Enhancing Semantic Segmentation of LiDAR Point Clouds through Global Maps
This is a code repository of my Thesis work.
You can find all .py
files and notebooks here.
I hade following workdir structure:
.
└── workdir/
├── dataset/
│ └── sequences/
│ └── ...
├── gits/
│ └── ...
├── utils/
│ ├── one.py
│ └── two.py
├── .py files
└── .ipynb files
└── Pipfile
Then I created pipenv env and got provided Pipfile .
pip install --user pipenv
pipenv --python 3.10
pipenv install
Files in utils
folder - are modified files from Semantic Kitti API repo.
Then you can created parts of map with following command:
pipenv run python map_creators.py 08 0 1000
pipenv run python map_creators.py 08 500 1500
...
And with depth-weighted samplaing, one can crate GMP:
pipenv run python velodyne2_creator.py 08 0 1000 2
pipenv run python velodyne2_creator.py 08 500 1500 2
...
And with uniform: And with depth-weighted samplaing, one can crate GMP:
pipenv run python velodyne2_creator.py 08 0 1000 3
pipenv run python velodyne2_creator.py 08 500 1500 3
...
Notebooks were primarly used as playgrounds.
And with visualize.py
script one can view open3d windows with point clouds.