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I want to fit a plane to a 3D point cloud. I use a RANSAC approach, where I sample several points from the point cloud, calculate the plane, and store the plane with the smallest error. The error is the distance between the points and the plane. I want to do this in C++, using Eigen.

So far, I sample points from the point cloud and center the data. Now, I need to fit the plane to the samples points. I know I need to solve Mx = 0, but how do I do this? So far I have M (my samples), I want to know x (the plane) and this fit needs to be as close to 0 as possible.

I have no idea where to continue from here. All I have are my sampled points and I need more data.

double-beep
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Cassie
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    duplicate of http://stackoverflow.com/questions/1400213/3d-least-squares-plane –  Aug 28 '15 at 15:21
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    No this is not a duplicate. Cassie is asking about fitting a plane in presence of outliers. The other question considers purely the least squares fit. – Tolga Birdal Jan 09 '17 at 20:53

2 Answers2

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From you question I assume that you are familiar with the Ransac algorithm, so I will spare you of lengthy talks.

In a first step, you sample three random points. You can use the Random class for that but picking them not truly random usually gives better results. To those points, you can simply fit a plane using Hyperplane::Through.

In the second step, you repetitively cross out some points with large Hyperplane::absDistance and perform a least-squares fit on the remaining ones. It may look like this:

Vector3f mu = mean(points);
Matrix3f covar = covariance(points, mu);
Vector3 normal = smallest_eigenvector(covar);
JacobiSVD<Matrix3f> svd(covariance, ComputeFullU);
Vector3f normal = svd.matrixU().col(2);
Hyperplane<float, 3> result(normal, mu);

Unfortunately, the functions mean and covariance are not built-in, but they are rather straightforward to code.

emu
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Recall that the equation for a plane passing through origin is Ax + By + Cz = 0, where (x, y, z) can be any point on the plane and (A, B, C) is the normal vector perpendicular to this plane.

The equation for a general plane (that may or may not pass through origin) is Ax + By + Cz + D = 0, where the additional coefficient D represents how far the plane is away from the origin, along the direction of the normal vector of the plane. [Note that in this equation (A, B, C) forms a unit normal vector.]

Now, we can apply a trick here and fit the plane using only provided point coordinates. Divide both sides by D and rearrange this term to the right-hand side. This leads to A/D x + B/D y + C/D z = -1. [Note that in this equation (A/D, B/D, C/D) forms a normal vector with length 1/D.]

We can set up a system of linear equations accordingly, and then solve it by an Eigen solver as follows.

// Example for 5 points
Eigen::Matrix<double, 5, 3> matA; // row: 5 points; column: xyz coordinates
Eigen::Matrix<double, 5, 1> matB = -1 * Eigen::Matrix<double, 5, 1>::Ones();

// Find the plane normal
Eigen::Vector3d normal = matA.colPivHouseholderQr().solve(matB);

// Check if the fitting is healthy
double D = 1 / normal.norm();
normal.normalize(); // normal is a unit vector from now on
bool planeValid = true;
for (int i = 0; i < 5; ++i) { // compare Ax + By + Cz + D with 0.2 (ideally Ax + By + Cz + D = 0)
  if ( fabs( normal(0)*matA(i, 0) + normal(1)*matA(i, 1) + normal(2)*matA(i, 2) + D) > 0.2) {
    planeValid = false; // 0.2 is an experimental threshold; can be tuned
    break;
  }
}

This method is equivalent to the typical SVD-based method, but much faster. It is suitable for use when points are known to be roughly in a plane shape. However, the SVD-based method is more numerically stable (when the plane is far far away from origin) and robust to outliers.

Hanzhe Teng
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