PUGAN-pytorch

Pytorch unofficial implementation of PUGAN (a Point Cloud Upsampling Adversarial Network, ICCV, 2019)

From https://github.com/UncleMEDM/PUGAN-pytorch

Support RTX 3070 and CUDA 11.

Install some packages

RTX 3070; Python 3.6; Pytorch 1.7.0; CUDA 11.1.

simply by

conda create -n pugan python=3.6

conda activate pugan

conda install pytorch==1.7.0 torchvision torchaudio cudatoolkit=11.0 -c pytorch

pip install -r requirements.txt

Install Pointnet2 module

cd pointnet2
python setup.py install

Install KNN_cuda

pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl

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