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diff --git a/third_party/eigen3/Eigen/src/Geometry/Umeyama.h b/third_party/eigen3/Eigen/src/Geometry/Umeyama.h
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--- a/third_party/eigen3/Eigen/src/Geometry/Umeyama.h
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-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_UMEYAMA_H
-#define EIGEN_UMEYAMA_H
-
-// This file requires the user to include 
-// * Eigen/Core
-// * Eigen/LU 
-// * Eigen/SVD
-// * Eigen/Array
-
-namespace Eigen { 
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-
-// These helpers are required since it allows to use mixed types as parameters
-// for the Umeyama. The problem with mixed parameters is that the return type
-// cannot trivially be deduced when float and double types are mixed.
-namespace internal {
-
-// Compile time return type deduction for different MatrixBase types.
-// Different means here different alignment and parameters but the same underlying
-// real scalar type.
-template<typename MatrixType, typename OtherMatrixType>
-struct umeyama_transform_matrix_type
-{
-  enum {
-    MinRowsAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime, OtherMatrixType::RowsAtCompileTime),
-
-    // When possible we want to choose some small fixed size value since the result
-    // is likely to fit on the stack. So here, EIGEN_SIZE_MIN_PREFER_DYNAMIC is not what we want.
-    HomogeneousDimension = int(MinRowsAtCompileTime) == Dynamic ? Dynamic : int(MinRowsAtCompileTime)+1
-  };
-
-  typedef Matrix<typename traits<MatrixType>::Scalar,
-    HomogeneousDimension,
-    HomogeneousDimension,
-    AutoAlign | (traits<MatrixType>::Flags & RowMajorBit ? RowMajor : ColMajor),
-    HomogeneousDimension,
-    HomogeneousDimension
-  > type;
-};
-
-}
-
-#endif
-
-/**
-* \geometry_module \ingroup Geometry_Module
-*
-* \brief Returns the transformation between two point sets.
-*
-* The algorithm is based on:
-* "Least-squares estimation of transformation parameters between two point patterns",
-* Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573
-*
-* It estimates parameters \f$ c, \mathbf{R}, \f$ and \f$ \mathbf{t} \f$ such that
-* \f{align*}
-*   \frac{1}{n} \sum_{i=1}^n \vert\vert y_i - (c\mathbf{R}x_i + \mathbf{t}) \vert\vert_2^2
-* \f}
-* is minimized.
-*
-* The algorithm is based on the analysis of the covariance matrix
-* \f$ \Sigma_{\mathbf{x}\mathbf{y}} \in \mathbb{R}^{d \times d} \f$
-* of the input point sets \f$ \mathbf{x} \f$ and \f$ \mathbf{y} \f$ where 
-* \f$d\f$ is corresponding to the dimension (which is typically small).
-* The analysis is involving the SVD having a complexity of \f$O(d^3)\f$
-* though the actual computational effort lies in the covariance
-* matrix computation which has an asymptotic lower bound of \f$O(dm)\f$ when 
-* the input point sets have dimension \f$d \times m\f$.
-*
-* Currently the method is working only for floating point matrices.
-*
-* \todo Should the return type of umeyama() become a Transform?
-*
-* \param src Source points \f$ \mathbf{x} = \left( x_1, \hdots, x_n \right) \f$.
-* \param dst Destination points \f$ \mathbf{y} = \left( y_1, \hdots, y_n \right) \f$.
-* \param with_scaling Sets \f$ c=1 \f$ when <code>false</code> is passed.
-* \return The homogeneous transformation 
-* \f{align*}
-*   T = \begin{bmatrix} c\mathbf{R} & \mathbf{t} \\ \mathbf{0} & 1 \end{bmatrix}
-* \f}
-* minimizing the resudiual above. This transformation is always returned as an 
-* Eigen::Matrix.
-*/
-template <typename Derived, typename OtherDerived>
-typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type
-umeyama(const MatrixBase<Derived>& src, const MatrixBase<OtherDerived>& dst, bool with_scaling = true)
-{
-  typedef typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type TransformationMatrixType;
-  typedef typename internal::traits<TransformationMatrixType>::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  typedef typename Derived::Index Index;
-
-  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
-  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename internal::traits<OtherDerived>::Scalar>::value),
-    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
-
-  enum { Dimension = EIGEN_SIZE_MIN_PREFER_DYNAMIC(Derived::RowsAtCompileTime, OtherDerived::RowsAtCompileTime) };
-
-  typedef Matrix<Scalar, Dimension, 1> VectorType;
-  typedef Matrix<Scalar, Dimension, Dimension> MatrixType;
-  typedef typename internal::plain_matrix_type_row_major<Derived>::type RowMajorMatrixType;
-
-  const Index m = src.rows(); // dimension
-  const Index n = src.cols(); // number of measurements
-
-  // required for demeaning ...
-  const RealScalar one_over_n = RealScalar(1) / static_cast<RealScalar>(n);
-
-  // computation of mean
-  const VectorType src_mean = src.rowwise().sum() * one_over_n;
-  const VectorType dst_mean = dst.rowwise().sum() * one_over_n;
-
-  // demeaning of src and dst points
-  const RowMajorMatrixType src_demean = src.colwise() - src_mean;
-  const RowMajorMatrixType dst_demean = dst.colwise() - dst_mean;
-
-  // Eq. (36)-(37)
-  const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n;
-
-  // Eq. (38)
-  const MatrixType sigma = one_over_n * dst_demean * src_demean.transpose();
-
-  JacobiSVD<MatrixType> svd(sigma, ComputeFullU | ComputeFullV);
-
-  // Initialize the resulting transformation with an identity matrix...
-  TransformationMatrixType Rt = TransformationMatrixType::Identity(m+1,m+1);
-
-  // Eq. (39)
-  VectorType S = VectorType::Ones(m);
-  if (sigma.determinant()<Scalar(0)) S(m-1) = Scalar(-1);
-
-  // Eq. (40) and (43)
-  const VectorType& d = svd.singularValues();
-  Index rank = 0; for (Index i=0; i<m; ++i) if (!internal::isMuchSmallerThan(d.coeff(i),d.coeff(0))) ++rank;
-  if (rank == m-1) {
-    if ( svd.matrixU().determinant() * svd.matrixV().determinant() > Scalar(0) ) {
-      Rt.block(0,0,m,m).noalias() = svd.matrixU()*svd.matrixV().transpose();
-    } else {
-      const Scalar s = S(m-1); S(m-1) = Scalar(-1);
-      Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose();
-      S(m-1) = s;
-    }
-  } else {
-    Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose();
-  }
-
-  if (with_scaling)
-  {
-    // Eq. (42)
-    const Scalar c = Scalar(1)/src_var * svd.singularValues().dot(S);
-
-    // Eq. (41)
-    Rt.col(m).head(m) = dst_mean;
-    Rt.col(m).head(m).noalias() -= c*Rt.topLeftCorner(m,m)*src_mean;
-    Rt.block(0,0,m,m) *= c;
-  }
-  else
-  {
-    Rt.col(m).head(m) = dst_mean;
-    Rt.col(m).head(m).noalias() -= Rt.topLeftCorner(m,m)*src_mean;
-  }
-
-  return Rt;
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_UMEYAMA_H