The code has been tested on Ubuntu 20.04 with CUDA 12.0.
virtualenv -p /usr/bin/python venv_recon
source venv_recon/bin/activate
cd ARNet_shape_reconstruction
pip install -r requirements.txt
Please find the chamferdist/chamfer.py file under venv_recon/lib, and replace the chamfer.py in this python library to our chamfer.py file under the folder.
Download the dataset from the Google Drive and unzip under the folder.
exp_name
- used to specify data splitckpt_path
- used to specify saved model for testing
Run the following command:
CUDA_VISIBLE_DEVICES=6 python main.py data_split_<1-3>
Run the following command for evaluation of the trained models:
CUDA_VISIBLE_DEVICES=6 python test.py data_split_<1-3>
The visual reconstruction results are saved under image
folder.The numerical reconstruction results will be saved in .npy file under reconstruction_results
folder.