# filters.normal¶

filters.normal returns the estimated normal and curvature for a collection of points. The algorithm first computes the eigenvalues and eigenvectors of the collection of points, which is comprised of the k-nearest neighbors. The normal is taken as the eigenvector corresponding to the smallest eigenvalue. The curvature is computed as

$curvature = \frac{\lambda_0}{\lambda_0 + \lambda_1 + \lambda_2}$

where $$\lambda_i$$ are the eigenvalues sorted in ascending order.

The filter produces four new dimensions (NormalX, NormalY, NormalZ, and Curvature), which can be analyzed directly, or consumed by downstream stages for more advanced filtering.

The eigenvalue decomposition is performed using Eigen’s SelfAdjointEigenSolver. For more information see https://eigen.tuxfamily.org/dox/classEigen_1_1SelfAdjointEigenSolver.html.

## Example¶

This pipeline demonstrates the calculation of the normal values (along with curvature). The newly created dimensions are written out to BPF for further inspection.

{
"pipeline":[
"input.las",
{
"type":"filters.normal",
"knn":8
},
{
"type":"writers.bpf",
"filename":"output.bpf",
"output_dims":"X,Y,Z,NormalX,NormalY,NormalZ,Curvature"
}
]
}


## Options¶

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