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- #include "fast_winding_number.h"
- #include <vector>
- #include <iostream>
- #include <igl/parallel_for.h>
- void fast_winding_number_precompute(const Eigen::MatrixXd & P,
- const Eigen::MatrixXd & N,
- const Eigen::VectorXd & A,
- const std::vector<std::vector<int> > & point_indices,
- const std::vector<Eigen::Matrix<int,8,1>, Eigen::aligned_allocator<Eigen::Matrix<int,8,1>>> & children,
- const int expansion_order,
- Eigen::MatrixXd & CM,
- Eigen::VectorXd & R,
- Eigen::MatrixXd & EC
- ){
- int m = children.size();
- int num_terms;
- if(expansion_order == 0){
- num_terms = 3;
- } else if(expansion_order ==1){
- num_terms = 3 + 9;
- } else if(expansion_order == 2){
- num_terms = 3 + 9 + 27;
- } else {
- assert(false);
- }
-
- R.resize(m);
- CM.resize(m,3);
- EC.resize(m,num_terms);
- EC = Eigen::MatrixXd::Zero(m,num_terms);
- std::function< void(const int) > helper;
- helper = [&helper,
- &P,&N,&A,&expansion_order,&point_indices,&children,&EC,&R,&CM]
- (const int index)-> void
- {
- double sum_area = 0;
-
- Eigen::RowVector3d masscenter = Eigen::RowVector3d::Zero();
- Eigen::RowVector3d zeroth_expansion = Eigen::RowVector3d::Zero();
- double areatotal = 0.0;
- for(int j = 0; j < point_indices.at(index).size(); j++){
- int curr_point_index = point_indices.at(index).at(j);
-
- areatotal += A(curr_point_index);
- masscenter += A(curr_point_index)*P.row(curr_point_index);
- zeroth_expansion += A(curr_point_index)*N.row(curr_point_index);
- }
-
- masscenter = masscenter/areatotal;
- CM.row(index) = masscenter;
- EC.block<1,3>(index,0) = zeroth_expansion;
-
- double max_norm = 0;
- double curr_norm;
-
- for(int i = 0; i < point_indices.at(index).size(); i++){
- //Get max distance from center of mass:
- int curr_point_index = point_indices.at(index).at(i);
- Eigen::RowVector3d point = P.row(curr_point_index)-masscenter;
- curr_norm = point.norm();
- if(curr_norm > max_norm){
- max_norm = curr_norm;
- }
-
- //Calculate higher order terms if necessary
- Eigen::Matrix3d TempCoeffs;
- if(EC.cols() >= (3+9)){
- TempCoeffs = A(curr_point_index) * point.transpose() * N.row(curr_point_index);
- EC.block<1,9>(index,3) += Eigen::Map<Eigen::RowVectorXd>(TempCoeffs.data(), TempCoeffs.size());
- }
-
- if(EC.cols() == (3+9+27)){
- for(int k = 0; k < 3; k++){
- TempCoeffs = 0.5 * point(k) * (A(curr_point_index) * point.transpose() * N.row(curr_point_index)) ;
- EC.block<1,9>(index,12+9*k) += Eigen::Map<Eigen::RowVectorXd>(TempCoeffs.data(), TempCoeffs.size());
- }
- }
- }
-
- R(index) = max_norm;
- if(children.at(index)(0) != -1)
- {
- for(int i = 0; i < 8; i++){
- int child = children.at(index)(i);
- helper(child);
- }
- }
- };
- helper(0);
- }
- double direct_eval(const Eigen::RowVector3d & loc, const Eigen::RowVector3d & anorm){
- double wn = (loc(0)*anorm(0) + loc(1)*anorm(1) + loc(2)*anorm(2))/(4.0*M_PI*std::pow(loc.norm(),3));
- if(std::isnan(wn)){
- return 0.5;
- }else{
- return wn;
- }
- }
- double expansion_eval(const Eigen::RowVector3d & loc, const Eigen::RowVectorXd & EC){
- double wn = direct_eval(loc,EC.head<3>());
- double r = loc.norm();
- if(EC.size()>3){
- Eigen::Matrix3d SecondDerivative = Eigen::Matrix3d::Identity()/(4.0*M_PI*std::pow(r,3));
- SecondDerivative += -3.0*loc.transpose()*loc/(4.0*M_PI*std::pow(r,5));
- Eigen::RowVectorXd derivative_vector = Eigen::Map<Eigen::RowVectorXd>(SecondDerivative.data(), SecondDerivative.size());
- wn += derivative_vector.cwiseProduct(EC.segment<9>(3)).sum();
- }
- if(EC.size()>3+9){
- Eigen::Matrix3d ThirdDerivative;
- for(int i = 0; i < 3; i++){
- ThirdDerivative = 15.0*loc(i)*loc.transpose()*loc/(4.0*M_PI*std::pow(r,7));
- Eigen::Matrix3d Diagonal;
- Diagonal << loc(i), 0, 0,
- 0, loc(i), 0,
- 0, 0, loc(i);
- Eigen::Matrix3d RowCol = Eigen::Matrix3d::Zero();
- RowCol.row(i) = loc;
- RowCol = RowCol + RowCol.transpose();
- ThirdDerivative += -3.0/(4.0*M_PI*std::pow(r,5)) * (RowCol + Diagonal);
- Eigen::RowVectorXd derivative_vector = Eigen::Map<Eigen::RowVectorXd>(ThirdDerivative.data(), ThirdDerivative.size());
- wn += derivative_vector.cwiseProduct(EC.segment<9>(12 + i*9)).sum();
- }
- }
- return wn;
- }
- void fast_winding_number(const Eigen::MatrixXd & P,
- const Eigen::MatrixXd & N,
- const Eigen::VectorXd & A,
- const std::vector<std::vector<int> > & point_indices,
- const std::vector<Eigen::Matrix<int,8,1>, Eigen::aligned_allocator<Eigen::Matrix<int,8,1>>> & children,
- const Eigen::MatrixXd & CM,
- const Eigen::VectorXd & R,
- const Eigen::MatrixXd & EC,
- const Eigen::MatrixXd & Q,
- const double & beta,
- Eigen::VectorXd & WN
- ){
- int m = Q.rows();
- WN.resize(m);
-
- std::function< double(const Eigen::RowVector3d, const std::vector<int>) > helper;
- helper = [&helper,
- &P,&N,&A,
- &point_indices,&children,
- &CM,&R,&EC,&beta]
- (const Eigen::RowVector3d query, const std::vector<int> near_indices)-> double
- {
- std::vector<int> new_near_indices;
- double wn = 0;
- for(int i = 0; i < near_indices.size(); i++){
- int index = near_indices.at(i);
- //Leaf Case, Brute force
- if(children.at(index)(0) == -1){
- for(int j = 0; j < point_indices.at(index).size(); j++){
- int curr_row = point_indices.at(index).at(j);
- wn += direct_eval(P.row(curr_row)-query,N.row(curr_row)*A(curr_row));
- }
- }
- //Non-Leaf Case
- else {
- for(int child = 0; child < 8; child++){
- int child_index = children.at(index)(child);
- if(point_indices.at(child_index).size() > 0){
- if((CM.row(child_index)-query).norm() > beta*R(child_index)){
- if(children.at(child_index)(0) == -1){
- for(int j = 0; j < point_indices.at(child_index).size(); j++){
- int curr_row = point_indices.at(child_index).at(j);
- wn += direct_eval(P.row(curr_row)-query,N.row(curr_row)*A(curr_row));
- }
- }else{
- wn += expansion_eval(CM.row(child_index)-query,EC.row(child_index));
- }
- }else {
- new_near_indices.emplace_back(child_index);
- }
- }
- }
- }
- }
- if(new_near_indices.size() > 0){
- wn += helper(query,new_near_indices);
- }
- return wn;
- };
-
-
- if(beta >= 0){
- std::vector<int> near_indices_start = {0};
- igl::parallel_for(m,[&](int iter){
- WN(iter) = helper(Q.row(iter),near_indices_start);
- },1000);
- } else {
- igl::parallel_for(m,[&](int iter){
- double wn = 0;
- for(int j = 0; j <P.rows(); j++){
- wn += direct_eval(P.row(j)-Q.row(iter),N.row(j)*A(j));
- }
- WN(iter) = wn;
- },1000);
- }
- }
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