Point Cloud Clustering: Constant size, Compact and Non-Overlapping
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c2no.m

README.md

Point Cloud Clustering: Constant size, Compact and Non-Overlapping (C2NO)

This is a MATLAB function that receives a Point Cloud, and outputs a set of clusters with the same number of points, specified by the user. If the total number of points is not multiple of the target number of points per cluster, the user can specify if only 1 cluster should be incomplete with the remaining points, or if all points should be equally distributed. These incomplete clusters can be padded by repeating points. The resulting clusters are compact and do not overlap each other. A detailed description of the algorithm will be available soon.

Requirements

  • MATLAB: Implemented and tested in R2018b on Linux (Ubuntu 18.04). For other operating systems some changes might be required (e.g., on Windows, use backslash instead of forward slash for paths). In previous versions of MATLAB, some functions might not be available (e.g., isfolder() was introduced in R2017b, previous versions use isdir() instead).
  • Computer Vision System Toolbox

Instructions for use:

Usage

  • c2no(infile, outpcname, outfolder, ppc)
  • c2no(infile, outpcname, outfolder, ppc, cnn)
  • c2no(infile, outpcname, outfolder, ppc, cnn, nb)
  • c2no(infile, outpcname, outfolder, ppc, cnn, nb, mic)
  • c2no(infile, outpcname, outfolder, ppc, cnn, nb, mic, padding)
  • [progress, clust_disp, clust_bb, sum_dists, iter_adj_rest] = c2no(infile, outpcname, outfolder, ppc, cnn, nb, mic, padding, debug)

Input arguments:

  • infile - Input filename of the point cloud to cluster (PLY or PCD)
  • outpcname - Output clusters basename
  • outfolder - Output clusters directory path
  • ppc - Target number of points per cluster

Optional input arguments:

  • cnn - Number of nearest neighboring clusters eligible for transfers and refinement (Default: 10)
  • nb - Number of block divisions on each direction for Cluster Centroid Initialization step, which divides the point cloud into nbnbnb blocks (Default: 8)
  • mic - Selects the approach for dealing with target cluster sizes when the total number of points in the point cloud is not multiple of ppc; TRUE uses the Minimally Incomplete Clusters case, where points are equally distributed through all clusters; FALSE uses the Single Incomplete Cluster case, where all but one cluster will have ppc points, and one cluster will have the remaining points (Default: TRUE)
  • padding - Selects if padding is applied to incomplete clusters, when the total number of points in the point cloud is not multiple of ppc (Default: TRUE)
  • debug - This option allows tracking the progress of the algorithm in the different stages, computing cluster dispersion metrics at each iteration, and saving the clusters at the end of each phase (Default: FALSE)

Output arguments:

  • progress - 2D Matrix with the number of points in each cluster for each iteration of the Iterative Clustering Phase
  • clust_disp - 3D Matrix with the cluster dispersion (measured as the variance of cluster coordinates) for each of the 3 dimensions, for each cluster, at each iteration of the Iterative Clustering Phase (only if debug=TRUE)
  • clust_bb - 3D Matrix with the cluster dispersion (measured as the range of cluster coordinates) for each of the 3 dimensions, for each cluster, at each iteration of the Iterative Clustering Phase (only if debug=TRUE)
  • sum_dists - 1D Matrix with the sum of distances between points and respective centroids, averaged for all clusters, taken at each iteration of the Clustering Refinement Phase (only if debug=TRUE) iter_adj_rest - Indicates at which iteration of the Iterative Clustering Phase the adjacency restriction was lifted, or the value 0 if it was not (only if debug=TRUE)