Installing pre-requisites:
sudo apt install build-essential python3-dev libopenblas-dev
pip3 install -r requirements.txt
pip3 install torch ninja
Installing MinkowskiEngine with CUDA support:
pip3 install -U MinkowskiEngine==0.5.4 --install-option="--blas=openblas" -v --no-deps
Inside the docker/ directory there is a Dockerfile to build an image to run SegContrast. You can build the image from scratch or download the image from docker hub by:
docker pull nuneslu/segcontrast:minkunet
Then start the container with:
docker run --gpus all -it --rm -v /PATH/TO/SEGCONTRAST:/home/segcontrast segcontrast /bin/zsh
Download SemanticKITTI inside the directory ./Datasets/SemanticKITTI/datasets. The directory structure should be:
./
└── Datasets/
└── SemanticKITTI
└── dataset
└── sequences
├── 00/
│ ├── velodyne/
| | ├── 000000.bin
| | ├── 000001.bin
| | └── ...
│ └── labels/
| ├── 000000.label
| ├── 000001.label
| └── ...
├── 08/ # for validation
├── 11/ # 11-21 for testing
└── 21/
└── ...
- SegContrast pretraining weights
- Fine-tuned semantic segmentation
Run the following to start the pre-training:
python3 contrastive_train.py --use-cuda --use-intensity --segment-contrast --checkpoint segcontrast
The default parameters, e.g., learning rate, batch size and epochs are already the same as the paper.
After pre-training you can run the downstream fine-tuning with:
python3 downstream_train.py --use-cuda --use-intensity --checkpoint \
segment_contrast --contrastive --load-checkpoint --batch-size 2 \
--sparse-model MinkUNet --epochs 15
We provide in tools the contrastive_train.sh and downstream_train.sh scripts to reproduce the results pre-training and fine-tuning with the different label percentages shown on the paper:
For pre-training:
./tools/contrastive_train.sh
Then for fine-tuning:
./tools/downstream_train.sh
Finally, to compute the IoU metrics use:
./tools/eval_train.sh
If you use this repo, please cite as :
@article{nunes2022ral,
author = {L. Nunes and R. Marcuzzi and X. Chen and J. Behley and C. Stachniss},
title = {{SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination}},
journal = ral,
year = 2022,
doi = {10.1109/LRA.2022.3142440},
issn = {2377-3766},
volume = {7},
number = {2},
pages = {2116-2123},
url = {http://www.ipb.uni-bonn.de/pdfs/nunes2022ral-icra.pdf},
}
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