This software delivers tools to build surface reconstructions from point cloud data and a simple viewer to display the results. Additionally, the found surfaces will be classified into predefined categories. The main aim of this project is to deliver fast and accurate surface extraction algorithms for robotic applications such as tele operation in unknown environments and localization.
Download and Compilation from Source
Step 0: Get the source code from our Github repository:
Linux (Ubuntu 18.04)
Step 1: Install all required package dependencies:
sudo apt-get install build-essential \ cmake cmake-curses-gui libflann-dev \ libgsl-dev libeigen3-dev libopenmpi-dev \ openmpi-bin opencl-c-headers ocl-icd-opencl-dev \ libvtk7-dev libvtk7-qt-dev libboost-all-dev \ freeglut3-dev libhdf5-dev qtbase5-dev \ qt5-default libqt5opengl5-dev liblz4-dev \ libopencv-dev libyaml-cpp-dev
A C++17 compiler is required, e.g., g++7, gcc7 need bo installed. If CUDA is installed you also need g++6, see "Optional for NVIDIA graphics cards users"
Step 2: Configure and build from sources:
mkdir build cd build cmake .. && make
Optional for NVIDIA graphics cards users:
If you want to compile with CUDA support install the latest version of the CUDA toolkit, which you can find on NVIDIAs CUDA download site:
To enable CUDA support, you need to compile the software with a compatible GCC version. We have testet compilation with CUDA 9.1 and GCC 6. To use this compiler for compilation of CUDA generated code, set the
CUDA_HOST_COMPILER option to
g++-6 is forced to g++-6. Please ensure that this version is installed on your system.
Step 3: Installation
After successful compilation, you will find the generated example tools in the ./bin/ directory. Optionally, you can install the library and header files to your system:
sudo make install
Install the required libraries using Homebrew:
brew install boost boost-mpi cmake eigen flann gcc glew gsl hdf5 opencv lz4 qt vtk mkdir build cd build cmake .. && make
Your can experiment with the software using the provided dataset. For a simple reconstruction call in yout build directory:
in the root directory of the project. This will create a file called “triangle_mesh.ply” which can be displayed using the viewer application:
For more information, build the Doxygen documentation by calling
in the build directory.