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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Bibliographic Details
Published in:Computer graphics forum Vol. 43; no. 2
Main Authors: Liang, H., Wu, T., Hanji, P., Banterle, F., Gao, H., Mantiuk, R., Öztireli, C.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.05.2024
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.15036