# filters.approximatecoplanar¶

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.

\(\lambda_1\), \(\lambda_2\), \(\lambda_3\) 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).

## Example¶

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.

```
[
"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]