sample_per_voxel: Point Cloud Decimation Algorithm

In r-lidar/lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

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)

r-lidar/lidR documentation built on Feb. 7, 2025, 8:57 p.m.