/* The main function! */
void Worley(
Vector3 at,
long max_order,
double[] F,
Vector3[] delta,
ulong[] ID
)
{
/* Initialize the F values to "huge" so they will be replaced by the
first real sample tests. Note we'll be storing and comparing the
SQUARED distance from the feature points to avoid lots of slow
sqrt() calls. We'll use sqrt() only on the final answer. */
for(int i = 0; i < max_order; i++)
{
F[i] = 999999.9;
}
/* Make our own local copy, multiplying to make mean(F[0])==1.0 */
Vector3 new_at = DENSITY_ADJUSTMENT * at;
/* Find the integer cube holding the hit point */
Vector3 floor_at = Vector3.Floor(new_at); /* The macro makes this part a lot faster */
IVector3 int_at = Vector3.IFloor(new_at); /* The macro makes this part a lot faster */
/* A simple way to compute the closest neighbors would be to test all
boundary cubes exhaustively. This is simple with code like:
{
long ii, jj, kk;
for (ii=-1; ii<=1; ii++) for (jj=-1; jj<=1; jj++) for (kk=-1; kk<=1; kk++)
AddSamples(int_at[0]+ii,int_at[1]+jj,int_at[2]+kk,
max_order, new_at, F, delta, ID);
}
But this wastes a lot of time working on cubes which are known to be
too far away to matter! So we can use a more complex testing method
that avoids this needless testing of distant cubes. This doubles the
speed of the algorithm. */
/* Test the central cube for closest point(s). */
AddSamples(int_at, 0,0,0, max_order, new_at, F, delta, ID);
/* We test if neighbor cubes are even POSSIBLE contributors by examining the
combinations of the sum of the squared distances from the cube's lower
or upper corners.*/
Vector3 v = new_at - floor_at;
Vector3 iv = Vector3.One - v;
Vector3 m = iv * iv;
v *= v;
/* Test 6 facing neighbors of center cube. These are closest and most
likely to have a close feature point. */
if(v.X < F[max_order-1]) AddSamples(int_at, -1, 0, 0, max_order, new_at, F, delta, ID);
if(v.Y < F[max_order-1]) AddSamples(int_at, 0,-1, 0, max_order, new_at, F, delta, ID);
if(v.Z < F[max_order-1]) AddSamples(int_at, 0, 0,-1, max_order, new_at, F, delta, ID);
if(m.X < F[max_order-1]) AddSamples(int_at, 1, 0, 0, max_order, new_at, F, delta, ID);
if(m.Y < F[max_order-1]) AddSamples(int_at, 0, 1, 0, max_order, new_at, F, delta, ID);
if(m.Z < F[max_order-1]) AddSamples(int_at, 0, 0, 1, max_order, new_at, F, delta, ID);
/* Test 12 "edge cube" neighbors if necessary. They're next closest. */
if(v.X + v.Y < F[max_order-1]) AddSamples(int_at,-1, -1, 0, max_order, new_at, F, delta, ID);
if(v.X + v.Z < F[max_order-1]) AddSamples(int_at,-1, 0, -1, max_order, new_at, F, delta, ID);
if(v.Y + v.Z < F[max_order-1]) AddSamples(int_at, 0, -1, -1, max_order, new_at, F, delta, ID);
if(m.X + m.Y < F[max_order-1]) AddSamples(int_at,+1, +1, 0, max_order, new_at, F, delta, ID);
if(m.X + m.Z < F[max_order-1]) AddSamples(int_at,+1, 0, +1, max_order, new_at, F, delta, ID);
if(m.Y + m.Z < F[max_order-1]) AddSamples(int_at, 0, +1, +1, max_order, new_at, F, delta, ID);
if(v.X + m.Y < F[max_order-1]) AddSamples(int_at,-1, +1, 0, max_order, new_at, F, delta, ID);
if(v.X + m.Z < F[max_order-1]) AddSamples(int_at,-1, 0, +1, max_order, new_at, F, delta, ID);
if(v.Y + m.Z < F[max_order-1]) AddSamples(int_at, 0, -1, +1, max_order, new_at, F, delta, ID);
if(m.X + v.Y < F[max_order-1]) AddSamples(int_at,+1, -1, 0, max_order, new_at, F, delta, ID);
if(m.X + v.Z < F[max_order-1]) AddSamples(int_at,+1, 0, -1, max_order, new_at, F, delta, ID);
if(m.Y + v.Z < F[max_order-1]) AddSamples(int_at, 0, +1, -1, max_order, new_at, F, delta, ID);
/* Final 8 "corner" cubes */
if(v.X+v.Y+v.Z < F[max_order-1]) AddSamples(int_at, -1, -1, -1, max_order, new_at, F, delta, ID);
if(v.X+v.Y+m.Z < F[max_order-1]) AddSamples(int_at, -1, -1, +1, max_order, new_at, F, delta, ID);
if(v.X+m.Y+v.Z < F[max_order-1]) AddSamples(int_at, -1, +1, -1, max_order, new_at, F, delta, ID);
if(v.X+m.Y+m.Z < F[max_order-1]) AddSamples(int_at, -1, +1, +1, max_order, new_at, F, delta, ID);
if(m.X+v.Y+v.Z < F[max_order-1]) AddSamples(int_at, +1, -1, -1, max_order, new_at, F, delta, ID);
if(m.X+v.Y+m.Z < F[max_order-1]) AddSamples(int_at, +1, -1, +1, max_order, new_at, F, delta, ID);
if(m.X+m.Y+v.Z < F[max_order-1]) AddSamples(int_at, +1, +1, -1, max_order, new_at, F, delta, ID);
if(m.X+m.Y+m.Z < F[max_order-1]) AddSamples(int_at, +1, +1, +1, max_order, new_at, F, delta, ID);
/* We're done! Convert everything to right size scale */
for(int i = 0; i < max_order; i++)
{
F[i] = (float)System.Math.Sqrt(F[i]) * (1.0 / DENSITY_ADJUSTMENT);
delta[i] *= (1.0f / DENSITY_ADJUSTMENT);
}
return;
}
/* the function to merge-sort a "cube" of samples into the current best-found
list of values. */
static void AddSamples(
IVector3 iv,
int idx, int idy, int idz,
long max_order,
Vector3 at,
double[] F,
Vector3[] delta,
ulong[] ID
)
{
Vector3 d;
Vector3 f;
double d2;
long count, i, j, index;
ulong this_id;
long xi = iv.X + idx;
long yi = iv.Y + idy;
long zi = iv.Z + idz;
/* Each cube has a random number seed based on the cube's ID number.
The seed might be better if it were a nonlinear hash like Perlin uses
for noise but we do very well with this faster simple one.
Our LCG uses Knuth-approved constants for maximal periods. */
ulong seed = (ulong)(702395077 * xi + 915488749 * yi + 2120969693 * zi);
/* How many feature points are in this cube? */
count = Poisson_count[seed >> 24]; /* 256 element lookup table. Use MSB */
seed = 1402024253 * seed + 586950981; /* churn the seed with good Knuth LCG */
for(j = 0; j < count; j++) /* test and insert each point into our solution */
{
this_id = seed;
seed = 1402024253 * seed + 586950981; /* churn */
/* compute the 0..1 feature point location's XYZ */
f.X = (seed + 0.5f) * (1.0f / 4294967296.0f);
seed = 1402024253 * seed + 586950981; /* churn */
f.Y = (seed + 0.5f) * (1.0f / 4294967296.0f);
seed = 1402024253 * seed + 586950981; /* churn */
f.Z = (seed + 0.5f) * (1.0f / 4294967296.0f);
seed = 1402024253 * seed + 586950981; /* churn */
/* delta from feature point to sample location */
d.X = xi + f.X - at.X;
d.Y = yi + f.Y - at.Y;
d.Z = zi + f.Z - at.Z;
/* Distance computation! Lots of interesting variations are
possible here!
Biased "stretched" A*dx*dx+B*dy*dy+C*dz*dz
Manhattan distance fabs(dx)+fabs(dy)+fabs(dz)
Radial Manhattan: A*fabs(dR)+B*fabs(dTheta)+C*dz
Superquadratic: pow(fabs(dx), A) + pow(fabs(dy), B) + pow(fabs(dz),C)
Go ahead and make your own! Remember that you must insure that
new distance function causes large deltas in 3D space to map into
large deltas in your distance function, so our 3D search can find
them! [Alternatively, change the search algorithm for your special
cases.]
*/
d2 = d.X * d.X + d.Y * d.Y + d.Z * d.Z; /* Euclidian distance, squared */
if(d2 < F[max_order - 1]) /* Is this point close enough to rememember? */
{
/* Insert the information into the output arrays if it's close enough.
We use an insertion sort. No need for a binary search to find
the appropriate index.. usually we're dealing with order 2,3,4 so
we can just go through the list. If you were computing order 50
(wow!!) you could get a speedup with a binary search in the sorted
F[] list. */
index = max_order;
while(index > 0 && d2 < F[index - 1])
{
index--;
}
/* We insert this new point into slot # <index> */
/* Bump down more distant information to make room for this new point. */
for(i = max_order - 2; i >= index; i--)
{
F [i + 1] = F[i];
ID [i + 1] = ID[i];
delta[i + 1] = delta[i];
}
/* Insert the new point's information into the list. */
F [index] = d2;
ID [index] = this_id;
delta[index] = d;
}
}
}
