Chenguoz / PointSCNet

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

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

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 [e-mail]