void GlobalFun::find_original_neighbors(CGrid::iterator starta, CGrid::iterator enda,
CGrid::iterator startb, CGrid::iterator endb, double radius)
{
double radius2 = radius*radius;
double iradius16 = -4/radius2;
const double PI = 3.1415926;
for(CGrid::iterator dest = starta; dest != enda; dest++)
{
CVertex &v = *(*dest);
Point3f &p = v.P();
for(CGrid::iterator origin = startb; origin != endb; origin++)
{
CVertex &t = *(*origin);
Point3f &q = t.P();
Point3f diff = p-q;
double dist2 = diff.SquaredNorm();
if(dist2 < radius2)
{
v.original_neighbors.push_back((*origin)->m_index);
}
}
}
}
void WLOP::computeRepulsionTerm(CMesh* samples)
{
double repulsion_power = para->getDouble("Repulsion Power");
bool need_density = para->getBool("Need Compute Density");
double radius = para->getDouble("CGrid Radius");
double radius2 = radius * radius;
double iradius16 = -para->getDouble("H Gaussian Para")/radius2;
cout << endl<< endl<< "Sample Neighbor Size:" << samples->vert[0].neighbors.size() << endl<< endl;
for(int i = 0; i < samples->vert.size(); i++)
{
CVertex& v = samples->vert[i];
for (int j = 0; j < v.neighbors.size(); j++)
{
CVertex& t = samples->vert[v.neighbors[j]];
Point3f diff = v.P() - t.P();
double dist2 = diff.SquaredNorm();
double len = sqrt(dist2);
if(len <= 0.001 * radius) len = radius*0.001;
double w = exp(dist2*iradius16);
double rep = w * pow(1.0 / len, repulsion_power);
if (need_density)
{
rep *= samples_density[t.m_index];
}
repulsion[i] += diff * rep;
repulsion_weight_sum[i] += rep;
}
}
}
Point3f RandomUnitVec(){
Point3f k;
do {
k=Point3f(
(random(200)-100)*0.01,
(random(200)-100)*0.01,
(random(200)-100)*0.01
);
} while (k.SquaredNorm()>1.0);
return k.Normalize();
}
void WLOP::computeAverageTerm(CMesh* samples, CMesh* original)
{
double average_power = para->getDouble("Average Power");
bool need_density = para->getBool("Need Compute Density");
double radius = para->getDouble("CGrid Radius");
double radius2 = radius * radius;
double iradius16 = -para->getDouble("H Gaussian Para")/radius2;
cout << "Original Size:" << samples->vert[0].original_neighbors.size() << endl;
for(int i = 0; i < samples->vert.size(); i++)
{
CVertex& v = samples->vert[i];
for (int j = 0; j < v.original_neighbors.size(); j++)
{
CVertex& t = original->vert[v.original_neighbors[j]];
Point3f diff = v.P() - t.P();
double dist2 = diff.SquaredNorm();
double w = 1;
if (para->getBool("Run Anisotropic LOP"))
{
double len = sqrt(dist2);
if(len <= 0.001 * radius) len = radius*0.001;
double hn = diff * v.N();
double phi = exp(hn * hn * iradius16);
w = phi / pow(len, 2 - average_power);
}
else if (average_power < 2)
{
double len = sqrt(dist2);
if(len <= 0.001 * radius) len = radius*0.001;
w = exp(dist2 * iradius16) / pow(len, 2 - average_power);
}
else
{
w = exp(dist2 * iradius16);
}
if (need_density)
{
w *= original_density[t.m_index];
}
average[i] += t.P() * w;
average_weight_sum[i] += w;
}
}
}
void GlobalFun::other_neighbors(CGrid::iterator starta, CGrid::iterator enda,
CGrid::iterator startb, CGrid::iterator endb, double radius)
{
double radius2 = radius*radius;
for(CGrid::iterator dest = starta; dest != enda; dest++)
{
CVertex &v = *(*dest);
Point3f &p = v.P();
for(CGrid::iterator origin = startb; origin != endb; origin++)
{
CVertex &t = *(*origin);
Point3f &q = t.P();
Point3f diff = p-q;
double dist2 = diff.SquaredNorm();
if(dist2 < radius2)
{
v.neighbors.push_back((*origin)->m_index);
t.neighbors.push_back((*dest)->m_index);
}
}
}
}
void GlobalFun::computeEigenWithTheta(CMesh* _samples, double radius)
{
vector<vector<int> > neighborMap;
typedef vector<CVertex>::iterator VertexIterator;
VertexIterator begin = _samples->vert.begin();
VertexIterator end = _samples->vert.end();
neighborMap.assign(end - begin, vector<int>());
int curr_index = 0;
for (VertexIterator iter=begin; iter!=end; iter++, curr_index++)
{
if(iter->neighbors.size() <= 3)
{
iter->eigen_confidence = 0.5;
continue;
}
for(int j = 0; j < iter->neighbors.size(); j++)
{
neighborMap[curr_index].push_back(iter->neighbors[j]);
}
}
double radius2 = radius*radius;
double iradius16 = -1/radius2;
int currIndex = 0;
for (VertexIterator iter=begin; iter!=end; iter++, currIndex++)
{
if(iter->neighbors.size() <= 3)
{
iter->eigen_confidence = 0.5;
continue;
}
Matrix33d covariance_matrix;
Point3f diff;
covariance_matrix.SetZero();
int neighborIndex = -1;
int neighbor_size = iter->neighbors.size();
for (unsigned int n=0; n<neighbor_size; n++)
{
neighborIndex = neighborMap[currIndex][n];
if(neighborIndex < 0)
break;
VertexIterator neighborIter = begin + neighborIndex;
diff = iter->P() - neighborIter->P();
Point3f vm = iter->N();
Point3f tm = neighborIter->N();
double dist2 = diff.SquaredNorm();
double theta = exp(dist2*iradius16);
for (int i=0; i<3; i++)
for (int j=0; j<3; j++)
covariance_matrix[i][j] += diff[i]*diff[j] * theta;
}
Point3f eigenvalues;
Matrix33d eigenvectors;
int required_rotations;
vcg::Jacobi< Matrix33d, Point3f >(covariance_matrix, eigenvalues, eigenvectors, required_rotations);
vcg::SortEigenvaluesAndEigenvectors< Matrix33d, Point3f >(eigenvalues, eigenvectors);
double sum_eigen_value = (eigenvalues[0] + eigenvalues[1] + eigenvalues[2]);
iter->eigen_confidence = eigenvalues[0] / sum_eigen_value;
for (int d=0; d<3; d++)
iter->eigen_vector0[d] = eigenvectors[d][0];
for (int d=0; d<3; d++)
iter->eigen_vector1[d] = eigenvectors[d][1];
for (int d=0; d<3; d++)
iter->N()[d] = eigenvectors[d][2];
iter->eigen_vector0.Normalize();
iter->eigen_vector1.Normalize();
iter->N().Normalize();
}
}
double WLOP::iterate()
{
Timer time;
initVertexes();
time.start("Sample Original Neighbor Tree!!!");
GlobalFun::computeBallNeighbors(samples, original,
para->getDouble("CGrid Radius"), box);
time.end();
time.start("Sample Sample Neighbor Tree");
GlobalFun::computeBallNeighbors(samples, NULL,
para->getDouble("CGrid Radius"), samples->bbox);
time.end();
if (nTimeIterated == 0)
{
if (para->getBool("Need Compute Density"))
{
double local_density_para = 0.95;
time.start("Original Original Neighbor Tree");
GlobalFun::computeBallNeighbors(original, NULL,
para->getDouble("CGrid Radius") * local_density_para, original->bbox);
time.end();
time.start("Compute Original Density");
original_density.assign(original->vn, 0);
computeDensity(true, para->getDouble("CGrid Radius") * local_density_para);
time.end();
}
}
if (para->getBool("Need Compute Density"))
{
time.start("Compute Density For Sample");
computeDensity(false, para->getDouble("CGrid Radius"));
time.end();
}
time.start("Sample Original Neighbor Tree!!!");
GlobalFun::computeBallNeighbors(samples, original,
para->getDouble("CGrid Radius"), box);
time.end();
time.start("Compute Average Term");
computeAverageTerm(samples, original);
time.end();
time.start("Compute Repulsion Term");
computeRepulsionTerm(samples);
time.end();
double mu = para->getDouble("Repulsion Mu");
Point3f c;
for(int i = 0; i < samples->vert.size(); i++)
{
CVertex& v = samples->vert[i];
c = v.P();
if (average_weight_sum[i] > 1e-20)
{
v.P() = average[i] / average_weight_sum[i];
}
if (repulsion_weight_sum[i] > 1e-20 && mu >= 0)
{
v.P() += repulsion[i] * (mu / repulsion_weight_sum[i]);
}
if (average_weight_sum[i] > 1e-20 && repulsion_weight_sum[i] > 1e-20 )
{
Point3f diff = v.P() - c;
double move_error = sqrt(diff.SquaredNorm());
error_x += move_error;
}
}
error_x = error_x / samples->vn;
para->setValue("Current Movement Error", DoubleValue(error_x));
cout << "****finished compute WLOP error: " << error_x << endl;
if (para->getBool("Need Compute PCA"))
{
time.start("Recompute PCA");
recomputePCA_Normal();
time.end();
}
return error_x;
}
