No Reference 3D Mesh Quality Assessment Using Deep Convolutional Features
Author:
Affiliation:
1. Ibn Tofail University,SETIME Laboratory, Information Processing and A.I Team, Faculty of Sciences,Kénitra,Morocco
2. Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC,Caen,France,14000
Funder
PHC TOUBKAL TBK/22/142-CAMPUS
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10278502/10278578/10278663.pdf?arnumber=10278663
Reference19 articles.
1. Understanding the difficulty of training deep feedforward neural networks;glorot;AISTATS,2010
2. No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling
3. Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features
4. 3D Blind Mesh Quality Assessment Index
5. Very deep convolutional networks for large-scale image recognition;simonyan;ICLRE,2015
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. No Reference 3D Mesh Quality Assessment Learned From Quality Scores on 2D Projections;IEEE Access;2024
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