ohhhyeahhh / PointAttN

Code for the paper "PointAttN: You Only Need Attention for Point Cloud Completion"

PointAttN

News：Our paper has been accepted by AAAI-2024.

1. Environment setup

Install related libraries

This code has been tested on Ubuntu 20.04, python 3.8.12, torch 1.9.0 and cuda 11.2. Please install related libraries before running this code:

pip install -r requirements.txt

Compile Pytorch 3rd-party modules

please compile Pytorch 3rd-party modules ChamferDistancePytorch and mm3d_pn2. A simple way is using the following command:

cd $PointAttN_Home/utils/ChamferDistancePytorch/chamfer3D
python setup.py install

cd $PointAttN_Home/utils/mm3d_pn2
python setup.py build_ext --inplace

2. Train

Prepare training datasets

Download the datasets:

• PCN(raw data)
• PCN(processed data)
• Completion3D

Train a model

To train the PointAttN model, modify the dataset path in cfgs/PointAttN.yaml , run:

python train.py -c PointAttN.yaml

3. Test

Pretrained models

The pretrained models on Completion3D and PCN benchmark are available as follows:

dataset	performance	model link
Completion3D	CD = 6.63	[BaiDuYun] (code：nf0m)[GoogleDrive]
PCN	CD = 6.86	[BaiDuYun] (code：kmju)[GoogleDrive]

Test for paper result

To test PointAttN on PCN benchmark, download the pretrained model and put it into PointAttN_cd_debug_pcn directory, run:

python test_pcn.py -c PointAttN.yaml

To test PointAttN on Completion3D benchmark, download the pretrained model and put it into PointAttN_cd_debug_c3d directory, run:

python test_c3d.py -c PointAttN.yaml

4. Acknowledgement

1. We include the following PyTorch 3rd-party libraries:
   [1] ChamferDistancePytorch
   [2] mm3d_pn2

2. Some of the code of this project is borrowed from VRC-Net

5. Cite this work

If you use PointAttN in your work, please cite our paper:

@article{Wang_Cui_Guo_Li_Liu_Shen_2024,
   title={PointAttN: You Only Need Attention for Point Cloud Completion},
   volume={38}, 
   url={https://ojs.aaai.org/index.php/AAAI/article/view/28356}, DOI={10.1609/aaai.v38i6.28356}, 
   number={6}, 
   journal={Proceedings of the AAAI Conference on Artificial Intelligence},
   author={Wang, Jun and Cui, Ying and Guo, Dongyan and Li, Junxia and Liu, Qingshan and Shen, Chunhua},
   year={2024},
   month={Mar.},
   pages={5472-5480}
}