NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods-Reference-Cited by-同舟云学术

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NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods

Published:2024-05 Issue:5 Volume:30 Page:2129-2139
ISSN:1077-2626
Container-title:IEEE Transactions on Visualization and Computer Graphics
language:
Short-container-title:IEEE Trans. Visual. Comput. Graphics

Author:

Qu Qiang¹^ORCID,Liang Hanxue²,Chen Xiaoming³^ORCID,Chung Yuk Ying¹^ORCID,Shen Yiran⁴^ORCID

Affiliation:

1. School of Computer Science, University of Sydney, Australia

2. Department of Computer Science and Technology, University of Cambridge, United Kingdom

3. School of Computer and Artificial Intelligence, Beijing Technology and Business University, China

4. School of Software, Shandong University, China

Funder

Beijing Natural Science Foundation

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/2945/10505866/10459060.pdf?arnumber=10459060

Reference65 articles.

1. An AR-Guided System for Fast Image-Based Modeling of Indoor Scenes

2. Image quality assessment based on DCT subband similarity

3. Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

4. Matterport3D: Learning from RGB-D Data in Indoor Environments

5. TensoRF: Tensorial Radiance Fields

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. X-NR: Towards An Extended Reality-Driven Human Evaluation Framework for Neural-Rendering;Lecture Notes in Computer Science;2024

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