filters.statisticaloutlier¶
The Statistical Outlier filter passes data through the Point Cloud Library (PCL) StatisticalOutlierRemoval algorithm.
filters.statisticaloutlier
uses point neighborhood statistics to filter
outlier data. The algorithm iterates through the entire input twice. The first
iteration is used to calculate the average of the distances from each point \(i\)
to its \(k\) nearest neighbors or \(AD_{i}\). The second iteration is used to
identify outliers based on the distribution of \(AD_{i}\) values. Points with an
average neighbor distance greater than the mean plus 2 standard deviations are
classified as outliers. The value of \(k\) can be set using \(\tt mean\_k\). By
default, the distance threshold is set to \(\overline{AD}_{i} + 2 \hat{\sigma}\),
but a value other than 2 can be chosen using \(\tt multiplier\).
See [Rusu2008] for more information.
[Rusu2008] | Rusu, Radu Bogdan, et al. “Towards 3D point cloud based object maps for household environments.” Robotics and Autonomous Systems 56.11 (2008): 927-941. |
Example¶
{
"pipeline":[
"input.las",
{
"type":"filters.statisticaloutlier",
"mean_k":"12",
"multiplier":"2.2"
},
{
"type":"writers.las",
"filename":"output.las"
}
]
}
Options¶
- mean_k
- Mean number of neighbors. [Default: 8]
- multiplier
- Standard deviation threshold. [Default: 2.0]
- classify
- Apply classification labels? [Default: true]
- extract
- Extract ground returns? [Default: false]