lidR (version 4.2.1)

sample_per_voxel: Point Cloud Decimation Algorithm

Description

These functions are made to be used in decimate_points. They implements algorithm that creates a 3D grid with a given resolution and filters the point cloud by selecting points of interest within each voxel. `random_per_voxel()` sample random points. `barycenter_per_voxel()` samples the point that is the closest to the barycenter of the points within a given voxel. `[lowest|highest]_attribute_per_voxel()` sample respectively the point that have the highest/lowest attribute (e.g. Intensity) per voxel.

Usage

random_per_voxel(res = 1, n = 1)
barycenter_per_voxel(res = 1)
lowest_attribute_per_voxel(res, attribute = "Z")
highest_attribute_per_voxel(res, attribute = "Z")

Arguments

res

numeric. The resolution of the voxel grid used to filter the point cloud

n

integer. The number of points to select

attribute

string name of an attribute (such as 'intensity')

See Also

Other point cloud decimation algorithms: sample_homogenize, sample_maxima, sample_random

Examples

LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las <- readLAS(LASfile, select = "xyz")
thinned <- decimate_points(las, random_per_voxel(8, 1))
#plot(thinned)