mfkiwl / rfnet

The codes for RFNet: Recurrent Forward Network for Dense Point Cloud Completion

RFNet

The codes for RFNet: Recurrent Forward Network for Dense Point Cloud Completion

Environment

• TensorFlow 1.13.1
• Cuda 10.0
• Python 3.6.9
• lmdb 0.98
• tensorpack 0.10.1
• numpy 1.14.5

Dataset

The dataset can be found in PCN.

Usage

1. Compile

cd ./tf_ops
bash compile.sh

2. Train

Python3 vv_recon.py

Note that the paths of training data(trainpath) and validation data(valpath) should be edited according to your setting.

3. Test

Python3 recon_test.py

The paths of test data(data_dir) and lists(list_path) should be edited before testing.

Citation

If you find our work useful for your research, please cite:

@inproceedings{huang2021rfnet,
  title={RFNet: Recurrent Forward Network for Dense Point Cloud Completion},
  author={Huang, Tianxin and Zou, Hao and Cui, Jinhao and Yang, Xuemeng and Wang, Mengmeng and Zhao, Xiangrui and Zhang, Jiangning and Yuan, Yi and Xu, Yifan and Liu, Yong},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={12508--12517},
  year={2021}
}