This is the code for "No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models" and it is the point cloud version. This paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technology. The arxiv version can be found here [http://arxiv.org/abs/2107.02041] and the TCVST version can be found here [https://ieeexplore.ieee.org/document/9810024].
You should get the h5py, pyntcloud, skimage package by
pip install h5py
pip install pyntcloud
pip install scikit-image
We test the code with Python 3.7 (and higher) on the Windows platform and the code may run on linux as well.
The demo.py includes a demo for the feature extraction of colored point cloud and we provide with a colored point cloud sample hhi_5.ply.
The code is tested on the SJTU-PCQA database which can be downloaded at [https://smt.sjtu.edu.cn/].
The code is also tested on the WPC database which can be downloaded at [https://github.com/qdushl/Waterloo-Point-Cloud-Database].
We update the experiment files for SJTU-PCQA and WPC databases, which includes the MOSs and extracted features from point clouds. We do not use the GGD, AGGD, Gamma parameters of color features in this experiment version for simplification.
We implement and collect several common PCQA metrics, which can be accessed here.
If you find our work useful, please cite our work as:
@ARTICLE{zhang2022no,
author={Zhang, Zicheng and Sun, Wei and Min, Xiongkuo and Wang, Tao and Lu, Wei and Zhai, Guangtao},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/TCSVT.2022.3186894}}
If you have further questions, please email us through [email protected].