C# C++；基于特征矩阵库的代码翻译 我有下面的代码，我用C++来翻译成C++。p> template <typename PointT> inline unsigned int pcl::SamplingSurfaceNormal<PointT>::computeMeanAndCovarianceMatrix (const pcl::PointCloud<PointT> &cloud, Eigen::Matrix3f &covariance_matrix, Eigen::Vector4f &centroid) { // create the buffer on the stack which is much faster than using cloud.points[indices[i]] and centroid as a buffer Eigen::Matrix<float, 1, 9, Eigen::RowMajor> accu = Eigen::Matrix<float, 1, 9, Eigen::RowMajor>::Zero (); std::size_t point_count = 0; for (std::size_t i = 0; i < cloud.points.size (); i++) { if (!isFinite (cloud[i])) { continue; } ++point_count; accu [0] += cloud[i].x * cloud[i].x; accu [1] += cloud[i].x * cloud[i].y; accu [2] += cloud[i].x * cloud[i].z; accu [3] += cloud[i].y * cloud[i].y; // 4 accu [4] += cloud[i].y * cloud[i].z; // 5 accu [5] += cloud[i].z * cloud[i].z; // 8 accu [6] += cloud[i].x; accu [7] += cloud[i].y; accu [8] += cloud[i].z; } accu /= static_cast<float> (point_count); centroid[0] = accu[6]; centroid[1] = accu[7]; centroid[2] = accu[8]; centroid[3] = 0; covariance_matrix.coeffRef (0) = accu [0] - accu [6] * accu [6]; covariance_matrix.coeffRef (1) = accu [1] - accu [6] * accu [7]; covariance_matrix.coeffRef (2) = accu [2] - accu [6] * accu [8]; covariance_matrix.coeffRef (4) = accu [3] - accu [7] * accu [7]; covariance_matrix.coeffRef (5) = accu [4] - accu [7] * accu [8]; covariance_matrix.coeffRef (8) = accu [5] - accu [8] * accu [8]; covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1); covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2); covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5); return (static_cast<unsigned int> (point_count)); }

看吧

coeffRef

只提供对底层数据数组的访问。因此，对协方差矩阵[i，j]的转换应该是等价的。请注意，表达式

协方差矩阵.coeffRef（k）

只给出了数据数组中的

k

第th个元素，与存储顺序无关。是的，对于原始代码来说，使用

coeffRef（i，j）

，IMO会更有意义

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之所以会出现这种情况（我在这里猜测，ggael和chtz可能能够确认/反驳），是因为Eigen使用了大量表达式模板来确定何时以及如何计算表达式的一部分。有些依赖于矩阵的存储顺序，有些则不然。在不依赖于存储顺序（例如标量*矩阵）的情况下，表达式可以“短路”，从而减少编译器为决定如何计算给定表达式而必须经历的步骤数量，从而减少编译时间。如果我们显式地声明

coeffRef

，那么我们会告诉编译器，我们谈论的是一个具有存储的具体对象，而不是一个表达式。

是的，但是“这是一个重载版本的DenseCoeffsBase:：coeffRef（Index）const，用于绕过表达式求值器的创建，从而节省了编译工作。有关详细信息，请参见DenseCoeffsBase:：coeffRef（Index）const.`mean？我想知道它从矩阵中得到了什么功能。我假设它假设矩阵是RowMajor，并相应地检索值，但就我所知，这是绝对没有写的，而且我已经查看了同样不透明的源代码…是否有特定的原因将其移植到C#而不是将其作为dll调用？是的，我只需要代码的一小部分，不需要包装这个类的开销，因为它有一个复杂的依赖层次结构。

private int ComputeMeanAndCovarianceMatrix(
    PointCloud cloud,
    Matrix<float> covariance_matrix,
    MathNet.Numerics.LinearAlgebra.Vector<float> centroid)
{
    int point_count = 0;
    Matrix<float> accu = Matrix<float>.Build.DenseOfRowMajor(1, 9, Enumerable.Repeat(0.0f, 9));

    for (int i = 0; i < cloud.Vertices.Length; ++i)
    {
        //if (!isFinite(cloud.Vertices[i].Point.))
        //{
        //  continue;
        //}

        ++point_count;
        accu[0, 0] += cloud.Vertices[i].Point.X * cloud.Vertices[i].Point.X;
        accu[0, 1] += cloud.Vertices[i].Point.X * cloud.Vertices[i].Point.Y;
        accu[0, 2] += cloud.Vertices[i].Point.X * cloud.Vertices[i].Point.Z;
        accu[0, 3] += cloud.Vertices[i].Point.Y * cloud.Vertices[i].Point.Y; // 4
        accu[0, 4] += cloud.Vertices[i].Point.Y * cloud.Vertices[i].Point.Z; // 5
        accu[0, 5] += cloud.Vertices[i].Point.Z * cloud.Vertices[i].Point.Z; // 8
        accu[0, 6] += cloud.Vertices[i].Point.X;
        accu[0, 7] += cloud.Vertices[i].Point.Y;
        accu[0, 8] += cloud.Vertices[i].Point.Z;
    }
    accu /= point_count;

    centroid[0] = accu[0, 6];
    centroid[1] = accu[0, 7];
    centroid[2] = accu[0, 8];
    centroid[3] = 0;

    covariance_matrix[0, 0] = accu[0, 0] - accu[0, 6] * accu[0, 6];
    covariance_matrix[0, 1] = accu[0, 1] - accu[0, 6] * accu[0, 7];
    covariance_matrix[0, 2] = accu[0, 2] - accu[0, 6] * accu[0, 8];
    covariance_matrix[1, 1] = accu[0, 3] - accu[0, 7] * accu[0, 7];
    covariance_matrix[1, 2] = accu[0, 4] - accu[0, 7] * accu[0, 8];
    covariance_matrix[2, 2] = accu[0, 5] - accu[0, 8] * accu[0, 8];
    covariance_matrix[1, 0] = covariance_matrix[0, 1];
    covariance_matrix[2, 0] = covariance_matrix[0, 2];
    covariance_matrix[2, 1] = covariance_matrix[1, 2];

    return point_count;
}