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Replaced Eigen::Vector3f p; with Eigen::Matrix<S,3,1> p; (support for double precision).

This commit is contained in:
Federico Ponchio 2014-02-12 15:07:19 +00:00
parent 2464b63495
commit 57880ef231
1 changed files with 11 additions and 11 deletions
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22
vcg/space/fitting3.h
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@ -8,7 +8,7 @@
* \ *
* All rights reserved. *
* *
* This program is free software; you can redistribute it and/or modify *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
@ -49,7 +49,7 @@ void ComputeCovarianceMatrix(const std::vector<Point3<S> > &pointVec, Point3<S>
// second cycle: compute the covariance matrix
m.setZero();
Eigen::Vector3f p;
Eigen::Matrix<S,3,1> p;
for(pit = pointVec.begin(); pit != pointVec.end(); ++pit) {
((*pit)-barycenter).ToEigenVector(p);
m+= p*p.transpose(); // outer product
@ -64,13 +64,13 @@ The algorithm used is the classical Covariance matrix eigenvector approach.
template <class S>
void FitPlaneToPointSet(const std::vector< Point3<S> > & pointVec, Plane3<S> & plane)
{
Eigen::Matrix3f covMat=Eigen::Matrix3f::Zero();
Eigen::Matrix<S,3,3> covMat = Eigen::Matrix<S,3,3>::Zero();
Point3<S> b;
ComputeCovarianceMatrix(pointVec,b,covMat);
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eig(covMat);
Eigen::Vector3f eval = eig.eigenvalues();
Eigen::Matrix3f evec = eig.eigenvectors();
Eigen::SelfAdjointEigenSolver<Eigen::Matrix<S,3,3> > eig(covMat);
Eigen::Matrix<S,3,1> eval = eig.eigenvalues();
Eigen::Matrix<S,3,3> evec = eig.eigenvectors();
eval = eval.cwiseAbs();
int minInd;
eval.minCoeff(&minInd);
@ -107,7 +107,7 @@ void ComputeWeightedCovarianceMatrix(const std::vector<Point3<S> > &pointVec, co
// The weights are applied to the points transposed to the origin.
m.setZero();
Eigen::Matrix<S,3,3> A;
Eigen::Vector3f p;
Eigen::Matrix<S,3,1> p;
for( pit = pointVec.begin(),wit=weightVec.begin(); pit != pointVec.end(); ++pit,++wit)
{
(((*pit)-bp)*(*wit)).ToEigenVector(p);
@ -123,13 +123,13 @@ The algorithm used is the classical Covariance matrix eigenvector approach.
template <class S>
void WeightedFitPlaneToPointSet(const std::vector< Point3<S> > & pointVec, const std::vector<S> &weightVec, Plane3<S> & plane)
{
Eigen::Matrix3f covMat=Eigen::Matrix3f::Zero();
Eigen::Matrix<S,3,3> covMat = Eigen::Matrix<S,3,3>::Zero();
Point3<S> b;
ComputeWeightedCovarianceMatrix(pointVec,weightVec, b,covMat);
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> eig(covMat);
Eigen::Vector3f eval = eig.eigenvalues();
Eigen::Matrix3f evec = eig.eigenvectors();
Eigen::SelfAdjointEigenSolver<Eigen::Matrix<S,3,3> > eig(covMat);
Eigen::Matrix<S,3,1> eval = eig.eigenvalues();
Eigen::Matrix<S,3,3> evec = eig.eigenvectors();
eval = eval.cwiseAbs();
int minInd;
eval.minCoeff(&minInd);