[Paper] [Code]

The official code repository for our CVPR 2021 paper "Diffusion Probabilistic Models for 3D Point Cloud Generation".

[Option 1] Install via conda environment YAML file (CUDA 10.1).

# Create the environment
conda env create -f env.yml
# Activate the environment
conda activate dpm-pc-gen

[Option 2] Or you may setup the environment manually (If you are using GPUs that only work with CUDA 11 or greater).

Our model only depends on the following commonly used packages, all of which can be installed via conda.

We have removed the EMD module due to GPU compatability issues. The legacy code can be found on the emd-cd branch.

If you have to compute the EMD score or compare our model with others, we strongly advise you to use your own code to compute the metrics. The generation and decoding results will be saved to the results folder after each test run.

Datasets and pretrained models are available at: https://drive.google.com/drive/folders/1Su0hCuGFo1AGrNb_VMNnlF7qeQwKjfhZ

# Train an auto-encoder
python train_ae.py 

# Train a generator
python train_gen.py

You may specify the value of arguments. Please find the available arguments in the script.

Note that --categories can take all (use all the categories in the dataset), airplane, chair (use a single category), or airplane,chair (use multiple categories, separated by commas).

# Test an auto-encoder
python test_ae.py --ckpt ./pretrained/AE_all.pt --categories all

# Test a generator
python test_gen.py --ckpt ./pretrained/GEN_airplane.pt --categories airplane

@inproceedings{luo2021diffusion,
  author = {Luo, Shitong and Hu, Wei},
  title = {Diffusion Probabilistic Models for 3D Point Cloud Generation},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2021}
}