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Update SVD Module with Options template parameter

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This commit is contained in:
Arthur
2022-02-02 00:15:44 +00:00
committed by Rasmus Munk Larsen
parent 89c6ab2385
commit 18b50458b6
24 changed files with 1112 additions and 815 deletions
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2
Eigen/src/Geometry/Hyperplane.h
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@@ -108,7 +108,7 @@ public:
if(norm <= v0.norm() * v1.norm() * NumTraits<RealScalar>::epsilon())
{
Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV);
JacobiSVD<Matrix<Scalar,2,3>, ComputeFullV> svd(m);
result.normal() = svd.matrixV().col(2);
}
else

2
Eigen/src/Geometry/Quaternion.h
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@@ -651,7 +651,7 @@ EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::setFromTwoVectors(con
{
c = numext::maxi(c,Scalar(-1));
Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV);
JacobiSVD<Matrix<Scalar,2,3>, ComputeFullV> svd(m);
Vector3 axis = svd.matrixV().col(2);
Scalar w2 = (Scalar(1)+c)*Scalar(0.5);

4
Eigen/src/Geometry/Transform.h
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@@ -1105,7 +1105,7 @@ template<typename RotationMatrixType, typename ScalingMatrixType>
EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const
{
// Note that JacobiSVD is faster than BDCSVD for small matrices.
JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV);
JacobiSVD<LinearMatrixType, ComputeFullU | ComputeFullV> svd(linear());
Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
VectorType sv(svd.singularValues());
@@ -1135,7 +1135,7 @@ template<typename ScalingMatrixType, typename RotationMatrixType>
EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const
{
// Note that JacobiSVD is faster than BDCSVD for small matrices.
JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV);
JacobiSVD<LinearMatrixType, ComputeFullU | ComputeFullV> svd(linear());
Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
VectorType sv(svd.singularValues());

2
Eigen/src/Geometry/Umeyama.h
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@@ -127,7 +127,7 @@ umeyama(const MatrixBase<Derived>& src, const MatrixBase<OtherDerived>& dst, boo
// Eq. (38)
const MatrixType sigma = one_over_n * dst_demean * src_demean.transpose();
JacobiSVD<MatrixType> svd(sigma, ComputeFullU | ComputeFullV);
JacobiSVD<MatrixType, ComputeFullU | ComputeFullV> svd(sigma);
// Initialize the resulting transformation with an identity matrix...
TransformationMatrixType Rt = TransformationMatrixType::Identity(m+1,m+1);