The approximate coplanar filter 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


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




The number of k-nearest neighbors. [Default: 8]


The threshold to be applied to the smallest eigenvalue. [Default: 25]


The threshold to be applied to the second smallest eigenvalue. [Default: 6]


An expression that limits points passed to a filter. Points that don’t pass the expression skip the stage but are available to subsequent stages in a pipeline. [Default: no filtering]


A strategy for merging points skipped by a ‘where’ option when running in standard mode. If true, the skipped points are added to the first point view returned by the skipped filter. If false, skipped points are placed in their own point view. If auto, skipped points are merged into the returned point view provided that only one point view is returned and it has the same point count as it did when the filter was run. [Default: auto]