AlphaExpansion

An implementation of alpha-expansion and graph cut in C++. This is

We use the Boost Graph library.

Building

mkdir build && cd build/
cmake ../
make

or if you are using the command line on Linux:

./build.sh

Use

Run the executable from the command line with

./Main path/to/folder

where the folder should contain the node potentials and edges as

nodes.txt
edges.txt

If you have a reference labels file named label_ref.txt, you can have the program check the coherence by using the --check flag:

./Main path/to/folder --check

Python bindings

Once this root C++ library is built or installed (e.g. using make install), you can install Python bindings using setuptools:

cd python
python setup.py build_ext  # with option --inplace for a local build

This requires Pybind11.

Application to semantic 3D point cloud segmentation

The data files (unary potentials and list of edges) included in this repository are generated in the context of a 3D point cloud classification challenge: its code can be found at https://github.com/JulesSanchez/npm3d-challenge. The soft labels are generated using the XGBoost algorithm with handcrafted input features (local covariance and shape features).

More details on the challenge can be found here: https://npm3d.fr/benchmark-for-master-course-on-3d-point-clouds

References

• https://hal.inria.fr/file/index/docid/617524/filename/sample2e.pdf
• http://akira.ruc.dk/~keld/teaching/algoritmedesign_f03/Artikler/08/Goldberg88.pdf
• Simon JD Prince, Computer vision: models, learning, and inference. URL: http://www0.cs.ucl.ac.uk/external/s.prince/book/Algorithms.pdf