Neural radiance fields (NeRF) are a groundbreaking computer vision technology that enables the generation of high-quality, immersive visual content from multiple viewpoints. This capability has significant advantages for applications such as virtual/augmented reality, 3D modelling, and content creation for the film and entertainment industry. However, the evaluation of NeRF methods poses several challenges, including a lack of comprehensive datasets, reliable assessment methodologies, and objective quality metrics. This paper addresses the problem of NeRF view synthesis (NVS) quality assessment thoroughly, by conducting a rigorous subjective quality assessment test that considers several scene classes and recently proposed NVS methods. Additionally, the performance of a wide range of state-of-the-art conventional and learning-based full-reference 2D image and video quality assessment metrics is evaluated against the subjective scores of the subjective study. This study found that errors in camera pose estimation can result in spatial misalignments between synthesized and reference images, which need to be corrected before applying an objective quality metric. The experimental results are analyzed in depth, providing a comparative evaluation of several NVS methods and objective quality metrics, across different classes of visual scenes, including real and synthetic content for front-face and 360-degree camera trajectories.

翻译：神经辐射场（NeRF）是一项突破性的计算机视觉技术，能够从多个视角生成高质量、沉浸式的视觉内容。该能力在虚拟/增强现实、三维建模以及电影与娱乐行业的内容创作等应用中具有显著优势。然而，NeRF方法的评估面临若干挑战，包括缺乏全面的数据集、可靠的评估方法以及客观的质量指标。本文通过开展严谨的主观质量评估实验，考虑了多种场景类别及近期提出的NeRF视图合成方法，从而深入探讨了NeRF视图合成的质量评估问题。此外，本研究还基于主观实验的评分，对一系列前沿的传统及基于学习的全参考二维图像与视频质量评估指标的性能进行了评估。研究发现，相机姿态估计的误差可能导致合成图像与参考图像之间的空间错位，这在应用客观质量指标前需予以校正。实验结果表明了深入分析，对不同类别的视觉场景（包括真实与合成内容的前向及360度相机轨迹）下的多种NeRF视图合成方法与客观质量指标进行了比较评估。