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compiling-cloudviewer.md

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Build from source

System requirements

  • Ubuntu 18.04+: GCC 5+, Clang 7+

  • macOS 10.14+: XCode 8.0+

  • Windows 10 (64-bit): Visual Studio 2019+

  • CMake: 3.15+ for Ubuntu and macOS, 3.18+ for Windows

    • Ubuntu (18.04):

      • Install with apt-get: see official APT repository <https://apt.kitware.com/>_
      • Install with snap: sudo snap install cmake --classic
      • Install with pip (run inside a Python virtualenv): pip install cmake

    • Ubuntu (20.04+): Use the default OS repository: sudo apt-get install cmake

    • macOS: Install with Homebrew: brew install cmake

    • Windows: Download from: CMake download page <https://cmake.org/download/>_

  • CUDA 10.1 (optional): CloudViewer supports GPU acceleration of an increasing number of operations through CUDA on Linux. Please see the official documentation <https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html>_ to install the CUDA toolkit from Nvidia.

Cloning CloudViewer

Make sure to use the --recursive flag when cloning CloudViewer.

git clone --recursive https://github.com/Asher-1/ACloudViewer.git

# You can also update the submodule manually
git submodule update --init --recursive

Note

custom modification：

3rdparty/rply
libs/Reconstruction/lib/PoissonRecon

Ubuntu/macOS

Refer to the compiling-cloudviewer-linux.md file for compilation Ubuntu/macOS information.

Windows

  1. Setup Python binding environments

    Most steps are the steps for Ubuntu: :ref:compilation_unix_python. Instead of which, check the Python path with where python

  2. Config

     mkdir build
 cd build
 
 cmake -DQT_QMAKE_EXECUTABLE:PATH=/opt/Qt5.14.2/5.14.2/gcc_64/bin/qmake \
 -DCMAKE_PREFIX_PATH:PATH=/opt/Qt5.14.2/5.14.2/gcc_64/lib/cmake  \
   ..
 
 :: Specify the generator based on your Visual Studio version
 :: If CMAKE_INSTALL_PREFIX is a system folder, admin access is needed for installation
 cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_INSTALL_PREFIX="<cloudViewer_install_directory>" ..

  3. Build

     cmake --build . --config Release --target ALL_BUILD

    Alternatively, you can open the CloudViewer.sln project with Visual Studio and build the same target.

  4. Install

    To install CloudViewer C++ library, build the INSTALL target in terminal or in Visual Studio.

     cmake --build . --config Release --target INSTALL

To link a C++ project against the CloudViewer C++ library, please refer to :ref:create_cplusplus_project.

To install CloudViewer Python library, build the corresponding python installation targets in terminal or Visual Studio.

:: Activate the virtualenv first
:: Install pip package in the current python environment
cmake --build . --config Release --target install-pip-package

:: Create Python package in build/lib
cmake --build . --config Release --target python-package

:: Create pip package in build/lib
:: This creates a .whl file that you can install manually.
cmake --build . --config Release --target pip-package

:: Create conda package in build/lib
:: This creates a .tar.bz2 file that you can install manually.
cmake --build . --config Release --target conda-package

Finally, verify the Python installation with:

python -c "import cloudViewer; print(cloudViewer)"

Compilation options

OpenMP

We automatically detect if the C++ compiler supports OpenMP and compile CloudViewer with it if the compilation option WITH_OPENMP is ON. OpenMP can greatly accelerate computation on a multi-core CPU.

The default LLVM compiler on OS X does not support OpenMP. A workaround is to install a C++ compiler with OpenMP support, such as gcc, then use it to compile CloudViewer. For example, starting from a clean build directory, run

brew install gcc --without-multilib
cmake -DCMAKE_C_COMPILER=gcc-6 -DCMAKE_CXX_COMPILER=g++-6 ..
make -j

This workaround has some compatibility issues with the source code of GLFW included in 3rdparty. Make sure CloudViewer is linked against GLFW installed on the OS.

ML Module

arning: Due to incompatibilities in the cxx11_abi on Linux between PyTorch and TensorFlow, official Python wheels on Linux only support PyTorch, not TensorFlow. The ML module consists of primitives like operators and layers as well as high level code for models and pipelines. To build the operators and layers, set BUILD_PYTORCH_OPS=ON and/or BUILD_TENSORFLOW_OPS=ON. Don't forget to also enable BUILD_CUDA_MODULE=ON for GPU support. To include the models and pipelines from CloudViewer-ML in the python package, set BUNDLE_CLOUDVIEWER_ML=ON and CLOUDVIEWER_ML_ROOT to the CloudViewer-ML repository. You can directly download CloudViewer-ML from GitHub during the build with CLOUDVIEWER_ML_ROOT=https://github.com/intel-isl/CloudViewer-ML.git.

The following example shows the command for building the ops with GPU support for all supported ML frameworks and bundling the high level CloudViewer-ML code.

# In the build directory
cmake -DBUILD_CUDA_MODULE=ON \
      -DBUILD_PYTORCH_OPS=ON \
      -DBUILD_TENSORFLOW_OPS=OFF \
      -DBUNDLE_CLOUDVIEWER_ML=ON \
      -DCLOUDVIEWER_ML_ROOT=https://github.com/intel-isl/CloudViewer-ML.git \
      ..
# Install the python wheel with pip
make -j install-pip-package

Importing Python libraries compiled with different CXX ABI may cause segfaults
in regex. https://stackoverflow.com/q/51382355/1255535. By default, PyTorch
and TensorFlow Python releases use the older CXX ABI; while when they are
compiled from source, newer ABI is enabled by default.

When releasing CloudViewer as a Python package, we set
``-DGLIBCXX_USE_CXX11_ABI=OFF`` and compile all dependencies from source,
in order to ensure compatibility with PyTorch and TensorFlow Python releases.

If you build PyTorch or TensorFlow from source or if you run into ABI
compatibility issues with them, please:

1. Check PyTorch and TensorFlow ABI with

   .. code-block:: bash

       python -c "import torch; print(torch._C._GLIBCXX_USE_CXX11_ABI)"
       python -c "import tensorflow; print(tensorflow.__cxx11_abi_flag__)"

2. Configure CloudViewer to compile all dependencies from source
   with the corresponding ABI version obtained from step 1.

After installation of the Python package, you can check CloudViewer ABI version
with:

.. code-block:: bash

    python -c "import cloudViewer; print(cloudViewer.pybind._GLIBCXX_USE_CXX11_ABI)"

To build CloudViewer with CUDA support, configure with:

.. code-block:: bash

    cmake -DBUILD_CUDA_MODULE=ON -DCMAKE_INSTALL_PREFIX=<cloudViewer_install_directory> ..

Please note that CUDA support is work in progress and experimental. For building
CloudViewer with CUDA support, ensure that CUDA is properly installed by running following commands:

.. code-block:: bash

    nvidia-smi      # Prints CUDA-enabled GPU information
    nvcc -V         # Prints compiler version

If you see an output similar to ``command not found``, you can install CUDA toolkit
by following the `official
documentation. <https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html>`_

Unit test

To build and run C++ unit tests:

cmake -DBUILD_UNIT_TESTS=ON ..
make -j
./bin/tests

To run Python unit tests:

# Activate virtualenv first
pip install pytest
make install-pip-package
pytest ../python/test

Upload whl fies

pip install twine
twine upload dist/*

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