Description Usage Arguments Value Examples

Implements the Statistical Outliers Removal (SOR) filter available in
CloudCompare.
Computes the distance of each point to its `k`

nearest neighbours and considers
a point as noise if it is further than the average distance (for the entire point cloud)
plus `sigma`

times the standard deviation away from other points.

1 | ```
filter_noise(data, k, sigma, store_noise, message)
``` |

`data` |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud. |

`k` |
numeric. The number of nearest neighbours to use. Default = 5. |

`sigma` |
numeric. The multiplier of standard deviation to consider a point as noise. Default = 1.5. |

`store_noise` |
logical. Should the noisy points be retained ? Default = FALSE. |

`message` |
logical. If FALSE, messages are disabled. Default = TRUE. |

If `store_noise = TRUE`

the input data is returned with an additional field ("Noise")
where points that are classified as noise points are labaled with 2 and the points not classified as noise are labeled as 1.
If `store_noise = FALSE`

only the points that were not classified as noise are returned.

1 2 3 4 5 6 7 8 9 | ```
#- import tls data
tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR"))
#- run noise filter
clean=VoxR::filter_noise(tls,store_noise = TRUE)
#- plot the result (noise in red)
rgl::open3d()
rgl::plot3d(clean,col=clean$Noise,add=TRUE)
``` |

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