandelbrot set. Throughout this entire discussion, we have simply used the lower bound as a distance estimate. But a lower bound is not good enough as a distance estimate. This can easily be realized since 0 is always a lower bound of the true distance. In order for our sphere tracing / ray marching approach to work, the lower bound must converge towards to the true distance, as it approaches zero! In our case, we are safe, because we also have an upper bound which is four times the lower bound (in the limit where the exp(G(z)) term disappears). Since the true distance must be between the lower and upper bound, the true distance converges towards the lower bound, as the lower bound get smaller.

DE’s are approximations. All our DE formulas above are only approximations – valid in the limit \(n \to \infty\), and some also only for point close to the fractal boundary. This becomes very apparent when you start rendering these structures – you will often encounter noise and artifacts. Multiplying the DE estimates by a number smaller than 1, may be used to reduce noise (this is the Fudge Factor in Fragmentarium). Another common approach is to oversample – or render images at large sizes and downscale.

Future directions. There is much more to explore and understand about Distance Estimators. For instance, the methods above use four-point numerical gradient estimation, but perhaps the primary camera ray marching could be done using directional derivatives (two point delta estimation), and thus save the four-point sampling for the non-directional stuff (AO, soft shadows, normal estimation). Automatic differentation with dual numbers (as noted in post II) may also be used to avoid the finite difference gradient estimation. It would be nice to have a better understanding of why the scalar gradients work.

The next blog post discusses the Mandelbox fractal.

Distance Estimated 3D Fractals (II): Lighting and Coloring

The first post discussed how to find the intersection between a camera ray and a fractal, but did not talk about how to color the object. There are two steps involved here: setting up a coloring scheme for the fractal object itself, and the shading (lighting) of the object.

Lights and shading

Since we are raymarching our objects, we can use the standard lighting techniques from ray tracing. The most common form of lightning is to use something like Blinn-Phong, and calculate approximated ambient, diffuse, and specular light based on the position of the light source and the normal of the fractal object.

Surface Normal

So how do we obtain a normal of a fractal surface?

A common method is to probe the Distance Estimator function in small steps along the coordinate system axis and use the numerical gradient obtained from this as the normal (since the normal must point in the direction where the distance field increase most rapidly). This is an example of the finite difference method for numerical differentiation. The following snippet shows how the normal may be calculated:

The original Hart paper also suggested that alternatively, the screen space depth buffer could be used to determine the normal – but this seems to be both more difficult and less accurate.

Finally, as fpsunflower noted in this thread it is possible to use Automatic Differentiation with dual numbers, to obtain a gradient without having to introduce an arbitrary epsilon sampling distance.

Ambient Occlusion

Besides the ambient, diffuse, and specular light from Phong-shading, one thing that really improves the quality and depth illusion of a 3D model is ambient occlusion. In my first post, I gave an example of how the number of ray steps could be used as a very rough measure of how occluded the geometry is (I first saw this at Subblue’s site – his Quaternion Julia page has some nice illustrations of this effect). This ‘ray step AO‘ approach has its shortcomings though: for instance, if the camera ray is nearly parallel to a surface (a grazing incidence) a lot of steps will be used, and the surface will be darkened, even if it is not occluded at all.

Another approach is to sample the Distance Estimator at points along the normal of the surface and use this information to put together a measure for the Ambient Occlusion. This is a more intuitive method, but comes with some other shortcomings – i.e. new parameters are needed to control the distance between the samplings and their relative weights with no obvious default settings. A description of this ‘normal sampling AO‘ approach can be found in Iñigo Quilez’s introduction to distance field rendering.

In Fragmentarium, I’ve implemented both methods: The ‘DetailAO’ parameter controls the distance at which the normal is sampled for the ‘normal sampling AO’ method. If ‘DetailAO’ is set to zero, the ‘ray step AO’ method is used.

Other lighting effects

Besides Phong shading and ambient occlusion, all the usual tips and tricks in ray tracing may be applied:

Glow – can be added simply by mixing in a color based on the number of ray steps taken (points close to the fractal will use more ray steps, even if they miss the fractal, so pixels close to the object will glow).

Fog - is also great for adding to the depth perception. Simply blend in the background color based on the distance from the camera.

Hard shadows are also straight forward – check if the ray from the surface point to the light source is occluded.

Soft shadows: Iñigo Quilez has a good description of doing softened shadows.

Reflections are pretty much the same – reflect the camera ray in the surface normal, and mix in the color of whatever the reflected ray hits.

The effects above are all implemented in Fragmentarium as well. Numerous other extensions could be added to the raytracer: for example, environment mapping using HDRI panoramic maps provides very natural lighting and is easy to apply for the user, simulated depth-of-field also adds great depth illusion to an image, and can be calculated in reasonable time and quality using screen space buffers, and more complex materials could also be added.

Coloring

Fractal objects with a uniform base color and simple colored light sources can produce great images. But algorithmic coloring is a powerful tool for bringing the fractals to life.

Algorithmic color use one or more quantities, determined by looking at the orbit or the escape point or time.

Orbit traps is a popular way to color fractals. This method keeps track of how close the orbit comes to a chosen geometric object. Typical traps include keeping track of the minimum distance to the coordinate system center, or to simple geometric shapes like planes, lines, or spheres. In Fragmentarium, many of the systems use a 4-component vector to keep track of the minimum distance to the three x=0, y=0, and z=0 planes and to the distance from origo. These are mapped to color through the X,Y,Z, and R parameters in the ‘Coloring’ tab.

The iteration count is the number of iterations it takes before the orbit diverges (becomes larger than the escape radius). Since this is an integer number it is prone to banding, which is discussed later in this post. One way to avoid this is by using a smooth fractional iteration count:

(For a derivation of this quantity, see for instance here)

Here ‘iteration’ is the number of iterations, and dot(z,z) is the square of the escape time length. There are a couple of things to notice. First, the formula involves a characteristic scale, referring to the scaling factor in the problem (e.g. 2 for a standard Mandelbrot, 3 for a Menger). It is not always possible to obtain such a number (e.g. for Mandelboxes or hybrid systems). Secondly, if the smooth iteration count is used to lookup a color in a palette, offset may be ignored, which means the second term can be dropped. Finally, which ‘log’ functions should be used? This does not matter if only they are used consistently: since all different log functions are proportional, the ratio of two logs does not depend on the base used. For the inner logs (e.g. log(dot(,z))), changing the log will result in a constant offset to the overall term, so again this will just result in an offset in the palette lookup.


The lower half of this image use a smooth iteration count.

Conditional Path Coloring

(I made this name up – I’m not sure there is an official name, but I’ve seen the technique used several times in Fractal Forums posts.)

Some fractals may have conditional branches inside their iteration loop (sometimes disguised as an ‘abs’ operator). The Mandelbox is a good example: the sphere fold performs different actions depending on whether the length of the iterated point is smaller or larger than a set threshold. This makes it possible to keep track of a color variable, which is updated depending on the path taken.

Many other types of coloring are also possible, for example based on the normal of the surface, spherical angles of the escape time points, and so on. Many of the 2D fractal coloring types can also be applied to 3D fractals. UltraFractal has a nice list of 2D coloring types.

Improving Quality

Some visual effects and colorings, are based on integer quantities – for example glow is based on on the number of ray steps. This will result in visible boundaries between the discrete steps, an artifact called banding.

The smooth iteration count introduced above is one way to get rid of banding, but it is not generally applicable. A more generic approach is to add some kind of noise into the system. For instance, by scaling the length of the first ray step for each pixel by a random number, the banding will disappear – at the cost of introducing some noise.

Personally, I much prefer noise to banding – in fact I like the noisy, grainy look, but that is a matter of preference.

Another important issue is aliasing: if only one ray is traced per pixel, the image is prone to aliasing and artifacts. Using more than one sample will remove aliasing and reduce noise. There are many ways to oversample the image – different strategies exist for choosing the samples in a way that optimizes the image quality and there are different ways of weighting (filtering) the samples for each pixel. Physical Based Rendering has a very good chapter on sampling and filtering for ray tracing, and this particular chapter is freely available here:

In Fragmentarium there is some simple oversampling built-in – by setting the ‘AntiAlias’ variable, a number of samples are chosen (on a uniform grid). They are given the same weight (box filtered). I usually only use this for 2D fractals – because they render faster, which allows for a high number of samples. For 3D renders, I normally render a high resolution image, and downscale it in a image editing program – this seems to create better quality images for the same number of samples.

Part III discusses how to derive and work with Distance Estimator functions.