Fast and robust algorithm to extract edges in unorganized point clouds.

Source code and the dataset of this paper:
Fast and Robust Edge Extraction in Unorganized Point Clouds (Dena Bazazian, Josep R Casas, Javier Ruiz-Hidalgo) - DICTA2015

• Difference_Eigenvalues.py is a source code for extracting the edges of a point cloud based on Python 3 and pyntcloud library.
  
• Installation is based on conda install pyntcloud -c conda-forge or pip install pyntcloud.
  

• Difference_Eigenvalues.cpp includes the C++ source code for extracting edges in unorganized point clouds.
  

• F1Score-Eigenvalues.cpp is for computing the accuracy of edge extraction.
  

• We have created some artificial point clouds in order to have a labeled dataset, since we have both the point clouds and ground truths. Hence, in the ArtificialPointClouds and GroundTruth directories, you can find the artificial point clouds and their correspond ground truth.
  

• In addition, in the artificial_point_cloud.cpp you can access to the source code that we have generated those artificial point clouds.
  

Please cite this work in your publications if it helps your research:

@InProceedings{Bazazian15,
author = {Bazazian, Dena and Casas, Josep R and Ruiz-Hidalgo, Javier},
title = {Fast and Robust Edge Extraction in Unorganized Point Clouds},
booktitle = {Proceeding of International Confere on Digital Image Computing: Techniques and Applications (DICTA)},
publisher = {IEEE},
pages = {1-8},
year = {2015}
}