#### Occlusion-aware Non-Rigid Point Cloud Registration via Unsupervised Neural Deformation Correntropy ####


#### Prerequisites ####

This code is based on PyTorch implementation, and tested on the following environment dependencies:

Linux (tested on Ubuntu 22.04.1)
Python 3.9.19
torch=='1.12.1+cu113'




#### Setup ####
## We recommend using Miniconda to set up the environment ##

# Create conda environment

   conda create -n oar python=3.9
   conda activate oar

# Install packages

   pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
   conda install -c fvcore -c iopath -c conda-forge fvcore iopath
   conda install pytorch3d


# If you want the torch version match the pytorch3d version, please use "conda list" to check the corresponding Version, and then re-setup the torch, such as

   pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113


# Finally, setup other libraries: 

   pip install -r requirements.txt


#### Test ####

   cd src

   python test_OAR.py

# The deformed point clouds are save in the subdirectory "save_deformed" of the directory "data"






