/PUGAN_zsliu

This is the official PyTorch implementation of our paper "Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions" (CVPR 2023).

Primary LanguagePythonApache License 2.0Apache-2.0

Grad-PU and PUGAN

Installation

  • Install the following packages
pip install open3d einops scikit-learn tqdm h5py ninja
git submodule add https://github.com/ThibaultGROUEIX/ChamferDistancePytorch
  • Install the built-in libraries
cd models/pointops
python setup.py install

dataset

We use the PU-Net dataset for training, you can refer to https://github.com/yulequan/PU-Net to download the .h5 dataset file, which can be directly used in this project.

modify some setting in the option/train_option.py

change opt['project_dir'] to where this project is located, and change opt['dataset_dir'] to where you store the dataset.
also change params['train_split'] and params['test_split'] to where you save the train/test split txt files.

training

cd train
python train.py --exp_name=the_project_name --gpu=gpu_number --use_gan --batch_size=12