* References: *
* * @author Kai Burjack */ public static class Sphere { /** * Implementation of a Hierarchical Triangular Mesh structure to index the sample points on the unit sphere for accelerating 1-nearest neighbor searches. * * @author Kai Burjack */ private static final class Node { private static final int MAX_OBJECTS_PER_NODE = 32; private float v0x, v0y, v0z; private float v1x, v1y, v1z; private float v2x, v2y, v2z; private float cx, cy, cz; private float arc; private ArrayList objects; private Node[] children; Node() { this.children = new Node[8]; float s = 1f; this.arc = 2.0f * (float) Math.PI; /* * See: https://arxiv.org/ftp/cs/papers/0701/0701164.pdf */ this.children[0] = new Node(-s, 0, 0, 0, 0, s, 0, s, 0); this.children[1] = new Node(0, 0, s, s, 0, 0, 0, s, 0); this.children[2] = new Node(s, 0, 0, 0, 0, -s, 0, s, 0); this.children[3] = new Node(0, 0, -s, -s, 0, 0, 0, s, 0); this.children[4] = new Node(-s, 0, 0, 0, -s, 0, 0, 0, s); this.children[5] = new Node(0, 0, s, 0, -s, 0, s, 0, 0); this.children[6] = new Node(s, 0, 0, 0, -s, 0, 0, 0, -s); this.children[7] = new Node(0, 0, -s, 0, -s, 0, -s, 0, 0); } private Node(float x0, float y0, float z0, float x1, float y1, float z1, float x2, float y2, float z2) { this.v0x = x0; this.v0y = y0; this.v0z = z0; this.v1x = x1; this.v1y = y1; this.v1z = z1; this.v2x = x2; this.v2y = y2; this.v2z = z2; cx = (v0x + v1x + v2x) / 3.0f; cy = (v0y + v1y + v2y) / 3.0f; cz = (v0z + v1z + v2z) / 3.0f; float invCLen = Math.invsqrt(cx * cx + cy * cy + cz * cz); cx *= invCLen; cy *= invCLen; cz *= invCLen; // Compute maximum radius around triangle centroid float arc1 = greatCircleDist(cx, cy, cz, v0x, v0y, v0z); float arc2 = greatCircleDist(cx, cy, cz, v1x, v1y, v1z); float arc3 = greatCircleDist(cx, cy, cz, v2x, v2y, v2z); float dist = Math.max(Math.max(arc1, arc2), arc3); /* * Convert radius into diameter, but also take into account the linear * arccos approximation we use. * This value was obtained empirically! */ dist *= 1.7f; this.arc = dist; } private void split() { float w0x = v1x + v2x; float w0y = v1y + v2y; float w0z = v1z + v2z; float len0 = Math.invsqrt(w0x * w0x + w0y * w0y + w0z * w0z); w0x *= len0; w0y *= len0; w0z *= len0; float w1x = v0x + v2x; float w1y = v0y + v2y; float w1z = v0z + v2z; float len1 = Math.invsqrt(w1x * w1x + w1y * w1y + w1z * w1z); w1x *= len1; w1y *= len1; w1z *= len1; float w2x = v0x + v1x; float w2y = v0y + v1y; float w2z = v0z + v1z; float len2 = Math.invsqrt(w2x * w2x + w2y * w2y + w2z * w2z); w2x *= len2; w2y *= len2; w2z *= len2; children = new Node[4]; /* * See: https://arxiv.org/ftp/cs/papers/0701/0701164.pdf */ children[0] = new Node(v0x, v0y, v0z, w2x, w2y, w2z, w1x, w1y, w1z); children[1] = new Node(v1x, v1y, v1z, w0x, w0y, w0z, w2x, w2y, w2z); children[2] = new Node(v2x, v2y, v2z, w1x, w1y, w1z, w0x, w0y, w0z); children[3] = new Node(w0x, w0y, w0z, w1x, w1y, w1z, w2x, w2y, w2z); } private void insertIntoChild(Vector3f o) { for (int i = 0; i < children.length; i++) { Node c = children[i]; /* * Idea: Test whether ray from origin towards point cuts triangle * * See: http://math.stackexchange.com/questions/1244512/point-in-a-spherical-triangle-test */ if (isPointOnSphericalTriangle(o.x, o.y, o.z, c.v0x, c.v0y, c.v0z, c.v1x, c.v1y, c.v1z, c.v2x, c.v2y, c.v2z, 1E-6f)) { c.insert(o); return; } } } void insert(Vector3f object) { if (children != null) { insertIntoChild(object); return; } if (objects != null && objects.size() == MAX_OBJECTS_PER_NODE) { split(); for (int i = 0; i < MAX_OBJECTS_PER_NODE; i++) insertIntoChild((Vector3f) objects.get(i)); objects = null; insertIntoChild(object); } else { if (objects == null) objects = new ArrayList(MAX_OBJECTS_PER_NODE); objects.add(object); } } /** * This is essentially a ray cast from the origin of the sphere to the point to test and then checking whether that ray goes through the triangle. *
* Reference: Fast,
* Minimum Storage Ray/Triangle Intersection
*/
private static boolean isPointOnSphericalTriangle(float x, float y, float z, float v0X, float v0Y, float v0Z, float v1X, float v1Y, float v1Z, float v2X, float v2Y,
float v2Z, float epsilon) {
float edge1X = v1X - v0X;
float edge1Y = v1Y - v0Y;
float edge1Z = v1Z - v0Z;
float edge2X = v2X - v0X;
float edge2Y = v2Y - v0Y;
float edge2Z = v2Z - v0Z;
float pvecX = y * edge2Z - z * edge2Y;
float pvecY = z * edge2X - x * edge2Z;
float pvecZ = x * edge2Y - y * edge2X;
float det = edge1X * pvecX + edge1Y * pvecY + edge1Z * pvecZ;
if (det > -epsilon && det < epsilon)
return false;
float tvecX = -v0X;
float tvecY = -v0Y;
float tvecZ = -v0Z;
float invDet = 1.0f / det;
float u = (tvecX * pvecX + tvecY * pvecY + tvecZ * pvecZ) * invDet;
if (u < 0.0f || u > 1.0f)
return false;
float qvecX = tvecY * edge1Z - tvecZ * edge1Y;
float qvecY = tvecZ * edge1X - tvecX * edge1Z;
float qvecZ = tvecX * edge1Y - tvecY * edge1X;
float v = (x * qvecX + y * qvecY + z * qvecZ) * invDet;
if (v < 0.0f || u + v > 1.0f)
return false;
float t = (edge2X * qvecX + edge2Y * qvecY + edge2Z * qvecZ) * invDet;
return t >= epsilon;
}
private int child(float x, float y, float z) {
for (int i = 0; i < children.length; i++) {
Node c = children[i];
if (isPointOnSphericalTriangle(x, y, z, c.v0x, c.v0y, c.v0z, c.v1x, c.v1y, c.v1z, c.v2x, c.v2y, c.v2z, 1E-5f))
return i;
}
// No child found. This can happen in 'nearest()' when querying possible nearby nodes
return 0;
}
/**
* Reference: https://en.wikipedia.org/
*/
private float greatCircleDist(float x1, float y1, float z1, float x2, float y2, float z2) {
float dot = x1 * x2 + y1 * y2 + z1 * z2;
/*
* Just use a linear function, because we (mostly) do less-than comparisons on the result.
* We just need a linear function which:
* f(-1) = PI
* f(0) = PI/2
* f(1) = 0
*/
return (float) (-Math.PIHalf * dot + Math.PIHalf);
//return (float) Math.acos(dot);
}
float nearest(float x, float y, float z) {
return nearest(x, y, z, Float.POSITIVE_INFINITY);
}
float nearest(float x, float y, float z, float n) {
float gcd = greatCircleDist(x, y, z, cx, cy, cz);
/*
* If great-circle-distance between query point and centroid is larger than the current smallest distance 'n' plus the great circle diameter 'arc', we abort here,
* because then it is not possible for any point in the triangle patch to be closer to the query point than 'n'.
*/
/*
* Yes, we are subtracting two great-circle distances from one another here, which we did not even compute correctly
* using our overly linear arccos approximation. But the 1.7 factor above will take care that we still stay conservative
* enough here and not rejecting triangle patches which would contain samples nearer than 'n'.
*/
if (gcd - arc > n)
return n;
float nr = n;
if (children != null) {
int num = children.length, mod = num-1;
for (int i = child(x, y, z), c = 0; c < num; i = (i + 1) & mod, c++) {
float n1 = children[i].nearest(x, y, z, nr);
nr = Math.min(n1, nr);
}
return nr;
}
for (int i = 0; objects != null && i < objects.size(); i++) {
Vector3f o = (Vector3f) objects.get(i);
float d = greatCircleDist(o.x, o.y, o.z, x, y, z);
if (d < nr)
nr = d;
}
return nr;
}
}
private boolean onHemisphere;
private int numSamples;
private int numCandidates = 60; // <- use a reasonable default
private long seed;
/**
* Create a new instance of {@link Sphere} to configure and generate 'best candidate' sample positions on the unit sphere.
*/
public Sphere() {}
/**
* Generate 'best candidate' sample positions and store the coordinates of all generated samples into the given xyzs float array.
*
* This method performs heap allocations, so should be used sparingly.
*
* @param xyzs
* will hold the x, y and z coordinates of all samples in the order XYZXYZXYZ....
* This array must have a length of at least numSamples
* @return this
*/
public Sphere generate(final float[] xyzs) {
final IntHolder i = new IntHolder();
return generate(new Callback3d() {
public void onNewSample(float x, float y, float z) {
xyzs[3 * i.value + 0] = x;
xyzs[3 * i.value + 1] = y;
xyzs[3 * i.value + 2] = z;
i.value++;
}
});
}
/**
* Generate 'best candidate' sample positions and store the coordinates of all generated samples into the given xyzs FloatBuffer.
*
* The samples will be written starting at the current position of the FloatBuffer. The position of the FloatBuffer will not be modified. *
* This method performs heap allocations, so should be used sparingly.
*
* @param xyzs
* will hold the x, y and z coordinates of all samples in the order XYZXYZXYZ....
* This FloatBuffer must have at least numSamples remaining elements.
* The position of the buffer will not be modified by this method
* @return this
*/
public Sphere generate(final FloatBuffer xyzs) {
final IntHolder i = new IntHolder();
final int pos = xyzs.position();
return generate(new Callback3d() {
public void onNewSample(float x, float y, float z) {
xyzs.put(pos + 3 * i.value + 0, x);
xyzs.put(pos + 3 * i.value + 1, y);
xyzs.put(pos + 3 * i.value + 2, z);
i.value++;
}
});
}
/**
* Set the seed to initialize the pseudo-random number generator with.
*
* @param seed
* the seed value
* @return this
*/
public Sphere seed(long seed) {
this.seed = seed;
return this;
}
/**
* Set the number of samples to generate.
*
* @param numSamples
* the number of samples
* @return this
*/
public Sphere numSamples(int numSamples) {
this.numSamples = numSamples;
return this;
}
/**
* Set the number of candidates to try for each generated sample.
*
* @param numCandidates
* the number of candidates to try
* @return this
*/
public Sphere numCandidates(int numCandidates) {
this.numCandidates = numCandidates;
return this;
}
/**
* Set whether to generate samples on a hemisphere around the +Z axis.
*
* The default is false, which will generate samples on the whole unit sphere.
*
* @param onHemisphere
* whether to generate samples on the hemisphere
* @return this
*/
public Sphere onHemisphere(boolean onHemisphere) {
this.onHemisphere = onHemisphere;
return this;
}
/**
* Generate 'best candidate' sample call the given callback for each generated sample.
*
* This method performs heap allocations, so should be used sparingly.
*
* @param callback
* will be called with the coordinates of each generated sample position
* @return this
*/
public Sphere generate(Callback3d callback) {
Random rnd = new Random(seed);
Node otree = new Node();
for (int i = 0; i < numSamples; i++) {
float bestX = Float.NaN, bestY = Float.NaN, bestZ = Float.NaN, bestDist = 0.0f;
for (int c = 0; c < numCandidates; c++) {
/*
* Random point on sphere
*
* Reference: http://mathworld.wolfram.com/
*/
float x1, x2;
do {
x1 = rnd.nextFloat() * 2.0f - 1.0f;
x2 = rnd.nextFloat() * 2.0f - 1.0f;
} while (x1 * x1 + x2 * x2 > 1.0f);
float sqrt = (float) Math.sqrt(1.0 - x1 * x1 - x2 * x2);
float x = 2 * x1 * sqrt;
float y = 2 * x2 * sqrt;
float z = 1.0f - 2.0f * (x1 * x1 + x2 * x2);
if (onHemisphere) {
z = Math.abs(z);
}
float minDist = otree.nearest(x, y, z);
if (minDist > bestDist) {
bestDist = minDist;
bestX = x;
bestY = y;
bestZ = z;
}
}
callback.onNewSample(bestX, bestY, bestZ);
otree.insert(new Vector3f(bestX, bestY, bestZ));
}
return this;
}
}
/**
* Simple quatree that can handle points and 1-nearest neighbor search.
*
* @author Kai Burjack
*/
private static class QuadTree {
private static final int MAX_OBJECTS_PER_NODE = 32;
// Constants for the quadrants of the quadtree
private static final int PXNY = 0;
private static final int NXNY = 1;
private static final int NXPY = 2;
private static final int PXPY = 3;
private float minX, minY, hs;
private ArrayList objects;
private QuadTree[] children;
QuadTree(float minX, float minY, float size) {
this.minX = minX;
this.minY = minY;
this.hs = size * 0.5f;
}
private void split() {
children = new QuadTree[4];
children[NXNY] = new QuadTree(minX, minY, hs);
children[PXNY] = new QuadTree(minX + hs, minY, hs);
children[NXPY] = new QuadTree(minX, minY + hs, hs);
children[PXPY] = new QuadTree(minX + hs, minY + hs, hs);
}
private void insertIntoChild(Vector2f o) {
children[quadrant(o.x, o.y)].insert(o);
}
void insert(Vector2f object) {
if (children != null) {
insertIntoChild(object);
return;
}
if (objects != null && objects.size() == MAX_OBJECTS_PER_NODE) {
split();
for (int i = 0; i < objects.size(); i++)
insertIntoChild((Vector2f) objects.get(i));
objects = null;
insertIntoChild(object);
} else {
if (objects == null)
objects = new ArrayList(MAX_OBJECTS_PER_NODE);
objects.add(object);
}
}
private int quadrant(float x, float y) {
if (x < minX + hs) {
if (y < minY + hs)
return NXNY;
return NXPY;
}
if (y < minY + hs)
return PXNY;
return PXPY;
}
float nearest(float x, float y, float lowerBound, float upperBound) {
float ub = upperBound;
if (x < minX - upperBound || x > minX + hs * 2 + upperBound || y < minY - upperBound
|| y > minY + hs * 2 + upperBound)
return ub;
if (children != null) {
for (int i = quadrant(x, y), c = 0; c < 4; i = (i + 1) & 3, c++) {
float n1 = children[i].nearest(x, y, lowerBound, ub);
ub = Math.min(n1, ub);
if (ub <= lowerBound)
return lowerBound;
}
return ub;
}
float ub2 = ub * ub;
float lb2 = lowerBound * lowerBound;
for (int i = 0; objects != null && i < objects.size(); i++) {
Vector2f o = (Vector2f) objects.get(i);
float d = o.distanceSquared(x, y);
if (d <= lb2)
return lowerBound;
if (d < ub2)
ub2 = d;
}
return (float) Math.sqrt(ub2);
}
}
/**
* Generates Best Candidate samples on a unit disk.
*
* @author Kai Burjack
*/
public static class Disk {
private int numSamples;
private int numCandidates = 60; // <- use a reasonable default
private long seed;
/**
* Create a new instance of {@link Disk} to configure and generate 'best candidate' sample positions on the unit disk.
*/
public Disk() {}
/**
* Set the seed to initialize the pseudo-random number generator with.
*
* @param seed
* the seed value
* @return this
*/
public Disk seed(long seed) {
this.seed = seed;
return this;
}
/**
* Set the number of samples to generate.
*
* @param numSamples
* the number of samples
* @return this
*/
public Disk numSamples(int numSamples) {
this.numSamples = numSamples;
return this;
}
/**
* Set the number of candidates to try for each generated sample.
*
* @param numCandidates
* the number of candidates to try
* @return this
*/
public Disk numCandidates(int numCandidates) {
this.numCandidates = numCandidates;
return this;
}
/**
* Generate 'best candidate' sample positions and store the coordinates of all generated samples into the given xys float array.
*
* This method performs heap allocations, so should be used sparingly.
*
* @param xys
* will hold the x and y coordinates of all samples in the order XYXYXY....
* This array must have a length of at least numSamples
* @return this
*/
public Disk generate(final float[] xys) {
final IntHolder i = new IntHolder();
return generate(new Callback2d() {
public void onNewSample(float x, float y) {
xys[2 * i.value + 0] = x;
xys[2 * i.value + 1] = y;
i.value++;
}
});
}
/**
* Generate 'best candidate' sample positions and store the coordinates of all generated samples into the given xys FloatBuffer.
*
* The samples will be written starting at the current position of the FloatBuffer. The position of the FloatBuffer will not be modified. *
* This method performs heap allocations, so should be used sparingly.
*
* @param xys
* will hold the x and y coordinates of all samples in the order XYXYXY.... This FloatBuffer must have at least numSamples remaining elements. The
* position of the buffer will not be modified by this method
* @return this
*/
public Disk generate(final FloatBuffer xys) {
final IntHolder i = new IntHolder();
final int pos = xys.position();
return generate(new Callback2d() {
public void onNewSample(float x, float y) {
xys.put(pos + 3 * i.value + 0, x);
xys.put(pos + 3 * i.value + 1, y);
i.value++;
}
});
}
/**
* Generate 'best candidate' sample positions and call the given callback for each generated sample.
*
* This method performs heap allocations, so should be used sparingly.
*
* @param callback
* will be called with the coordinates of each generated sample position
* @return this
*/
public Disk generate(Callback2d callback) {
QuadTree qtree = new QuadTree(-1, -1, 2);
Random rnd = new Random(seed);
for (int i = 0; i < numSamples; i++) {
float bestX = 0, bestY = 0, bestDist = 0.0f;
for (int c = 0; c < numCandidates; c++) {
float x, y;
do {
x = rnd.nextFloat() * 2.0f - 1.0f;
y = rnd.nextFloat() * 2.0f - 1.0f;
} while (x * x + y * y > 1.0f);
float minDist = qtree.nearest(x, y, bestDist, Float.POSITIVE_INFINITY);
if (minDist > bestDist) {
bestDist = minDist;
bestX = x;
bestY = y;
}
}
callback.onNewSample(bestX, bestY);
qtree.insert(new Vector2f(bestX, bestY));
}
return this;
}
}
/**
* Generates Best Candidate samples on a unit quad.
*
* @author Kai Burjack
*/
public static class Quad {
private int numSamples;
private int numCandidates = 60; // <- use a reasonable default
private long seed;
/**
* Create a new instance of {@link Quad} to configure and generate 'best candidate' sample positions on the unit quad.
*/
public Quad() {}
/**
* Set the seed to initialize the pseudo-random number generator with.
*
* @param seed
* the seed value
* @return this
*/
public Quad seed(long seed) {
this.seed = seed;
return this;
}
/**
* Set the number of samples to generate.
*
* @param numSamples
* the number of samples
* @return this
*/
public Quad numSamples(int numSamples) {
this.numSamples = numSamples;
return this;
}
/**
* Set the number of candidates to try for each generated sample.
*
* @param numCandidates
* the number of candidates to try
* @return this
*/
public Quad numCandidates(int numCandidates) {
this.numCandidates = numCandidates;
return this;
}
/**
* Generate 'best candidate' sample positions and store the coordinates of all generated samples into the given xyzs float array.
*
* This method performs heap allocations, so should be used sparingly.
*
* @param xyzs
* will hold the x, y and z coordinates of all samples in the order XYZXYZXYZ....
* This array must have a length of at least numSamples
* @return this
*/
public Quad generate(final float[] xyzs) {
final IntHolder i = new IntHolder();
return generate(new Callback2d() {
public void onNewSample(float x, float y) {
xyzs[2 * i.value + 0] = x;
xyzs[2 * i.value + 1] = y;
i.value++;
}
});
}
/**
* Generate 'best candidate' sample positions and store the coordinates of all generated samples into the given xys FloatBuffer.
*
* The samples will be written starting at the current position of the FloatBuffer. The position of the FloatBuffer will not be modified. *
* This method performs heap allocations, so should be used sparingly.
*
* @param xys
* will hold the x and y coordinates of all samples in the order XYXYXY.... This FloatBuffer must have at least numSamples remaining elements. The position of
* the buffer will not be modified by this method
* @return this
*/
public Quad generate(final FloatBuffer xys) {
final IntHolder i = new IntHolder();
final int pos = xys.position();
return generate(new Callback2d() {
public void onNewSample(float x, float y) {
xys.put(pos + 3 * i.value + 0, x);
xys.put(pos + 3 * i.value + 1, y);
i.value++;
}
});
}
/**
* Generate 'best candidate' sample positions and call the given callback for each generated sample.
*
* This method performs heap allocations, so should be used sparingly.
*
* @param callback
* will be called with the coordinates of each generated sample position
* @return this
*/
public Quad generate(Callback2d callback) {
QuadTree qtree = new QuadTree(-1, -1, 2);
Random rnd = new Random(seed);
for (int i = 0; i < numSamples; i++) {
float bestX = 0, bestY = 0, bestDist = 0.0f;
for (int c = 0; c < numCandidates; c++) {
float x = rnd.nextFloat() * 2.0f - 1.0f;
float y = rnd.nextFloat() * 2.0f - 1.0f;
float minDist = qtree.nearest(x, y, bestDist, Float.POSITIVE_INFINITY);
if (minDist > bestDist) {
bestDist = minDist;
bestX = x;
bestY = y;
}
}
callback.onNewSample(bestX, bestY);
qtree.insert(new Vector2f(bestX, bestY));
}
return this;
}
}
/**
* Simple octree for points and 1-nearest neighbor distance query.
*
* @author Kai Burjack
*/
private static class Octree {
private static final int MAX_OBJECTS_PER_NODE = 32;
// Constants for the octants of the octree
private static final int PXNYNZ = 0;
private static final int NXNYNZ = 1;
private static final int NXPYNZ = 2;
private static final int PXPYNZ = 3;
private static final int PXNYPZ = 4;
private static final int NXNYPZ = 5;
private static final int NXPYPZ = 6;
private static final int PXPYPZ = 7;
private float minX, minY, minZ, hs;
private ArrayList objects;
private Octree[] children;
Octree(float minX, float minY, float minZ, float size) {
this.minX = minX;
this.minY = minY;
this.minZ = minZ;
this.hs = size * 0.5f;
}
private void split() {
children = new Octree[8];
children[NXNYNZ] = new Octree(minX, minY, minZ, hs);
children[PXNYNZ] = new Octree(minX + hs, minY, minZ, hs);
children[NXPYNZ] = new Octree(minX, minY + hs, minZ, hs);
children[PXPYNZ] = new Octree(minX + hs, minY + hs, minZ, hs);
children[NXNYPZ] = new Octree(minX, minY, minZ + hs, hs);
children[PXNYPZ] = new Octree(minX + hs, minY, minZ + hs, hs);
children[NXPYPZ] = new Octree(minX, minY + hs, minZ + hs, hs);
children[PXPYPZ] = new Octree(minX + hs, minY + hs, minZ + hs, hs);
}
private void insertIntoChild(Vector3f o) {
children[octant(o.x, o.y, o.z)].insert(o);
}
void insert(Vector3f object) {
if (children != null) {
insertIntoChild(object);
return;
}
if (objects != null && objects.size() == MAX_OBJECTS_PER_NODE) {
split();
for (int i = 0; i < objects.size(); i++)
insertIntoChild((Vector3f) objects.get(i));
objects = null;
insertIntoChild(object);
} else {
if (objects == null)
objects = new ArrayList(MAX_OBJECTS_PER_NODE);
objects.add(object);
}
}
private int octant(float x, float y, float z) {
if (x < minX + hs)
if (y < minY + hs) {
if (z < minZ + hs)
return NXNYNZ;
return NXNYPZ;
} else if (z < minZ + hs)
return NXPYNZ;
else
return NXPYPZ;
else if (y < minY + hs) {
if (z < minZ + hs)
return PXNYNZ;
return PXNYPZ;
} else if (z < minZ + hs)
return PXPYNZ;
else
return PXPYPZ;
}
float nearest(float x, float y, float z, float lowerBound, float upperBound) {
float up = upperBound;
if (x < minX - upperBound || x > minX + hs * 2 + upperBound || y < minY - upperBound || y > minY + hs * 2 + upperBound ||
z < minZ - upperBound || z > minZ + hs * 2 + upperBound)
return up;
if (children != null) {
for (int i = octant(x, y, z), c = 0; c < 8; i = (i + 1) & 7, c++) {
float n1 = children[i].nearest(x, y, z, lowerBound, up);
up = Math.min(n1, up);
if (up <= lowerBound)
return lowerBound;
}
return up;
}
float up2 = up * up;
float lb2 = lowerBound * lowerBound;
for (int i = 0; objects != null && i < objects.size(); i++) {
Vector3f o = (Vector3f) objects.get(i);
float d = o.distanceSquared(x, y, z);
if (d <= lb2)
return lowerBound;
if (d < up2)
up2 = d;
}
return (float) Math.sqrt(up2);
}
}
/**
* Generates Best Candidate samples inside a unit cube.
*
* @author Kai Burjack
*/
public static class Cube {
private int numSamples;
private int numCandidates = 60; // <- use a reasonable default
private long seed;
/**
* Create a new instance of {@link Cube} to configure and generate 'best candidate' sample positions
* on the unit cube with each sample tried numCandidates number of times, and call the
* given callback for each sample generate.
*/
public Cube() {}
/**
* Set the seed to initialize the pseudo-random number generator with.
*
* @param seed
* the seed value
* @return this
*/
public Cube seed(long seed) {
this.seed = seed;
return this;
}
/**
* Set the number of samples to generate.
*
* @param numSamples
* the number of samples
* @return this
*/
public Cube numSamples(int numSamples) {
this.numSamples = numSamples;
return this;
}
/**
* Set the number of candidates to try for each generated sample.
*
* @param numCandidates
* the number of candidates to try
* @return this
*/
public Cube numCandidates(int numCandidates) {
this.numCandidates = numCandidates;
return this;
}
/**
* Generate 'best candidate' sample positions and store the coordinates of all generated samples into the given xyzs float array.
*
* This method performs heap allocations, so should be used sparingly.
*
* @param xyzs
* will hold the x, y and z coordinates of all samples in the order XYZXYZXYZ....
* This array must have a length of at least numSamples
* @return this
*/
public Cube generate(final float[] xyzs) {
final IntHolder i = new IntHolder();
return generate(new Callback3d() {
public void onNewSample(float x, float y, float z) {
xyzs[3 * i.value + 0] = x;
xyzs[3 * i.value + 1] = y;
xyzs[3 * i.value + 2] = z;
i.value++;
}
});
}
/**
* Generate 'best candidate' sample positions and store the coordinates of all generated samples into the given xyzs FloatBuffer.
*
* The samples will be written starting at the current position of the FloatBuffer. The position of the FloatBuffer will not be modified. *
* This method performs heap allocations, so should be used sparingly.
*
* @param xyzs
* will hold the x, y and z coordinates of all samples in the order XYZXYZXYZ....
* This FloatBuffer must have at least numSamples remaining elements.
* The position of the buffer will not be modified by this method
* @return this
*/
public Cube generate(final FloatBuffer xyzs) {
final IntHolder i = new IntHolder();
final int pos = xyzs.position();
return generate(new Callback3d() {
public void onNewSample(float x, float y, float z) {
xyzs.put(pos + 3 * i.value + 0, x);
xyzs.put(pos + 3 * i.value + 1, y);
xyzs.put(pos + 3 * i.value + 2, z);
i.value++;
}
});
}
/**
* Generate 'best candidate' sample positions and call the given callback for each generated sample.
*
* This method performs heap allocations, so should be used sparingly. * * @param callback * will be called with the coordinates of each generated sample position * @return this */ public Cube generate(Callback3d callback) { Octree octree = new Octree(-1, -1, -1, 2); Random rnd = new Random(seed); for (int i = 0; i < numSamples; i++) { float bestX = 0, bestY = 0, bestZ = 0, bestDist = 0.0f; for (int c = 0; c < numCandidates; c++) { float x = rnd.nextFloat() * 2.0f - 1.0f; float y = rnd.nextFloat() * 2.0f - 1.0f; float z = rnd.nextFloat() * 2.0f - 1.0f; float minDist = octree.nearest(x, y, z, bestDist, Float.POSITIVE_INFINITY); if (minDist > bestDist) { bestDist = minDist; bestX = x; bestY = y; bestZ = z; } } callback.onNewSample(bestX, bestY, bestZ); octree.insert(new Vector3f(bestX, bestY, bestZ)); } return this; } } }