• VQA_PC
• • our contributions
  • file structure
  • datasets
  • environment
  • command
  • performance
  • citation
  • contributors

VQA_PC

key words:point cloud quality assessment, moving camera videos, no-reference, video quality assessment

VQA_PC is a kind of point cloud quality assessment method of no reference. It has excellent performances over SJTU, WPC and LSPCQA-I datasets, benefitting from projecting the point cloud files to videos and assessing the quality via VQA methods.

our contributions

1.transplant from pytorch to tensorflow and mindspore.
2.benchmark test on mindspore, tensorflow and pytorch, and compare the performance.

file structure

root
└── mindspore: mindspore code, models included
└── tensorflow: tensorflow code, models included
└── pytorch source code: please go to: https://github.com/zzc-1998/VQA_PC
└── Treating Point Cloud as Moving Camera Videos A No-Reference Quality Assessment Metric.pdf: origional paper

datasets

1. WPC
2. LS-PCQA
3. SJTU

environment

1. mindspore

• ubuntu 16.04
• cuda V11.1.105
• python 3.7.11
• mindspore 2.0.0.dev20230109, installation reference: https://www.mindspore.cn/install

2. tensorflow

• ubuntu 16.04
• cuda V10.1.243
• python 3.7.6
• tensorflow-gpu 2.3.1

command

• mindspore:

cd mindspore

• tensorflow:

cd TensorFlow

training:

python ./train/train_SJTU.py

test:

python ./test/test.py

performance

• Benchmark test on mindspore, tensorflow and pytorch below. From the result in all, we can see that the performances of versions between pytorch, mindspore and tensorflow is similar. For SJTU dataset, mindspore version has the best performance, while for WPC dataset, tensorflow version gets the best score.
• For running speed and gpu memory occupancy, mindspore takes the least time, while pytorch ranks the second with a little more time and less gpu memory. Tensorflow takes the longest time and the most volume of gpu memory.

Table 1. Test on SJTU dataset

Source	SRCC	PLCC	KRCC	RMSE
Paper	0.8509	0.8635	0.6585	1.1334
PyTorch	0.9125	0.9341	0.7634	0.8364
MindSpore	0.9136	0.9346	0.7619	0.835
TensorFlow	0.8774	0.9002	0.7048	1.0253

Table 2. Test on WPC dataset

Source	SRCC	PLCC	KRCC	RMSE
Paper	0.7968	0.7976	0.6115	13.6219
PyTorch	0.8173	0.8226	0.631	12.9618
MindSpore	0.8069	0.8085	0.6188	13.4193
TensorFlow	0.8296	0.8313	0.6457	12.6353

Table 3. test time and gpu memory on TESLA T4 gpu

framework	test time(s)	GPU memory(MB)
Paper	--	--
PyTorch	29.937	1358
MindSpore	26.405	3110
TensorFlow	44.887	4732

citation

@article{zhang2022treating,
  title={Treating Point Cloud as Moving Camera Videos: A No-Reference Quality Assessment Metric},
  author={Zhang, Zicheng and Sun, Wei and Zhu Yucheng, Min, Xiongkuo and Wu Wei, and Chen Ying, and Zhai, Guangtao},
  journal={arXiv preprint arXiv:2208.14085},
  year={2022}
}

contributors

name: Ye Hua
email: yeh@pcl.ac.cn