Quasi-SW

Official PyTorch implementation for paper: Quasi-Monte Carlo for 3D Sliced Wasserstein

Details of the model architecture and experimental results can be found in our papers.

@article{nguyen2024quasi,
  title={Quasi-Monte Carlo for 3D Sliced Wasserstein},
  author={Khai Nguyen and Nicola Bariletto and Nhat Ho},
  booktitle={International Conference on Learning Representations},
  year={2024},
  pdf={https://arxiv.org/pdf/2309.11713.pdf}
}

Please CITE our paper whenever this repository is used to help produce published results or incorporated into other software.

This implementation is made by Khai Nguyen.

Requirements

To install the required python packages, run

pip install -r requirements.txt

What is included?

• Point-Cloud Gradient flow
• Color Transfer
• Deep Point-Cloud Reconstruction

Point-Cloud Gradient flow

cd GradientFlow
python main_point.py

Color Transfer

cd ColorTransfer
python main.py --source [source image] --target [target image] --num_iter 1000 --cluster

Deep Point-cloud Reconstruction

Please read the README file in the PointcloudAE folder.

Acknowledgment

The structure of this repo is largely based on PointSWD. The structure of folder render is largely based on Mitsuba2PointCloudRenderer.