filters.covariancefeatures¶
This filter implements various local feature descriptors introduced that are based on the covariance matrix of a
point’s neighborhood. The user can pick a set of feature descriptors by
setting the feature_set
option. Currently, the only supported feature is the dimensionality
set of feature descriptors introduced below.
Example¶
[
"input.las",
{
"type":"filters.covariancefeatures",
"knn":8,
"threads": 2,
"feature_set": "Dimensionality"
},
{
"type":"writers.bpf",
"filename":"output.las",
"output_dims":"X,Y,Z,Linearity,Planarity,Scattering,Verticality"
}
]
Options¶
- knn
- The number of k nearest neighbors used for calculating the covariance matrix. [Default: 10]
- threads
- The number of threads used for computing the feature descriptors. [Default: 1]
- feature_set
- The features to be computed. Currently only supports
Dimensionality
. [Default: “Dimensionality”] - stride
- When finding k nearest neighbors, stride determines the sampling rate. A stride of 1 retains each neighbor in order. A stride of two selects every other neighbor and so on. [Default: 1]
Dimensionality feature set¶
The features introduced in [Demantke2011] describe the shape of the neighborhood, indicating whether the local geometry is more linear (1D), planar (2D) or volumetric (3D) while the one introduced in [Guinard2017] adds the idea of a structure being vertical.
The dimensionality filter introduces the following four descriptors that are computed from the covariance matrix of the knn
neighbors:
- linearity - higher for long thin strips
- planarity - higher for planar surfaces
- scattering - higher for complex 3d neighbourhoods
- verticality - higher for vertical structures, highest for thin vertical strips
It introduces four new dimensions that hold each one of these values: Linearity
Planarity
Scattering
and Verticality
.