swh:1:snp:a981ea1718c19c4d9cde9d807965fd6d38bebcd2
Tip revision: a34738fe34a051760b4042dc9d740231e511fec1 authored by Nico Schertler on 31 October 2020, 07:04:57 UTC
Updated access token
Updated access token
Tip revision: a34738f
README.md
# Generalized Motorcycle Graph
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This repository contains the source code for the research paper:
> **Generalized Motorcycle Graphs for Imperfect Quad-Dominant Meshes** <br/>
> Nico Schertler, Daniele Panozzo, Stefan Gumhold, Marco Tarini <br/>
> ACM TOG 37, 4, August 2018 <br/>
## Prerequisites
If you want support for invisible seam parametrization, you need to have [Gurobi](http://www.gurobi.com) installed. The CMake system will try to find the according libraries.
On Linux, the following packages are necessary (available via `apt-get`):
* xorg-dev
* libglu1-mesa-dev
* libboost-dev (only the header files are needed)
## Compilation
To compile the project, simply check out the git repository, initialize all submodules, use CMake to generate project files for your favorite build environment, and run the build.
On Unix-based systems, this may look as follows:
git clone https://github.com/NSchertler/GeneralizedMotorcycleGraph.git
cd GeneralizedMotorcycleGraph
git submodule update --init --recursive
mkdir build
cd build
cmake .. -DNANOGUI_USE_GLAD=ON
make RegularMeshTexturing
Some parts of the code make use of OpenMP.
If your build environment does not support OpenMP (e.g. Clang on MacOS), the program will still compile but performance may be inferior due to missing parallelization.
## Binaries
The following binaries are compiled from the latest commit with default options (no Gurobi support):
* [Windows Binaries](https://github.com/NSchertler/GeneralizedMotorcycleGraph/blob/deploy-windows/gmcg-windows.zip) (Visual Studio 2017)
* [Linux Binaries](https://github.com/NSchertler/GeneralizedMotorcycleGraph/blob/deploy-linux/gmcg-linux.zip) (GCC 6)
* [macOS Binaries](https://github.com/NSchertler/GeneralizedMotorcycleGraph/blob/deploy-osx/gmcg-macos.zip) (Clang, without OpenMP support)
## Data sets
A selection of data sets that we presented in our paper can be found here under `data.zip` in the repository root.
## Documentation
Source code documentation can be found in the source files. For usage instructions, see the [wiki](https://github.com/NSchertler/GeneralizedMotorcycleGraph/wiki).