PointSCNet: Point Cloud Structure and Correlation Learning based on Space Filling Curve guided Sampling

Description

This repository contains the code for our paper: PointSCNet: Point Cloud Structure and Correlation Learning based on Space Filling Curve guided Sampling

<div align="center"> <img src="images/example.jpg" width="70%" height="70%"><br><br> </div>

Environment setup

Current Code is tested on ubuntu18.04 with cuda11, python3.6.9, torch 1.10.0 and torchvision 0.11.3. We use a pytorch version of pointnet++ in our pipeline.

Classification (ModelNet10/40)

Data Preparation

Download alignment ModelNet here and save in data/modelnet40_normal_resampled/.

Data Preparation

Run

python train.py --model SCNet --log_dir SCNet_log --use_normals --process_data

• --model: model name
• --log_dir: path to log dir
• --use_normals: use normals
• --process_data: save data offline

Test

python test.py --log_dir SCNet_log --use_normals

Performance

| Model | Accuracy | |--|--| | PointNet (Official) | 89.2| | PointNet2 (Official) | 91.9 | | PointSCNet | 93.7|

Citation

Please cite our paper if you find it useful in your research:

@article{chen2022pointscnet,
  title={PointSCNet: Point Cloud Structure and Correlation Learning Based on Space-Filling Curve-Guided Sampling},
  author={Chen, Xingye and Wu, Yiqi and Xu, Wenjie and Li, Jin and Dong, Huaiyi and Chen, Yilin},
  journal={Symmetry},
  volume={14},
  number={1},
  pages={8},
  year={2022},
  publisher={Multidisciplinary Digital Publishing Institute}
}

Contact

If you have any questions, please contact cxy@cug.edu.cn