NeAF: Learning Neural Angle Fields for Point Normal Estimation (AAAI 2023 oral)

Shujuan Li* · Junsheng Zhou* · Baorui Ma · Yu-Shen Liu · Zhizhong Han

(* Equal Contribution)

Paper | Project Page

Requirements

• Install python dependencies:

conda create -n NeAF python=3.7.11
conda activate NeAF
pip install torch==1.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install tensorboardX scipy scikit-learn

Data preparation

Please download PCPNet dataset at: http://geometry.cs.ucl.ac.uk/projects/2018/pcpnet/

The preprocessed data of SceneNN can be downloaded at: https://drive.google.com/drive/folders/1JkL3PrYSZGylzIhXdL1hMKlxg6Idv88x?usp=drive_link

Test

To evaluate NeAF, you can simply use the following command:

python run.py --mode test --indir your_dataset_path --name NeAF --test_epoch 900 --need_prediction 1 --checkpoints 5 --coarse_normal_num 10 --gpu 0 1
# Please change 'your_dataset_path' to your own path of the dataset

Train

To train NeAF, you can simply use the following command:

python run.py --mode train --indir PCPNet_dataset_path --name NeAF --nepoch 1000 --lr 0.001 --query_vector_path ./query_vector_5k.xyz --gpu 0 1
 # Please change 'PCPNet_dataset_path' to your own path of the PCPNet dataset

Citation

If you find our code or paper useful, please consider citing

@inproceedings{li2023neaf,
  title={Neaf: Learning neural angle fields for point normal estimation},
  author={Li, Shujuan and Zhou, Junsheng and Ma, Baorui and Liu, Yu-Shen and Han, Zhizhong},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={37},
  number={1},
  pages={1396--1404},
  year={2023}
}