Skip to content
No description, website, or topics provided.
Branch: master
Clone or download
joaobcampos Update README.txt
Describe where the code for the paper is.
Latest commit 4dcbc07 Jun 10, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
doc/addons Enhance installation documentation Sep 26, 2016
include/opengv The program is giving all the statistical information required. Error… Feb 13, 2018
matlab Added UPnP to the matlab interface. Redid the online tutorial, and ad… Mar 23, 2016
modules Wrap p3p_kneip in python Sep 6, 2015
python - Fix some compilation warning Jun 21, 2016
src The program is giving all the statistical information required. Error… Feb 13, 2018
test The program is giving all the statistical information required. Error… Feb 13, 2018
.gitignore csv files added Feb 5, 2018
.travis.yml Add travis config Nov 11, 2015
CMakeLists.txt The program is giving all the statistical information required. Error… Feb 13, 2018
Doxyfile Initial commit Sep 5, 2013
License.txt Initial commit Sep 5, 2013
Makefile.ros Initial commit Sep 5, 2013
README.txt
manifest.xml Initial commit Sep 5, 2013
read_data.py Now the graphic are set Jan 23, 2018

README.txt

library: OpenGV
pages:   http://laurentkneip.github.io/opengv
brief:   OpenGV is a collection of computer vision methods for solving
         geometric vision problems. It contains absolute-pose, relative-pose,
         triangulation, and point-cloud alignment methods for the calibrated
         case. All problems can be solved with central or non-central cameras,
         and embedded into a random sample consensus or nonlinear optimization
         context. Matlab and Python interfaces are implemented as well. The link
         to the above pages also shows links to precompiled Matlab mex-libraries.
         Please consult the documentation for more information.
author:  Laurent Kneip, The Australian National University
contact: kneip.laurent@gmail.com


In folders: 2018AMMPoseSolver/include/opengv/optimization_tools/  and 2018AMMPoseSolver/src/optimization_tools/ you can find the headers/source code of the algorithm described in the paper.
In folder https://github.com/pmiraldo/2018AMMPoseSolver/blob/master/test/ you can find our algorithms testes under the names results_*
You can’t perform that action at this time.