Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
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.
Description
CCS Concepts: Computing methodologies -> Image-based rendering; Image and video acquisition; Perception
@article{10.1111:cgf.15036,
journal = {Computer Graphics Forum},
title = {{Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Views}},
author = {Liang, Hanxue and Wu, Tianhao and Hanji, Param and Banterle, Francesco and Gao, Hongyun and Mantiuk, Rafal and Öztireli, Cengiz},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15036}
}