Description Usage Arguments Value Examples

fast kmeans clustering for 2D or 3D point clouds - with the primary purpose to get a spatially equally distributed samples

1 | ```
fastKmeans(x, k, iter.max = 10, project = TRUE, threads = 0)
``` |

`x` |
matrix containing coordinates or mesh3d |

`k` |
number of clusters |

`iter.max` |
maximum number of iterations |

`project` |
logical: if x is a triangular mesh, the centers will be projected onto the surface. |

`threads` |
integer number of threads to use |

returns a list containing

`selected` |
coordinates closest to the final centers |

`centers` |
cluster center |

`class` |
vector with cluster association for each coordinate |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
require(Rvcg)
data(humface)
set.seed(42)
clust <- fastKmeans(humface,k=1000,threads=1)
## Not run:
require(rgl)
## plot the cluster centers
spheres3d(clust$centers)
## now look at the vertices closest to the centers
wire3d(humface)
spheres3d(vert2points(humface)[clust$selected,],col=2)
## End(Not run)
``` |

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