Tianxinhuang / 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 adopted 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. The qualitative results should be The quantitative results on the Known categories of ShapeNet in PCN would be

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}
}