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To evaluate NeAF, you can simply use the following command:
pythonrun.py--modetest--indiryour_dataset_path--nameNeAF--test_epoch900--need_prediction1--checkpoints5--coarse_normal_num10--gpu01# Please change 'your_dataset_path' to your own path of the dataset
Train
To train NeAF, you can simply use the following command:
pythonrun.py--modetrain--indirPCPNet_dataset_path--nameNeAF--nepoch1000--lr0.001--query_vector_path ./query_vector_5k.xyz--gpu01# 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}
}
About
Code Release for AAAI 2023, "NeAF: Learning Neural Angle Fields for Point Normal Estimation"