No-Reference 3D Point Cloud Quality Assessment Using Multi-View Projection and Deep Convolutional Neural Network
Author:
Affiliation:
1. L@bISEN, Vision-AD, ISEN Yncréa Ouest, Carquefou, France
2. Laboratoire PRISME, Université d’Orléans, Orléans, France
3. FLSH, FSR, Mohammed V University in Rabat, Rabat, Morocco
Funder
National Center for Scientific and Technical Research (CNRST), Morocco
French Institute (FI), Morocco
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10049404.pdf?arnumber=10049404
Reference58 articles.
1. Subjective and objective quality evaluation of 3D point cloud denoising algorithms
2. Deep Learning-Based Quality Assessment Of 3d Point Clouds Without Reference
3. A Review of Existing Evaluation Methods for Point Clouds Quality
4. Image Quality Assessment Without Reference By Mixing Deep Learning-Based Features
5. A Multi-Task Convolutional Neural Network For Blind Stereoscopic Image Quality Assessment Using Naturalness Analysis
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1. No-reference point cloud quality assessment based on multi-projection and hierarchical pyramid network;Signal, Image and Video Processing;2024-07-28
2. Plain-PCQA: No-Reference Point Cloud Quality Assessment by Analysis of Plain Visual and Geometrical Components;IEEE Transactions on Circuits and Systems for Video Technology;2024-07
3. Blind Quality Assessment of Dense 3D Point Clouds with Structure Guided Resampling;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-06-13
4. Point Cloud Quality Assessment Using a One-Dimensional Model Based on the Convolutional Neural Network;Journal of Imaging;2024-05-27
5. Balancing Representation Abstractions and Local Details Preservation for 3d Point Cloud Quality Assessment;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
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