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License information

Code and network weights are released under different licenses, both are dual licenses depending on applications, research or commercial.

COMMERCIAL PURPOSES

Please contact the authors Alexandre Boulch and Renaud Marlet.

RESEARCH AND NON COMMERCIAL PURPOSES

Code license

For research and non commercial purposes, all the code and documentation of github.com/aboulch/normals_HoughCNN is released under the GPLv3 license:

Deep Learning for Robust Normal Estimation in Unstructured Point Clouds Copyright (c) 2016 Alexande Boulch and Renaud Marlet

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA

PLEASE ACKNOWLEDGE THE ORIGINAL AUTHORS AND PUBLICATION: "Deep Learning for Robust Normal Estimation in Unstructured Point Clouds " by Alexandre Boulch and Renaud Marlet, Symposium of Geometry Processing 2016, Computer Graphics Forum

Network weights license

For research and non commercial purposes, all the network weights and documentation available at github.com/aboulch/normals_HoughCNN are released under the Creative Commons BY-NC-SA license, which implies:

  • Licensees may copy, distribute, display and perform the work and make derivative works and remixes based on it only if they give the author or licensor the credits (attribution) in the following manner: acknowledgement of the authors and paper citation as described on the repository page site, or if not available by citing:
"Deep Learning for Robust Normal Estimation in Unstructured Point Clouds " by Alexandre Boulch and Renaud Marlet, Symposium of Geometry Processing 2016, Computer Graphics Forum
  • Licensees may distribute derivative works only under a license identical ("not more restrictive") to the license that governs the original work. (See also copyleft.) Without share-alike, derivative works might be sublicensed with compatible but more restrictive license clauses, e.g. CC BY to CC BY-NC.)
  • Licensees may copy, distribute, display, and perform the work and make derivative works and remixes based on it only for non-commercial purposes. The detailed license is available at creativecommons.org.