Description Usage Arguments Details Eigen Decomposition of Point Neighborhoods
This function is meant to be used inside treeMap
. It applies a KNN filter to select points with specific neighborhood features. For more details on geometry features, check out fastPointMetrics
.
1 2 3 4 5 6 7 8  map.eigen.knn(
max_curvature = 0.1,
max_verticality = 10,
max_mean_dist = 0.1,
max_d = 0.5,
min_h = 1.5,
max_h = 3
)

max_curvature 

max_verticality 

max_mean_dist 

max_d 

min_h, max_h 

Point metrics are calculated for every point. Points are then removed depending on their point
metrics parameters and clustered to represent individual tree regions.
Clusters are defined as a function of the expected maximum diameter. Any fields added to the
point cloud are described in fastPointMetrics
.
Point filtering/classification methods that rely on eigen decomposition rely on shape indices calculated for point neighborhoods (knn or voxel). To derive these shape indices, eigen decomposition is performed on the XYZ columns of a point cloud patch. Metrics related to object curvature are calculated upon ratios of the resulting eigen values, and metrics related to object orientation are caltulated from approximate normals obtained from the eigen vectors.
For instance, a point neighborhood that belongs to a perfect flat surface will have all of its variance explained by the first two eigen values, and none explained by the third eigen value. The 'normal' of such surface, i.e. the vector oriented in the direction orthogonal to the surface, is therefore represented by the third eigenvector.
Methods for both tree mapping and stem segmentation use those metrics, so in order
to speed up the workflow one might apply fastPointMetrics
to the point
cloud before other methods. The advantages of this approach are that users
can parameterize the point neighborhoods themselves when calculating their metrics.
Those calculations won't be performed again internally in the tree mapping or stem
denoising methods, reducing the overall processing time.
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