diff options
| author |  Antonio Sanchez <cantonios@google.com> | 2021-01-11 11:30:01 -0800 |
|---|---|---|
| committer | Antonio Sanchez <cantonios@google.com> | 2021-01-11 11:30:01 -0800 |
| commit | 20440849794789eb9d9d29bc834296ce0e73b05c (patch) | |
| tree | 1e270d021f6ee33267a25602f473bcfa6432ec78 | |
| parent | 3daf92c7a5e8288d47839e47a461b8d249d206f1 (diff) | |
Remove TODO from Transform::computeScaleRotation()
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.
| -rw-r--r-- | Eigen/src/Geometry/Transform.h | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/Eigen/src/Geometry/Transform.h b/Eigen/src/Geometry/Transform.h index 7497b31ac..7d258c01d 100644 --- a/Eigen/src/Geometry/Transform.h +++ b/Eigen/src/Geometry/Transform.h @@ -1097,7 +1097,7 @@ template<typename Scalar, int Dim, int Mode, int Options> template<typename RotationMatrixType, typename ScalingMatrixType> 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); 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> 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); Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1 |