Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network

Salima Bourbia, Ayoub Karine, Aladine Chetouani, Mohammed El Hassouni, Mohammed El Hassouni

2022

Abstract

In recent years, 3D point clouds have experienced rapid growth in various fields of computer vision, increasing the demand for efficient approaches to automatically assess the quality of 3D point clouds. In this paper, we propose a blind point cloud quality assessment method based on deep learning, that takes an input point cloud object and predicts its quality score. The proposed approach starts with projecting each 3D point cloud into rendering views (2D images). It then feeds these views to a deep convolutional neural network (CNN) to obtain the perceptual quality scores. In order to predict accurately the quality score, we use transfer learning to exploit the high potential of VGG-16, which is a classification model trained on ImageNet database. We evaluate the performance of our model on two benchmark databases: ICIP2020 and SJTU. Based on the results analysis, our model shows a strong correlation between the predicted and the subjective quality scores, showing promising results, and outperforming the state-of-the-art point cloud quality assessment models.

Paper Citation

in Harvard Style

Bourbia S., Karine A., Chetouani A. and El Hassouni M. (2022). Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 518-525. DOI: 10.5220/0010872700003124

in Bibtex Style

@conference{visapp22,
author={Salima Bourbia and Ayoub Karine and Aladine Chetouani and Mohammed El Hassouni},
title={Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={518-525},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010872700003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network
SN - 978-989-758-555-5
AU - Bourbia S.
AU - Karine A.
AU - Chetouani A.
AU - El Hassouni M.
PY - 2022
SP - 518
EP - 525
DO - 10.5220/0010872700003124
PB - SciTePress