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https://github.com/manifoldfr/alphaexpansion
An implementation of alpha-expansion and graph cut
https://github.com/manifoldfr/alphaexpansion
Last synced: 26 days ago
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An implementation of alpha-expansion and graph cut
- Host: GitHub
- URL: https://github.com/manifoldfr/alphaexpansion
- Owner: ManifoldFR
- License: gpl-3.0
- Created: 2020-02-11T14:45:05.000Z (over 4 years ago)
- Default Branch: lucas-alpha
- Last Pushed: 2020-03-16T09:32:06.000Z (over 4 years ago)
- Last Synced: 2023-10-20T21:35:42.057Z (about 1 year ago)
- Language: C++
- Size: 4.24 MB
- Stars: 4
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AlphaExpansion
An implementation of alpha-expansion and graph cut in C++. This is
We use the [Boost Graph library](https://www.boost.org/doc/libs/1_72_0/libs/graph/doc/index.html).
## Building
```bash
mkdir build && cd build/
cmake ../
make
```
or if you are using the command line on Linux:
```bash
./build.sh
```
## Use
Run the executable from the command line with
```bash
./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:
```bash
./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:
```bash
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