Point Transformer Diffusion (PTD) integrates the standard DDPM, adapted for 3D data, with Point Transformer, a local self-attention network specifically designed for 3D point clouds.

• Thesis report: 3D Shape Generation through Point Transformer Diffusion.
• It achieved outstanding results in the unconditional shape generation task on the ShapeNet dataset.

Make sure the following environments are installed.

python
torch
tqdm
matplotlib
trimesh
scipy
open3d
einops
pytorch_warmup

You can utilize the yaml file in the requirements folder by

conda env create -f ptd_env.yml 
conda activate ptd_env

, which will take significant time. Then, install pointops by

cd lib/pointops/
python setup.py install
cp build/lib.linux-x86_64-cpython-39/pointops_cuda.cpython-39-x86_64-linux-gnu.so .

Also, install emd for evaluation by

cd metrics/PyTorchEMD/
python setup.py install
cp build/lib.linux-x86_64-cpython-39/emd_cuda.cpython-39-x86_64-linux-gnu.so .

The code was tested on Ubuntu 22.04 LTS and GeForce RTX 3090.

For generation, we use ShapeNet point cloud, which can be downloaded here.

Pretrained models can be downloaded here.

$ python train_generation.py --category car|chair|airplane

Please refer to the python file for optimal training parameters.

$ python train_generation.py --category car|chair|airplane --model MODEL_PATH

Evaluation results of the trained models of PTD.

Visualization of several generated results:

DDPM for 3D point clouds: Point-Voxel Diffusion.
Point Transformer:

• The version of my implementation,
• Pointcept.
• The CUDA library of pointops.