# filters.miniball¶

The Miniball Criterion was introduced in [Weyrich2004] and is based on the assumption that points that are distant to the cluster built by their k-neighborhood are likely to be outliers. First, the smallest enclosing ball is computed for the k-neighborhood, giving a center point and radius [Fischer2010]. The miniball criterion is then computed by comparing the distance (from the current point to the miniball center) to the radius of the miniball.

The author suggests that the Miniball Criterion is more robust than the Plane Fit Criterion around high-frequency details, but demonstrates poor outlier detection for points close to a smooth surface.

The filter creates a single new dimension, Miniball, that records the Miniball criterion for 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.

Default Embedded Stage

This stage is enabled by default

## Example¶

The sample pipeline below computes the Miniball criterion with a neighborhood of 8 neighbors. We do not apply a fixed threshold to single out outliers based on the Miniball criterion as the range of values can vary from one dataset to another. In general, higher values indicate the likelihood of a point being an outlier.

[
"input.las",
{
"type":"filters.miniball",
"knn":8
}
"output.laz"
]


## Options¶

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