Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models

Paper | Project Page | Video | Original Repo

This repository contains the official implementation for our publication: "Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models."

The implementation has been restructured using ⚡ PyTorch Lightning ⚡ for improved modularity and scalability.

News:

• 12/8/2024: Arxiv submission of the SPVD preprint.
• 12/9/2024: Release of SPVD Lightning, utilizing PyTorch Lightning ⚡

Installation

1. Clone the Repository (Including Submodules)

To clone this repository along with its submodules, use the --recursive flag:

git clone --recursive https://github.com/JohnRomanelis/SPVD_Lightning.git

2. Set Up an Anaconda Environment

We recommend using Anaconda to manage your Python environment. We also provide an environment.yml to set it up.

By running the following command you will create a conda env called spvd containing most of the libraries required to run our code.

conda env create -f environment.yml

3. Install Torchsparse

TorchSparse is now included as a submodule in the repository under the dependencies/torchsparse folder, so there is no need to clone it separately. However, you need to install system dependencies and the library itself.

1. Install Google Sparse Hash:

sudo apt-get install libsparsehash-dev

2. Install torchsparse from the submodule:

cd dependencies/torchsparse
pip install -e .

4. Install Chamfer Distance and Earth Mover Distance

• Chamfer:

  1. Navigate to the SPVD/metrics/chamfer_dist directory:

  cd SPVD/metrics/chamfer_dist

  2. Run:

  python setup.py install --user

• EMD:

  1. Navigate to the SPVD/metrics/PyTorchEMD directory:

  cd SPVD/metrics/PyTorchEMD

  2. Run:

  python setup.py install

  3. Run:

  cp ./build/lib.linux-x86_64-cpython-310/emd_cuda.cpython-310-x86_64-linux-gnu.so .

  If an error is raised in this last command, list all directories inside build and replace the name of the directory with the one in your system named lib.linux-x86_64-cpython-*.

Data

For generation, we use the same version of ShapeNet as PointFlow. Please refer to their instructions for downloading the dataset.

Citation

If you find this work useful in your research, please consider citing:

@misc{romanelis2024efficientscalablepointcloud,
      title={Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models}, 
      author={Ioannis Romanelis and Vlassios Fotis and Athanasios Kalogeras and Christos Alexakos and Konstantinos Moustakas and Adrian Munteanu},
      year={2024},
      eprint={2408.06145},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.06145}, 
}