# filters.approximatecoplanar

`filters.approximatecoplanar` filter estimates the planarity of a neighborhood of points by first computing eigenvalues for the points and then tagging those points for which the following is true:

\[\lambda_1 > (thresh_1 * \lambda_0) \&\& (\lambda_1 * thresh_2) > \lambda_2\]

where \(\lambda_0\), \(\lambda_1\), \(\lambda_2\) are the eigenvalues in ascending order. The threshold values \(thresh_1\) and \(thresh_2\) are user-defined and default to 25 and 6 respectively.

The filter returns a point cloud with a new dimension `Coplanar` that indicates those points that are part of a neighborhood that is approximately coplanar (1) or not (0).

Eigenvalue estimation is performed using Eigen’s `SelfAdjointEigenSolver`. For more information see https://eigen.tuxfamily.org/dox/classEigen_1_1SelfAdjointEigenSolver.html.

Default Embedded Stage

This stage is enabled by default

## Example

The sample pipeline presented below estimates the planarity of a point based on its eight nearest neighbors using the `filters.approximatecoplanar` filter. A `filters.range` stage then filters out any points that were not deemed to be coplanar before writing the result in compressed LAZ.

    {
      "pipeline":[
        "input.las",
        {
          "type":"filters.approximatecoplanar",
          "knn":8,
          "thresh1":25,
          "thresh2":6
        },
        {
          "type":"filters.range",
          "limits":"Coplanar[1:1]"
        },
        "output.laz"
      ]
    }

## Options

knn
  The number of k-nearest neighbors. [Default: 8]
thresh1
  The threshold to be applied to the smallest eigenvalue. [Default: 25]
thresh2
  The threshold to be applied to the second smallest eigenvalue. [Default: 6]