• ResSCNN
• • Environment
  • Dataset
  • Training
  • Performance comparison
  • Citation
  • contributors

ResSCNN

This is the repository of ResSCNN. The original code is implemented by Pytorch, while we provide Mindspore and Tensorflow.
Key words: point cloud quality assessment, no-reference
In this case, the original implementation of PyTorch is as follows:
https://github.com/lyp22/ResSCNN

Environment

For ResSCNN-mindspore:
ubuntu 16.04
python 3.7
mindspore 2.0, installation reference: https://www.mindspore.cn/install

For ResSCNN-tensorflow:
ubuntu 16.04
python 3.7
tensorflow-gpu 2.x

Dataset

Link for LS-PCQA

Training

For mindspore,

cd ./ResSCNN-mindspore
python main.py

For tensorflow,

cd ./ResSCNN-tf
python main.py

Performance comparison

Benchmark test on mindspore, tensorflow and pytorch below
Paper:

Citation

@article{Liu2022ResSCNN,
title={Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric},
author={Yipeng Liu and Qi Yang and Yiling Xu and Le Yang},
journal={ACM Transactions on Multimedia Computing Communications and Applications},
year={2022}
}

contributors

name: Zhang Yongchi && Haohui Li
email: zhangych02@pcl.ac.cn