# filters.reciprocity¶

The **Nearest-Neighbor Reciprocity Criterion** was introduced in [Weyrich2004]
and is based on a simple assumption, that valid points may be in the
k-neighborhood of an outlier, but the outlier will most likely not be part of
the valid point’s k-neighborhood.

The author suggests that the Nearest-Neighbor Reciprocity Criterion is more robust than both the Plane Fit and Miniball Criterion, being equally sensitive around smooth and detailed regions. The criterion does however produce invalid reslts near manifold borders.

The filter creates a single new dimension, `Reciprocity`

, that records the
percentage of points(in the range 0 to 100) that are considered uni-directional
neighbors of the current point.

Note

To inspect the newly created, non-standard dimensions, be sure to write to an output format that can support arbitrary dimensions, such as BPF.

## Example¶

The sample pipeline below computes reciprocity with a neighborhood of 8
neighbors, followed by a range filter to crop out points whose `Reciprocity`

percentage is less than 98% before writing the output.

```
[
"input.las",
{
"type":"filters.reciprocity",
"knn":8
},
{
"type":"filters.range",
"limits":"Reciprocity[:98.0]"
},
"output.laz"
]
```

## Options¶

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