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Remove TODO from Transform::computeScaleRotation()

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Upon investigation, `JacobiSVD` is significantly faster than `BDCSVD`
for small matrices (twice as fast for 2x2, 20% faster for 3x3,
1% faster for 10x10).  Since the majority of cases will be small,
let's stick with `JacobiSVD`.  See !361.
This commit is contained in:
Antonio Sanchez 2021-01-11 11:30:01 -08:00
parent 3daf92c7a5
commit 2044084979
1 changed files with 2 additions and 2 deletions
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Eigen/src/Geometry/Transform.h
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@ -1097,7 +1097,7 @@ template<typename Scalar, int Dim, int Mode, int Options>
template<typename RotationMatrixType, typename ScalingMatrixType> template<typename RotationMatrixType, typename ScalingMatrixType>
EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const
{ {
// TODO: investigate BDCSVD implementation. // Note that JacobiSVD is faster than BDCSVD for small matrices.
JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV); JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV);
Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1 Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
@ -1127,7 +1127,7 @@ template<typename Scalar, int Dim, int Mode, int Options>
template<typename ScalingMatrixType, typename RotationMatrixType> template<typename ScalingMatrixType, typename RotationMatrixType>
EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const
{ {
// TODO: investigate BDCSVD implementation. // Note that JacobiSVD is faster than BDCSVD for small matrices.
JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV); JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV);
Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1 Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1