Deep Closest Point
Prerequisites
PyTorch>=1.0: https://pytorch.org
scipy>=1.2
numpy
h5py
tqdm
TensorboardX: https://github.com/lanpa/tensorboardX
Training
DCP-v1
python main.py --exp_name=dcp_v1 --model=dcp --emb_nn=dgcnn --pointer=identity --head=svd
DCP-v2
python main.py --exp_name=dcp_v2 --model=dcp --emb_nn=dgcnn --pointer=transformer --head=svd
Testing
DCP-v1
python main.py --exp_name=dcp_v1 --model=dcp --emb_nn=dgcnn --pointer=identity --head=svd --eval
or
python main.py --exp_name=dcp_v1 --model=dcp --emb_nn=dgcnn --pointer=identity --head=svd --eval --model_path=xx/yy
DCP-v2
python main.py --exp_name=dcp_v2 --model=dcp --emb_nn=dgcnn --pointer=transformer --head=svd --eval
or
python main.py --exp_name=dcp_v2 --model=dcp --emb_nn=dgcnn --pointer=transformer --head=svd --eval --model_path=xx/yy
where xx/yy is the pretrained model
Citation
Please cite this paper if you want to use it in your work,
@InProceedings{Wang_2019_ICCV,
title={Deep Closest Point: Learning Representations for Point Cloud Registration},
author={Wang, Yue and Solomon, Justin M.},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year={2019}
}
License
MIT License