VecN GeodesicInHeat::compute_heat(const VecN& delta,const Sparse& LapMatrix,const Sparse& areamatrix, float t)
{
Sparse A = areamatrix - t*LapMatrix;
Eigen::SimplicialCholesky<Sparse, Eigen::RowMajor> solver;
solver.compute(A);
VecN u = solver.solve(delta);
return u;
}
bool solveLinearSystem()
{
X.resize (L.rows (), B.cols ());
// Nothing to solve
if (L.rows () == 0 || B.cols () == 0)
return true;
Eigen::SimplicialCholesky<SparseMatrix, Eigen::Lower> cg;
cg.compute (L);
bool succeeded = true;
for (int i = 0; i < B.cols (); ++i)
{
Vector b = B.col (i);
X.col (i) = cg.solve (b);
if (cg.info () != Eigen::Success)
succeeded = false;
}
assignColors ();
return succeeded;
}
void GeodesicInHeat::compute_distance(const Vertex& start_vertex)
{
int n = mesh.n_vertices();
int start_idx = start_vertex.idx();
VecN delta(n);
delta.setZero();
delta(start_idx) =1.0f;
//The laplacian, gradient divergence matrix only need to compute once.
if (!initialized)
initialization();
float timestep = avglength*avglength*factor;
VecN uNeumann = compute_heat(delta,Laplacian, AreaMatrix,timestep);
VecN uDirichlet = compute_heat(delta, LaplacianDirichlet, AreaMatrix, timestep);
VecN u =0.5*uNeumann+0.5*uDirichlet;
//Method::the three boundary function will active one, if all false, display the average.
if (Neumann)
u = uNeumann;
if (Dirichlet)
u = uDirichlet;
MatMxN X = compute_gradient(Grad_x, Grad_y, Grad_z, u);
VecN div_X = compute_divergence(Div_x, Div_y, Div_z, X);
Eigen::SimplicialCholesky<Sparse, Eigen::RowMajor> Solver;
Solver.compute(Laplacian);
VecN phi = Solver.solve(div_X);
//shift phi and find out the Max G, Min G and the Max geodesic distance span along edge.
for (auto const& vertex : mesh.vertices())
{
float d1 = phi[vertex.idx()];
HeatDisplay[vertex] = d1;
float d2 = 0.0;
for (auto const& vj : mesh.vertices(vertex))
{
float d = std::abs(phi[vertex.idx()] - phi[vj.idx()]);
if (d > d2)
d2 = d;
}
if (d1 > MaxGeodesic)
MaxGeodesic = d1;
if (d2 > MaxEdgeSpan)
MaxEdgeSpan = d2;
if (d1 < MinGeodesic)
MinGeodesic = d1;
}
for (auto const& vertex : mesh.vertices())
{
HeatDisplay[vertex] = HeatDisplay[vertex] - MinGeodesic;
HeatDistance[vertex] = HeatDisplay[vertex];
}
MaxGeodesic -= MinGeodesic;
MinGeodesic = 0;
if (Neumann) {
mLogger() << "Heat: Neumann Boudary Condition, M=" << factor;
return;
}
if (Dirichlet) {
mLogger() << "Heat: Dirichlet Boudary Condition, M=" << factor;
return;
}
mLogger() << "Heat: Average Boudary Condition, M=" << factor;
}
static void
_solve_linear_system(TriMesh& src, const TriMesh& dst,
const std::vector<std::vector<int>>& tri_neighbours,
const std::vector<std::pair<int, int> >& corres,
double weights[3]
) {
int rows = 0; int cols = src.vert_num + src.poly_num - corres.size();
assert (tri_neighbours.size() == src.poly_num);
std::vector<std::pair<int, int>> soft_corres;
_setup_kd_correspondence(src, dst, soft_corres);
#if 0
std::ofstream ofs("E:/data/ScapeOriginal/build/data_ming/dt_pairs.txt");
ofs << soft_corres.size() << endl;
for (size_t i=0; i<soft_corres.size(); ++i)
ofs << soft_corres[i].first << "\t" << soft_corres[i].second << std::endl;
printf("check soft pair\n");
getchar();
#endif
for (int i=0; i<src.poly_num; ++i)
rows += 3*tri_neighbours[i].size(); //smooth part
rows += src.poly_num*3; //identity part
static std::vector<bool> vertex_flag; //indicate whether the vertex is hard constrainted by corres
if (vertex_flag.empty()) {
vertex_flag.resize(src.vert_num, false);
for (size_t i=0; i<corres.size(); ++i)
vertex_flag[corres[i].first] = true;
}
for (int i=0; i<soft_corres.size(); ++i) { //soft constraints part
if (vertex_flag[soft_corres[i].first]) continue;
++rows;
}
//vertex_real_col stores two information : unknow vertex's col(offset by
//poly_num) in X and know vertexs' corresponding index in dst mesh.
//there is no need to compute this in each iteration
static std::vector<int> vertex_real_col;
if (vertex_real_col.empty()) {
vertex_real_col.resize(src.vert_num, -1);
std::map<int, int> corres_map;
for (int i=0; i<corres.size(); ++i) corres_map.insert(std::make_pair(corres[i].first, corres[i].second));
int real_col = 0;
for (int i=0; i<src.vert_num; ++i) {
if (vertex_flag[i]) {
vertex_real_col[i] = corres_map[i];
} else {
vertex_real_col[i] = real_col;
real_col++;
}
}
assert (real_col == src.vert_num - corres.size()); //make sure indexes in corres are different from each other
}
SparseMatrix<double> A(rows, cols);
A.reserve(Eigen::VectorXi::Constant(cols, 200));
std::vector<VectorXd> Y(3, VectorXd(rows));
Y[0].setZero();Y[1].setZero();Y[2].setZero();
//precompute Q_hat^-1 in Q_hat_inverse [n v2-v1 v3-v1]^-1
src.updateNorm();
assert (!src.face_norm.empty());
std::vector<Matrix3d> Q_hat_inverse(src.poly_num);
Eigen::Matrix3d _inverse;
for (int i=0; i<src.poly_num; ++i) {
unsigned int* index_i = src.polyIndex[i].vert_index;
Vector3d v[3];
v[0] = src.face_norm[i];
v[1] = src.vertex_coord[index_i[1]] - src.vertex_coord[index_i[0]];//v2-v1
v[2] = src.vertex_coord[index_i[2]] - src.vertex_coord[index_i[0]];//v3-v1
for (int k=0; k<3; ++k)
for (int j=0; j<3; ++j)
Q_hat_inverse[i](k, j) = v[j][k];
_inverse = Q_hat_inverse[i].inverse();
Q_hat_inverse[i] = _inverse;
}
int energy_size[3] = {0, 0, 0}; //each energy part's starting index
//start establishing the large linear sparse system
double weight_smooth = weights[0];
int row = 0;
for (int i=0; i<src.poly_num; ++i) {
Eigen::Matrix3d& Q_i_hat = Q_hat_inverse[i];
unsigned int* index_i = src.polyIndex[i].vert_index;
for (size_t _j=0; _j<tri_neighbours[i].size(); ++_j) {
int j = tri_neighbours[i][_j]; //triangle index
Eigen::Matrix3d& Q_j_hat = Q_hat_inverse[j];
unsigned int* index_j = src.polyIndex[j].vert_index;
for (int k=0; k<3; ++k)
for (int dim = 0; dim < 3; ++dim)
Y[dim](row+k) = 0.0;
for (int k=0; k<3; ++k) {
A.coeffRef(row+k, i) = weight_smooth*Q_i_hat(0, k); //n
A.coeffRef(row+k, j) = -weight_smooth*Q_j_hat(0, k); //n
}
for (int k=0; k<3; ++k)
for (int p=0; p<3; ++p)
if (!vertex_flag[index_i[p]])
A.coeffRef(row+k, src.poly_num+vertex_real_col[index_i[p]]) = 0.0;
if (vertex_flag[index_i[0]]) {
for (int k=0; k<3; ++k) {
for (int dim = 0; dim < 3; ++dim)
Y[dim](row + k) += weight_smooth*(Q_i_hat(1, k)+Q_i_hat(2, k))*dst.vertex_coord[vertex_real_col[index_i[0]]][dim];
}
} else {
for (int k=0; k<3; ++k)
A.coeffRef(row+k, src.poly_num + vertex_real_col[index_i[0]]) +=
-weight_smooth*(Q_i_hat(1, k)+Q_i_hat(2, k));
}
if (vertex_flag[index_j[0]]) {
for (int k=0; k<3; ++k) {
for (int dim = 0; dim < 3; ++dim)
Y[dim](row + k) += -weight_smooth*(Q_j_hat(1, k)+Q_j_hat(2, k))*dst.vertex_coord[vertex_real_col[index_j[0]]][dim];
}
} else {
for (int k=0; k<3; ++k)
A.coeffRef(row+k, src.poly_num + vertex_real_col[index_j[0]]) +=
weight_smooth*(Q_j_hat(1, k)+Q_j_hat(2, k));
}
for (int p=1; p<3; ++p) {//v2 or v3
if (vertex_flag[index_i[p]]) {
for (int k=0; k<3; ++k)
for (int dim=0; dim<3; ++dim)
Y[dim](row + k) += -weight_smooth*Q_i_hat(p, k)*dst.vertex_coord[vertex_real_col[index_i[p]]][dim];
} else {
for (int k=0; k<3; ++k)
A.coeffRef(row+k, src.poly_num + vertex_real_col[index_i[p]]) += weight_smooth*Q_i_hat(p, k);
}
}
for (int p=1; p<3; ++p) {
if (vertex_flag[index_j[p]]) {
for (int k=0; k<3; ++k)
for (int dim=0; dim < 3; ++dim)
Y[dim](row + k) += weight_smooth*Q_j_hat(p, k)*dst.vertex_coord[vertex_real_col[index_j[p]]][dim];
} else {
for (int k=0; k<3; ++k)
A.coeffRef(row+k, src.poly_num + vertex_real_col[index_j[p]]) += -weight_smooth*Q_j_hat(p, k);
}
}
row += 3;
}
}
energy_size[0] = row;
double weight_identity = weights[1];
for (int i=0; i<src.poly_num; ++i) {
Eigen::Matrix3d& Q_i_hat = Q_hat_inverse[i];
unsigned int* index_i = src.polyIndex[i].vert_index;
Y[0](row) = weight_identity; Y[0](row+1) = 0.0; Y[0](row+2) = 0.0;
Y[1](row) = 0.0; Y[1](row+1) = weight_identity; Y[1](row+2) = 0.0;
Y[2](row) = 0.0; Y[2](row+1) = 0.0; Y[2](row+2) = weight_identity;
for (int k=0; k<3; ++k)
A.coeffRef(row+k, i) = weight_identity*Q_i_hat(0, k); //n
if (vertex_flag[index_i[0]]) {
for (int k=0; k<3; ++k)
for (int dim = 0; dim < 3; ++dim)
Y[dim](row+k) += weight_identity*(Q_i_hat(1, k)+Q_i_hat(2,k))*dst.vertex_coord[vertex_real_col[index_i[0]]][dim];
} else {
for (int k=0; k<3; ++k)
A.coeffRef(row+k, src.poly_num+vertex_real_col[index_i[0]]) = -weight_identity*(Q_i_hat(1, k)+Q_i_hat(2,k));
}
for (int p=1; p<3; ++p) {
if (vertex_flag[index_i[p]]) {
for (int k=0; k<3; ++k)
for (int dim=0; dim<3; ++dim)
Y[dim](row + k) += -weight_identity*Q_i_hat(p, k)*dst.vertex_coord[vertex_real_col[index_i[p]]][dim];
} else {
for (int k=0; k<3; ++k)
A.coeffRef(row+k, src.poly_num + vertex_real_col[index_i[p]]) = weight_identity*Q_i_hat(p, k);
}
}
row += 3;
}
energy_size[1] = row;
double weight_soft_constraint = weights[2];
for (int i=0; i<soft_corres.size(); ++i) {
if (vertex_flag[soft_corres[i].first]) continue;
A.coeffRef(row, src.poly_num + vertex_real_col[soft_corres[i].first]) = weight_soft_constraint;
for (int dim=0; dim<3; ++dim)
Y[dim](row) += weight_soft_constraint*dst.vertex_coord[soft_corres[i].second][dim];
++row;
}
energy_size[2] = row;
assert (row == rows);
//start solving the least-square problem
fprintf(stdout, "finished filling matrix\n");
Eigen::SparseMatrix<double> At = A.transpose();
Eigen::SparseMatrix<double> AtA = At*A;
Eigen::SimplicialCholesky<SparseMatrix<double>> solver;
solver.compute(AtA);
if (solver.info() != Eigen::Success) {
fprintf(stdout, "unable to defactorize AtA\n");
exit(-1);
}
VectorXd X[3];
for (int i=0; i<3; ++i) {
VectorXd AtY = At*Y[i];
X[i] = solver.solve(AtY);
Eigen::VectorXd Energy = A*X[i] - Y[i];
Eigen::VectorXd smoothEnergy = Energy.head(energy_size[0]);
Eigen::VectorXd identityEnergy = Energy.segment(energy_size[0], energy_size[1]-energy_size[0]);
Eigen::VectorXd softRegularEnergy = Energy.tail(energy_size[2]-energy_size[1]);
fprintf(stdout, "\t%lf = %lf + %lf + %lf\n",
Energy.dot(Energy), smoothEnergy.dot(smoothEnergy),
identityEnergy.dot(identityEnergy), softRegularEnergy.dot(softRegularEnergy));
}
//fill data back to src
for (int i=0; i<src.poly_num; ++i)
for (int d=0; d<3; ++d)
src.face_norm[i][d] = X[d](i);
for (size_t i=0; i<corres.size(); ++i) src.vertex_coord[corres[i].first] = dst.vertex_coord[corres[i].second];
int p = 0;
for (int i=0; i<src.vert_num; ++i) {
if (vertex_flag[i]) continue;
for (int d=0; d<3; ++d)
src.vertex_coord[i][d] = X[d](src.poly_num+p);
++p;
}
return;
}
void MsInterpolation::build_interpolation( FaceNode *subroot, MyMesh &src_mesh, MyMesh &target_mesh, double t )
{
bool leaf_node = true;
for (int i = 0; i != NR_MIN_PATCH_PER_LEVEL; i++)
{
if( subroot->next[i] != NULL)
{
leaf_node = false;
break;
}
}
/***如果是叶节点***/
if( leaf_node )
{
leaf_node_interpolation(t, subroot, src_mesh,target_mesh);
return;
}
for (int i = 0; i != NR_MIN_PATCH_PER_LEVEL; i++)
{
build_interpolation(subroot->next[i], src_mesh, target_mesh, t);
}
/***进行配准***/
int M = NR_MIN_PATCH_PER_LEVEL;
int P = subroot->P.size();
std::vector<PointList> pl;
for (int i = 0; i != P; i++)
{
int a = subroot->P[i].a;
int b = subroot->P[i].b;
FaceNode *node_a = subroot->next[a];
FaceNode *node_b = subroot->next[b];
std::vector<int> *curr_p = &subroot->pl[i];
int size = curr_p->size();
PointList pts;
for (int j = 0; j != size; j++)
{
int idx = (*curr_p)[j];//共有的点索引
PairVertex pv;
pv.a = node_a->pts[ node_a->r_idx[idx] ];
pv.b = node_b->pts[ node_b->r_idx[idx] ];
pts.push_back( pv );
}
pl.push_back( pts );
}
MultiRegistration mr(M, P, &subroot->P, &pl);
mr.init();
std::vector<MyMatrix3f> R;
std::vector<MyVector3f> T;
mr.get_R_and_T(R, T);
/***转换到统一的坐标系中***/
for (int i = 0; i != NR_MIN_PATCH_PER_LEVEL; i++)
{
int size = subroot->next[i]->pts.size();
for (int j = 0; j != size; j++)
{
subroot->next[i]->pts[j] = R[i] * subroot->next[i]->pts[j] + T[i];
}
}
/***分配空间***/
int size = subroot->pts_index.size();
subroot->pts.assign(size, MyVector3f(0, 0, 0) );
if( BLENDING )
{
int m = 0;
MyMatrixXf E;
E.resize(subroot->M.rows(), 3);
for (int i = 0; i != NR_MIN_PATCH_PER_LEVEL; i++)
{
FaceNode *node_i = subroot->next[i];
int nr_edges = subroot->edges_vector[i].size();
for (int j = 0; j != nr_edges; j++)
{
int g_from = subroot->edges_vector[i][j].a;
int g_to = subroot->edges_vector[i][j].b;
int n_from =node_i->r_idx[ g_from ];
int n_to = node_i->r_idx[ g_to ];
MyVector3f e = node_i->pts[n_from] - node_i->pts[n_to];
int from = subroot->r_idx[ g_from ];
int to = subroot->r_idx[ g_to ];
assert( subroot->M(m, from) == 1);
assert( subroot->M(m, to) == -1);
E(m, 0) = e(0);
E(m, 1) = e(1);
E(m, 2) = e(2);
m++;
}
}
E(m, 0) = 0;
E(m, 1) = 0;
E(m, 2) = 0;
Eigen::SimplicialCholesky<MySMatrixXf > solver;
solver.compute( (subroot->M.transpose() * subroot->M) );
MyMatrixXf result = solver.solve( subroot->M.transpose() * E );
int pts_size = subroot->pts_index.size();
for (int i = 0; i != pts_size; i++)
{
double x, y, z;
if( fabs( result(i, 0) ) < 1e-10 )
{
x = 0;
}else
{
x = result(i, 0);
}
if( fabs( result(i, 1) ) < 1e-10 )
{
y = 0;
}else
{
y = result(i, 1);
}
if( fabs( result(i, 2) ) < 1e-10 )
{
z = 0;
}else
{
z = result(i, 2);
}
MyVector3f vi = MyVector3f(x, y, z);
subroot->pts[i] = vi;
}
}else
{
// 如果没有blending的过程，则平均
std::vector<int> cnt;
cnt.assign( size, 0);
for (int i = 0; i != NR_MIN_PATCH_PER_LEVEL; i++)
{
FaceNode *node_i = subroot->next[i];
int pts_size = node_i->pts_index.size();
for (int j = 0; j != pts_size; j++)
{
int idx = node_i->pts_index[j];
int r_idx = subroot->r_idx[ idx ];
cnt[r_idx] += 1;
subroot->pts[ r_idx ] += node_i->pts[j];
}
}
/**** 对于重复的点的坐标取平均值 ***/
for (int i = 0; i != size; i++)
{
subroot->pts[ i ] /= cnt[i];
}
}
}
