sudo apt-get install libsparsehash-dev
conda create -n pointcept python=3.8 -y
conda activate pointcept
conda install ninja -y
# Choose version you want here: https://pytorch.org/get-started/previous-versions/
# We use CUDA 11.8 and PyTorch 2.1.0 for our development of PTv3
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install h5py pyyaml -c anaconda -y
conda install sharedarray tensorboard tensorboardx yapf addict einops scipy plyfile termcolor timm -c conda-forge -y
conda install pytorch-cluster pytorch-scatter pytorch-sparse -c pyg -y
pip install torch-geometric
cd libs/pointgroup_ops
python setup.py install
cd ../..
# PTv1 & PTv2 or precise eval
cd libs/pointops
# usual
python setup.py install
# spconv (SparseUNet)
# refer https://github.com/traveller59/spconv
pip install spconv-cu118 # choose version match your local cuda version
# Open3D (visualization, optional)
pip install open3d
conda install -c conda-forge gcc
# MinkowskiEngine
sudo apt install libopenblas-dev
conda install -c conda-forge blas openblas
git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd lib/MinkowskiEngine
python setup.py install --blas_include_dirs=/opt/anaconda3/envs/pointcept2/include --blas=openblasWe conducted tests on a total of 20 datasets obtained from different types of sensors.
datasets link
Additionally, we express our gratitude to several scholars who shared their data with us. We processed and annotated these data for testing purposes. The original links to these data include:
python tools/train.py --config-file configs/corn3d_group_semantic/full/semseg-spvunet-v1m2-base.pypython tools/test.py --config-file configs/corn3d_group_semantic/full/semseg-spvunet-v1m2-base.py --options save_path="{weight_path}" weight="{weight_path}/model_best.pth"We provide our best model weights here: model_pth
If you find this project useful in your research, please consider cite: