Noise experiment with Open Shading Language

I am still looking for good procedural noise to be used as the basis for bark materials. Such noise would need some anisotropy because the cracks in bark are almast always oriented: either along the direction of the trunk or perpendicular to it, depending on the stretching characteristics during growth. Now you could scale for example Perlin noise or use Gabor noise (the latter is available in OSL as well, check for example this article), but we could also create it from scratch.

Theory

In the shader we randomly distribute a number of line segments through space and calculate the distance to these line segments. If we are close enough to a segment we add a value to our sum and in the end the value for a pixel is the sum of all these contributions. Now the essence is in the distribution of those line segments because we do not only control their length but also how far they may deviate from a preferred direction.
The code for the shader itself is quite simple (I have omitted all auxiliary functions):

shader dtnoise(
 point  Pos = P,            // texture position and scale
 float  Scale = 1,
 
 int    Np = 15,            // number of ticks (impulse) per cell
 
 int    Seed = 42,          // random seed
 
 float  Radius = 0.3,       // distance to impulse that will add to result
 float  Size = 0.7,         // length of an impulse
 vector Dir = vector(1,0,0),// direction of anisotropy
 float  Theta = M_PI/2,     // cone (half)angle. The default = 90% which means no anisotropy
 
 float  Step = 0.3,         // cutoff value for Fac output
 float  Gain = 0.3,         // brightness of the result
 
 output float Sum = 0,      // the sum of all the tick values
 output float Fac = 0       // the 
){
 point p = Pos * Scale;
 point f = floor(p);

 vector an= normalize(Dir);
 vector r = vector(an[2],an[1],an[0]); //no need to norm again
 vector uv = cross(an,r);
 vector vv = cross(an,u);

 
 int xx,yy,zz, np;
 
 int nn[3];
 
 for( xx=-1; xx<=1; xx++){
  for( yy=-1; yy<=1; yy++){
   for( zz=-1; zz<=1; zz++){
    point ff = f + vector(xx,yy,zz);
    int s=Seed;
    
    nn[0] = int(ff[0]);
    nn[1] = int(ff[1]);
    nn[2] = int(ff[2]);
    
    for( np=0; np < Np; np++){
     vector pd1 = noise("cell",ff,s);
     vector pd2 = randomcone(uv, vv, an, Theta, nn, s+1);
    
     point p1 = ff + pd1;
     point p2 = p1 + Size * pd2;
    
     float d = distance(p1,p2,p);     
     Sum += (1 - smoothstep(0, Radius, d));
     
     s+=3;
    }
   }
  }
 }
 
 Sum *= Gain;
 Fac = smoothstep(0, Step, Sum);
}

The essence is in line 44 and 45: here we pick a random point and another point along a random direction, where this random direction is restricted to a cone whose narrowness we can control with the Theta parameter. Those two points make up the start and end points of a line segment and if the distance to this line segment is small enough we a a value. (make sure that the sum of Size and Length < 1 to prevent artifacts.

Samples

With few points per cell the anisotropy is very clear (left: Theta = 0.05, right Theta = 1.51 (approx. 90 degrees):

   

 Adding points increases the complexity of the noise (we reduced the gain, the number of points is 40):

 

If we compare our noise to gabor and perlin noise (each thresholded and suitably scaled in a preferred direction) we see that the patterns are similar but each with its own character.

How useful this all is, it up to you of course :-) It is nice to have a different procedural noise in your toolbox but on the other hand, it ties you to OSL (i.e. you can't use it on the GPU) and it is relatively slow.

Code availability

As usual the full code is available on GitHub.

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