TreeLS (version 2.0.2)

fastPointMetrics.available: Print available point metrics

Description

Print the list of available metrics for fastPointMetrics.

Usage

fastPointMetrics.available(enable = ENABLED_POINT_METRICS$names)

Arguments

enable

optional integer or character vector containing indices or names of the metrics you want to enable globally. Enabled metrics are calculated every time you run fastPointMetrics by default. Only metrics used internally in other TreeLS methods are enabled out-of-the-box.

Value

character vector of all metrics.

List of available point 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, (EV_1 + EV_2) / EV_1(EV1 + EV2) / EV1

• Planarity: planar saliency, (EV_2 + EV_3) / EV_1(EV2 + EV3) / EV1

• Scattering: EV_3 / EV_1EV3 / EV1

• Omnivariance: (EV_2 + EV_3) / EV_1(EV2 + EV3) / EV1

• Anisotropy: (EV_1 - EV_3) / EV_1(EV1 - EV3) / EV1

• Eigentropy: - _i=1^n=3 EV_i * ln(EV_i)-sum(EV * ln(EV))

• EigenSum: sum of eigenvalues, _i=1^n=3 EV_isum(EV)

• Curvature: surface variation, EV_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, _i=1^n=2 EV2D_isum(EV2D)

• EigenRatio2d: EV2D_2 / EV2D_1EV2D2 / EV2D1

• EigenValuei: 3D eigenvalues

• EigenVectorij: 3D eigenvector coefficients, i-th load of j-th eigenvector

Examples

# NOT RUN {
m = fastPointMetrics.available()
length(m)
# }