Description Usage Arguments Value List of available point metrics Examples
View source: R/point_metrics_methods.R
Print the list of available metrics for fastPointMetrics.
1 | fastPointMetrics.available(enable = ENABLED_POINT_METRICS$names)
|
enable |
optional |
character vector of all metrics.
* EVi = i-th 3D eigen value
* EV2Di = i-th 2D eigen value
N: number of nearest neighbors
MinDist: minimum distance among neighbors
MaxDist: maximum distance among neighbors
MeanDist: mean distance
SdDist: standard deviation of within neighborhood distances
Linearity: linear saliency, \mjeqn(EV_1 + EV_2) / EV_1(EV1 + EV2) / EV1
Planarity: planar saliency, \mjeqn(EV_2 + EV_3) / EV_1(EV2 + EV3) / EV1
Scattering: \mjeqnEV_3 / EV_1EV3 / EV1
Omnivariance: \mjeqn(EV_2 + EV_3) / EV_1(EV2 + EV3) / EV1
Anisotropy: \mjeqn(EV_1 - EV_3) / EV_1(EV1 - EV3) / EV1
Eigentropy: \mjeqn- \sum_i=1^n=3 EV_i * ln(EV_i)-sum(EV * ln(EV))
EigenSum: sum of eigenvalues, \mjeqn\sum_i=1^n=3 EV_isum(EV)
Curvature: surface variation, \mjeqnEV_3 / EigenSumEV3 / EigenSum
KnnRadius: 3D neighborhood radius
KnnDensity: 3D point density (N / sphere volume)
Verticality: absolute vertical deviation, in degrees
ZRange: point neighborhood height difference
ZSd: standard deviation of point neighborhood heights
KnnRadius2d: 2D neighborhood radius
KnnDensity2d: 2D point density (N / circle area)
EigenSum2d: sum of 2D eigenvalues, \mjeqn\sum_i=1^n=2 EV2D_isum(EV2D)
EigenRatio2d: \mjeqnEV2D_2 / EV2D_1EV2D2 / EV2D1
EigenValuei: 3D eigenvalues
EigenVectorij: 3D eigenvector coefficients, i-th load of j-th eigenvector
1 2 | m = fastPointMetrics.available()
length(m)
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