Summary of Nerf-nqa: No-reference Quality Assessment For Scenes Generated by Nerf and Neural View Synthesis Methods, By Qiang Qu et al.
NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods
by Qiang Qu, Hanxue Liang, Xiaoming Chen, Yuk Ying Chung, Yiran Shen
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes NeRF-NQA, a novel no-reference quality assessment method for densely-observed scenes synthesized by Neural View Synthesis (NVS) and NeRF variants. The existing quality assessment methods, such as PSNR, SSIM, and LPIPS, are inadequate for evaluating the perceptual quality of NVS-synthesized scenes, including spatial and angular aspects. To address this challenge, NeRF-NQA employs a joint quality assessment strategy that integrates both viewwise and pointwise approaches to evaluate the quality of NVS-generated scenes. The viewwise approach assesses the spatial quality of each individual synthesized view and the overall inter-views consistency, while the pointwise approach focuses on the angular qualities of scene surface points and their compound inter-point quality. NeRF-NQA is evaluated extensively against 23 mainstream visual quality assessment methods from fields of image, video, and light-field assessment. The results demonstrate that NeRF-NQA outperforms existing assessment methods significantly and shows substantial superiority in assessing NVS-synthesized scenes without references. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to measure how good videos are when they’re made using special computer vision techniques called Neural View Synthesis (NVS) and NeRF. Right now, we don’t have a good way to compare these kinds of videos because the old methods weren’t designed for them. This makes it hard to tell if the videos are good or not. The new method, called NeRF-NQA, is special because it can measure how good these videos are without needing to know what they’re supposed to look like. It does this by looking at each frame of the video separately and also at the way different parts of the scene fit together. This helps us understand if the video looks realistic or not. The new method was tested against 23 other ways of measuring how good videos are, and it did much better than all of them! This is important because it will help make sure that NVS-synthesized scenes look as good as possible. |
