/opengv

OpenGV is a collection of computer vision methods for solving geometric vision problems.
  1. C++ 97.1%
  2. MATLAB 2.0%
  3. Other 0.9%
Switch branches/tags
Nothing to show
Find file
Clone or download

Clone with HTTPS

Use Git or checkout with SVN using the web URL.

Download ZIP

Launching GitHub Desktop...

If nothing happens, download GitHub Desktop and try again.

Launching GitHub Desktop...

If nothing happens, download GitHub Desktop and try again.

Launching Xcode...

If nothing happens, download Xcode and try again.

Launching Visual Studio...

If nothing happens, download the GitHub extension for Visual Studio and try again.

@laurentkneip
laurentkneip Merge pull request #81 from alicevision/dev_msvc 
Build fixes for MSVC
Latest commit 306a54e Oct 25, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
doc/addons Update python wrappers documentation Oct 18, 2018
include/opengv Merge pull request #68 from paulinus/feature-random-seed Oct 18, 2018
matlab Merge pull request #56 from jbriales/bash_MEX Oct 18, 2018
modules [ci] enable python build Apr 3, 2017
python Detect where to install the python module Oct 24, 2018
src Fix MSVC build with Matrix operator* Oct 24, 2018
test Merge pull request #37 from johannesmik/master Oct 18, 2018
.gitignore Use pybind11 for python bindings Oct 18, 2018
.gitmodules Add pybind11 as a submodule Oct 18, 2018
.travis.yml [ci] enable python build Apr 3, 2017
CMakeLists.txt [cmake] Use different build flags for MSVC and GCC Oct 24, 2018
Doxyfile Initial commit Sep 5, 2013
License.txt Initial commit Sep 5, 2013
Makefile.ros Initial commit Sep 5, 2013
README.txt Updated the README file May 11, 2016
manifest.xml Initial commit Sep 5, 2013

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