[英]C++ Code Translation with Eigen Matrix Library
我有以下代码,我正在将使用 Eigen 的 C++ 转换为 C#。
template <typename PointT> inline unsigned int
pcl::SamplingSurfaceNormal<PointT>::computeMeanAndCovarianceMatrix (const pcl::PointCloud<PointT> &cloud,
Eigen::Matrix3f &covariance_matrix,
Eigen::Vector4f ¢roid)
{
// 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));
}
在Eigens文件,我可以找到没有地方意味着什么叫someMatix3f.coeffRef(x)
或someMatrix3f.coeff(x)
上的二维矩阵。 这些运营商是做什么的?
请注意,我已经看过文档( https://eigen.tuxfamily.org/dox/classEigen_1_1PlainObjectBase.html#a72e84dc1bb573ad8ecc9109fbbc1b63b ),即使我拥有数学博士学位,这对我来说也毫无意义。
我已经尝试使用MathNET.Numerics
进行翻译,这种方法是
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;
}
向右看?
coeffRef
仅提供对底层数据数组的访问。 因此,您对covariance_matrix[i, j]
应该是等效的。 请注意,表达式covariance_matrix.coeffRef(k)
仅给出数据数组中的第k
个元素,与存储顺序无关。 是的,原始代码使用coeffRef(i,j)
,IMO 会更有意义。
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 Eigen 使用大量表达式模板来确定何时以及如何评估表达式的一部分。 有些取决于矩阵的存储顺序,有些则不是。 在它不依赖于存储顺序(例如标量 * 矩阵)能够“短路”的情况下,表达式减少了编译器必须经过的步骤数量,以便决定如何评估给定的表达式可以减少编译时间。 如果我们明确声明coeffRef
,那么我们告诉编译器我们正在谈论一个带有存储的具体对象,而不是一个表达式。
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