A Pytorch implementation of the bps representation using chamfer distance on GPU. This implementation is very fast and was used for the GrabNet model.
Basis Point Set (BPS) is a simple and efficient method for encoding 3D point clouds into fixed-length representations. For the original implementation please visit this implementation by Sergey Prokudin.
- Python >= 3.7
- PyTorch >= 1.1.0
- Numpy >= 1.16.2
- chamfer_distance
- Pytorch3d
If PyTorch is not installed run the following line:
pip install torch==1.5.1+cpu torchvision==0.6.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
To install the chamfer_distance package run:
pip install git+https://github.com/otaheri/chamfer_distance
Finally install the package using the command below:
pip install git+https://github.com/otaheri/bps_torch
Below is an example of how to use the bps_torch code.
import torch
import time
from bps_torch.bps import bps_torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# initiate the bps module
bps = bps_torch(bps_type='random_uniform',
n_bps_points=1024,
radius=1.,
n_dims=3,
custom_basis=None)
pointcloud = torch.rand([1000000,3]).to(device)
s = time.time()
bps_enc = bps.encode(pointcloud,
feature_type=['dists','deltas'],
x_features=None,
custom_basis=None)
print(time.time() - s)
deltas = bps_enc['deltas']
bps_dec = bps.decode(deltas)If you use this code in your research, please consider citing:
@inproceedings{prokudin2019efficient,
title={Efficient Learning on Point Clouds With Basis Point Sets},
author={Prokudin, Sergey and Lassner, Christoph and Romero, Javier},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={4332--4341},
year={2019}
}
This library is licensed under the MIT-0 License of the original implementation. See the LICENSE file.
The code of this repository was implemented by Omid Taheri.
For questions, please contact omid.taheri@tue.mpg.de.