Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front‐Facing Views

Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured images. This success has led to many variants of the techniques, each evaluated on a set of test views typically using im...

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Vydané v:Computer graphics forum Ročník 43; číslo 2
Hlavní autori: Liang, H., Wu, T., Hanji, P., Banterle, F., Gao, H., Mantiuk, R., Öztireli, C.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.05.2024
Predmet:
CCS Concepts
Computing methodologies → Image‐based rendering
Datasets
Evaluation
Image and video acquisition
Image quality
Perception
Quality assessment
Synthesis
Video
ISSN:0167-7055, 1467-8659
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Shrnutí:Neural view synthesis (NVS) is one of the most successful techniques for synthesizing free viewpoint videos, capable of achieving high fidelity from only a sparse set of captured images. This success has led to many variants of the techniques, each evaluated on a set of test views typically using image quality metrics such as PSNR, SSIM, or LPIPS. There has been a lack of research on how NVS methods perform with respect to perceived video quality. We present the first study on perceptual evaluation of NVS and NeRF variants. For this study, we collected two datasets of scenes captured in a controlled lab environment as well as in‐the‐wild. In contrast to existing datasets, these scenes come with reference video sequences, allowing us to test for temporal artifacts and subtle distortions that are easily overlooked when viewing only static images. We measured the quality of videos synthesized by several NVS methods in a well‐controlled perceptual quality assessment experiment as well as with many existing state‐of‐the‐art image/video quality metrics. We present a detailed analysis of the results and recommendations for dataset and metric selection for NVS evaluation.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.15036