filters.approximatecoplanar

filters.approximatecoplanar implements a portion of the algorithm presented in [Limberger2015]. Prior to clustering points, the authors first apply an approximate coplanarity test, where points that meet the following criteria are labeled as approximately coplanar.

\[\lambda_2 > (s_{\alpha}\lambda_1) \&\& (s_{\beta}\lambda_2) > \lambda_3\]

\(\lambda_1\), \(\lambda_2\), \(\lambda_3\) are the eigenvalues of a neighborhood of points (defined by knn nearest neighbors) in ascending order. The threshold values \(s_{\alpha}\) and \(s_{\beta}\) 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).

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]