CGAL 6.1.2 - Manual: Compilers and Third Party Dependencies

CGAL 6.1.2 - Manual

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Compilers and Third Party Dependencies

# Supported Compilers

In order to build a program using CGAL, you need a C++ compiler supporting C++17 or later. CGAL 6.1.2 is supported (continuously tested) for the following compilers/operating systems:

Operating System | Compiler
--- | ---
Linux | GNU `g++` 12.2.0 or later`https://gcc.gnu.org/`
| `Clang` `https://clang.llvm.org/` compiler version 20.1.6 or later
MS Windows | GNU `g++` 12.2.0 or later`https://gcc.gnu.org/`
| MS Visual `C++` 15.9, 16.10, 17.14 (Visual Studio 2017, 2019, and 2022)`https://visualstudio.microsoft.com/`
macOS | GNU `g++` 12.2.0 or later`https://gcc.gnu.org/`
| Apple `Clang` compiler versions 12.0.5, 14.0.0, and 15.0.0

Older versions of the above listed compilers might work, but no guarantee is provided.

# CMake

Version 3.22 or later

In order to configure and build the CGAL examples, demos, or libraries, you need CMake, a cross-platform "makefile generator".

This manual explains only the features of CMake which are needed in order to use CGAL. Please refer to the CMake documentation for further details.

Attention
  Recent versions of CMake are needed for the most recent versions of MS Visual C++. Please refer to CMake's documentation for further information.

# Essential Third Party Libraries

The focus of CGAL is on geometry, and we rely on other highly specialized libraries and software for non-geometric issues, for instance for numeric solvers or visualization. We first list software that is essential to most of CGAL, and must therefore be found during the configuration of CGAL. The page Summary of CGAL's Configuration Variables lists CMake and environment variables which can be used to specify the location of third-party software during configuration.

## Standard Template Library (STL)

CGAL heavily uses the STL, and in particular adopted many of its design ideas. You can find online documentation for the STL at various web sites, for instance, `https://en.cppreference.com`, or `https://msdn.microsoft.com`.

The STL comes with the compiler, and as such no installation is required.

## Boost

Version 1.74 or later

The Boost libraries are a set of portable C++ source libraries. Most of Boost libraries are header-only, but a few of them need to be compiled or installed as binaries.

CGAL only requires the headers of the Boost libraries, but some demos and examples depend on the binary library `Boost.Program_options`.

In case the Boost libraries are not installed on your system already, you can obtain them from `https://www.boost.org/`. For Visual C++ you can download precompiled libraries from `https://sourceforge.net/projects/boost/files/boost-binaries/`.

As there is no canonical directory for where to find Boost on Windows, we recommend to use the config mode of CMake by defining the environment variable `Boost_DIR` (case sensitive) to the path of the directory in your boost installation containing the file `BoostConfig.cmake`, e.g.`C:/local/boost_1_80_0/lib64-msvc-14.3/cmake/Boost-1.80.0`.

## Multi Precision Number Type Library

GNU Multiple Precision Arithmetic (GMP) and GNU Multiple Precision Floating-Point Reliably (MPFR) Libraries are libraries for multi precision integers and rational numbers, and for multi precision floating point numbers.

These libraries can be obtained from `https://gmplib.org/` and `https://www.mpfr.org/`. Since Visual C++ is not properly supported by the GMP and MPFR projects, we provide precompiled versions of GMP and MPFR, which can be downloaded from the assets of a release. Version supported are GMP Version 5.0.1 or later, MPFR Version 3.0.0 or later.

The Boost library also provides a module for multi precision integers and rational numbers: Boost multiprecision.

The components CGAL, and `CGAL_Qt6` require either GMP and MPFR, or Boost multiprecision for multi precision numbers. `CGAL_Core` requires Boost multiprecision.

CGAL combines floating point arithmetic with exact arithmetic in order to be efficient and reliable. CGAL has a built-in number type for that, but previous alternatives are faster solutions, and we recommend using one of them.

The CMake variable `CGAL_CMAKE_EXACT_NT_BACKEND` can be used to select the library that will be used internally for multi precision number types.

# Optional Third Party Libraries

Optional 3rd party software can be used by CGAL for various reasons: certain optional libraries might be required to build examples and demos shipped with CGAL or to build your own project using CGAL; another reason is to speed up basic tasks where specialized libraries can be faster than the default version shipped with CGAL. The page Summary of CGAL's Configuration Variables lists CMake and environment variables which can be used to specify the location of third-party software during configuration.

## Qt6

Version 6.4 or later

Qt is a cross-platform application and UI framework.

The component `CGAL_Qt6` is essential to run the CGAL demos and basic viewers. It requires Qt6 installed on your system. In case Qt is not yet installed on your system, you can download it from `https://contribute.qt-project.org/`.

The exhaustive list of Qt6 components used in demos is: `Core`, `Gui`, `Help`, `OpenGL`, `OpenGLWidgets`, `Qml`, `Svg`, `Widgets`, `WebSockets`, `Network`, and `qcollectiongenerator` (with `sqlite` driver plugin).

## Eigen

Version 3.3.7 or later (including Eigen3 5.0.0)

Eigen is a `C++` template library for linear algebra. Eigen supports all matrix sizes, various matrix decomposition methods and sparse linear solvers.

In CGAL, Eigen is used in many packages such as Poisson Surface Reconstruction or Estimation of Local Differential Properties of Point-Sampled Surfaces, providing sparse linear solvers and singular value decompositions. A package dependency over Eigen is marked on the Package Overview page. In order to use Eigen in CGAL programs, the executables should be linked with the CMake imported target `CGAL::Eigen3_support` provided in `CGAL_Eigen3_support.cmake`.

The Eigen web site is `https://libeigen.gitlab.io/`.

## OpenGR

OpenGR is a set C++ libraries for 3D Global Registration released under the terms of the APACHE V2 license.

CGAL provides wrappers for the Super4PCS algorithm of OpenGR in the Point Set Processing packages. In order to use OpenGR in CGAL programs, the executables should be linked with the CMake imported target `CGAL::OpenGR_support` provided in `CGAL_OpenGR_support.cmake`.

The OpenGR web site is `https://github.com/STORM-IRIT/OpenGR`.

## PointMatcher

libpointmatcher is a modular library implementing the Iterative Closest Point (ICP) algorithm for aligning point clouds, released under a permissive BSD license.

CGAL provides wrappers for the ICP algorithm of libpointmatcher in the Point Set Processing packages. In order to use libpointmatcher in CGAL programs, the executables should be linked with the CMake imported target `CGAL::pointmatcher_support` provided in `CGAL_pointmatcher_support.cmake`.

The libpointmatcher web site is `https://github.com/ethz-asl/libpointmatcher`.

Attention
  On Windows, libpointmatcher needs to be compiled with the flag BOOST_TIMER_ENABLE_DEPRECATED set as it uses a deprecated module from Boost.
  On Windows, we support version 1.4.4 of libpointmatcher with version 3.4.0 of Eigen. Alternatively, version 1.3.1 of libpointmatcher is supported with version 3.3.7 of Eigen, with some changes to the recipe at `https://github.com/ethz-asl/libpointmatcher/blob/master/doc/Compilation.md`:`NABO_INCLUDE_DIR` becomes `libnabo_INCLUDE_DIRS` and `NABO_LIBRARY` becomes `libnabo_LIBRARIES` in the "Build libpointmatcher" section.

## Multiple Precision Floating-point Interval (MPFI)

Version 1.4 or later

MPFI provides arbitrary precision interval arithmetic with intervals represented using MPFR reliable floating-point numbers. It is based on the libraries GMP and MPFR. In the setting of CGAL, this library is optional: it is used by some models of the Algebraic Kernel.

MPFI can be downloaded from `https://gitlab.inria.fr/mpfi/mpfi`.

## NTL

Version 5.1 or later

NTL provides data structures and algorithms for signed, arbitrary length integers, and for vectors, matrices, and polynomials over the integers and over finite fields. The optional library NTL is used by CGAL to speed up operations of the Polynomial package, such as GCDs. It is recommended to install NTL with support from GMP.

NTL can be downloaded from `https://libntl.org`.

## ESBTL

The ESBTL (Easy Structural Biology Template Library) is a library that allows the handling of PDB data.

In CGAL, the ESBTL is used in an example of the 3D Skin Surface Meshing package.

It can be downloaded from `https://esbtl.sourceforge.net/`.

## Intel TBB

TBB (Threading Building Blocks) is a library developed by Intel Corporation for writing software programs that take advantage of multi-core processors.

In CGAL, TBB is used by the packages that offer parallel code. In order to use TBB in CGAL programs, the executables should be linked with the CMake imported target `CGAL::TBB_support` provided in `CGAL_TBB_support.cmake`.

The TBB web site is `https://github.com/oneapi-src/oneTBB`.

## LASlib

LASlib is a `C++` library for handling LIDAR data sets stored in the LAS format (or the compressed LAZ format).

In CGAL, LASlib is used to provide input and output functions in the Point Set Processing package. In order to use LASlib in CGAL programs, the executables should be linked with the CMake imported target `CGAL::LASLIB_support` provided in `CGAL_LASLIB_support.cmake`.

LASlib information can be obtained from https://lastools.github.io/ and https://rapidlasso.de/product-overview/. LASlib is usually distributed along with LAStools. Current versions of LASlib provide CMake support. BUILD_SHARED_LIBS needs to be set to true on windows to create a dynamic linked library.

## OpenCV

OpenCV (Open Computer Vision) is a library designed for computer vision, computer graphics and machine learning.

In CGAL, OpenCV is used by the Classification package. In order to use OpenCV in CGAL programs, the executables should be linked with the CMake imported target `CGAL::OpenCV_support` provided in `CGAL_OpenCV_support.cmake`.

The OpenCV web site is `https://opencv.org/`.

## SuiteSparse

Version 7.10.1 or later

SuiteSparse is a set of sparse-matrix-related packages written or co-authored by Tim Davis, available at https://github.com/DrTimothyAldenDavis/SuiteSparse.

SuiteSparse is used by the Orbifold Tutte Embedding and ARAP parametrization, i.e., in the surface parametrization package.

Note
  SuiteSparse on Windows can easily be installed by
  vcpkg install suitesparse
  and it will be found automatically. When installed manually it is helpful to add the installation folder to the environment variable CMAKE_PREFIX_PATH for CMake to find the SuiteSparse libraries.

## METIS

Version 5.1 or later

METIS is a library developed by the Karypis Lab and designed to partition graphs and produce fill-reducing matrix orderings.

CGAL offers wrappers around some of the methods of the METIS library to allow the partitioning of graphs that are models of the concepts of the Boost Graph Library, and, by extension, of surface meshes (see Section Graph Partitioning of the package CGAL and the Boost Graph Library).

More information is available on the METIS library at `https://github.com/KarypisLab/METIS`.

## zlib

zlib is a data compression library, and is essential for the component libCGAL_ImageIO.

In CGAL, this library is used in the examples of the 3D Surface Mesh Generation package.

If it is not already on your system, for instance, on Windows, you can download it from `https://www.zlib.net/`.

## Ceres Solver

Ceres is an open source C++ library for modeling and solving large, complicated optimization problems.

In CGAL, Ceres is used by the Polygon Mesh Processing package for mesh smoothing, which requires solving complex non-linear least squares problems.

Visit the official website of the library at `ceres-solver.org` for more information.

Attention
  Ceres requires to be compiled with the exact same version of Eigen that is used for CGAL.
  Ceres indicates that `glog` is a recommended dependency. `glog` has `libunwind` as a recommended dependency. On some platforms, linking with `libunwind` was responsible for an increase of the runtime of the final application. If you experience such an issue, we recommend to compile Ceres without `glog` support.
  In the master branch of Ceres `glog` was dropped as a dependency and `abseil` was added instead. It is not compatible with Visual Studio 2017 or earlier versions.

## GLPK

GLPK (GNU Linear Programming Kit) is a library for solving linear programming (LP), mixed integer programming (MIP), and other related problems.

In CGAL, GLPK provides an optional linear integer program solver in the Polygonal Surface Reconstruction package. In order to use GLPK in CGAL programs, the executables should be linked with the CMake imported target `CGAL::GLPK_support` provided in `CGAL_GLPK_support.cmake`.

The GLPK web site is `https://www.gnu.org/software/glpk/`.

## SCIP

SCIP (Solving Constraint Integer Programs) is currently one of the fastest open source solvers for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP).

In CGAL, SCIP provides an optional linear integer program solver in the Polygonal Surface Reconstruction package. In order to use SCIP in CGAL programs, the executables should be linked with the CMake imported target `CGAL::SCIP_support` provided in `CGAL_SCIP_support.cmake`.

The SCIP web site is `https://www.scipopt.org/`.

## OSQP

OSQP (Operator Splitting Quadratic Program) is currently one of the fastest open source solvers for convex Quadratic Programs (QP).

In CGAL, OSQP provides an optional solver for the quadratic programming used in the Shape Regularization package. In order to use OSQP in CGAL programs, the executables should be linked with the CMake imported target `CGAL::OSQP_support` provided in `CGAL_OSQP_support.cmake`. CGAL is compatible with OSQP version 1.0.0.

The OSQP web site is `https://osqp.org`.